Hardness Assurance and Photonics Challenges for Space Systems

2004 IEEE NSREC Nuclear and Space Radiation Effects Conference Short Course Notebook Hardness Assurance and Photonics C...

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2004 IEEE NSREC Nuclear and Space Radiation Effects Conference Short Course Notebook

Hardness Assurance and Photonics Challenges for Space Systems

July 19, 2004 Sponsored by: IEEE/NPSS Radiation Effects Committee Supported by: Defense Threat Reduction Agency Sandia National Laboratories Air Force Research Laboratory NASA Electronic Parts and Packaging Program Jet Propulsion Laboratory Approved for public release; distribution is unlimited

2004 IEEE Nuclear and Space Radiation Effects Conference

Short Course Notebook

Hardness Assurance and Photonics Challenges for Space Systems

July 19, 2004 Atlanta, Georgia

Copyright© by the Institute of Electrical and Electronics Engineers, Inc. All rights reserved. Instructors are permitted to photocopy isolated articles for noncommercial classroom use without fee. For all other copying, reprint, or replication permission, write to Copyrights and Permissions Department, IEEE Publishing Services, 445 Hoes Lane, Piscataway, NJ 08855-1331.

CONTENTS Introduction Biographies Section

Page

I.

Hardness Assurance for Space Systems……………………….….I-1 Gary K. Lum, Lockheed Martin Space Systems

II.

Microelectronic Piece Part Radiation Hardness Assurance for Space Systems………………………………………………….II-1 Ronald L. Pease, RLP Research

III.

Optical Sources, Fibers, and Photonic Subsystems…………….III-1 Allan H. Johnston, Jet Propulsion Laboratory

IV.

Optical Detectors and Imaging Arrays………………………….IV-1 Terrence S. Lomheim, The Aerospace Corporation

V.

Solar Cell Technologies, Modeling, and Testing………………...V-1 Robert J. Walters, Naval Research Laboratory

INTRODUCTION This Short Course Notebook contains the material prepared by the instructors, and their coauthors, for the 2004 IEEE Nuclear and Space Radiation Effects Conference (NSREC) Short Course. The course was held on July 19, 2004, in Atlanta, Georgia, and was the 25th time the NSREC provided such a course. The present notebook complements the oral presentations made by the instructors in Atlanta, and serves as a valuable technical reference and archival record for the radiation effects community. The 2004 Short Course, “Hardness Assurance and Photonics Challenges for Space Systems”, addresses two topics of primary importance for present and future space systems. Two of the course instructors discuss hardness assurance for space systems, and three instructors address radiation effects on photonics in space. Hardness assurance approaches and methodologies are implemented to assure that space systems not only survive the natural space radiation environment but also perform within specifications during the entire mission. Assuring system radiation hardness involves many technical considerations. Effective hardness assurance methods are an integral part of designing space systems to satisfy mission performance goals. The Short Course includes lectures on hardness assurance at the system level and at the electronic component level. Nearly all present and envisioned space systems include photonic devices and subsystems. Thus, it is necessary to understand space radiation effects on such components and to develop effective radiation hardening approaches as needed. Examples of photonics commonly employed are solar cells and arrays, discrete optical detectors, optical emitters, optical fibers, visible and infrared imaging arrays, and passive optical elements. The Short Course addresses radiation effects on most of those photonic components. Photonic subsystems are also considered. This Short Course Notebook is divided into five sections. A brief summary of each of the lectures presented at the 2004 Short Course follows. Biographies for the instructors are given in the next section. In Section I, “Hardness Assurance for Space Systems”, Gary Lum, Lockheed Martin Space Systems, presents a comprehensive review of the approaches and methodologies used to assure the radiation hardness of space systems. He discusses the key phases of a hardness program for space systems and describes the hardness assurance management plan. He discusses the space radiation environment and gives an overview of the key effects of that environment on electronics and on systems. System hardening approaches are addressed, and radiation testing considerations are described. Dr. Lum discusses hardness assurance implementation during the production and deployment phases of a system. He also describes emerging issues and challenges and their potential solutions for hardened space systems. In Section II, “Microelectronic Piece Part Radiation Hardness Assurance for Space Systems”, Ron Pease, RLP Research, describes hardness assurance as applied at the electronic piece-

part level. He defines key concepts and terminology and gives an overview of traditional parts hardness assurance methods and their modifications. Key hardness assurance documentation for users is identified. Parts qualification and radiation lot acceptance testing are addressed as well as exceptions and limitations in practice. Parts hardness assurance challenges for space systems are discussed and recommendations are given. In Section III, “Optical Sources, Fibers, and Photonic Subsystems”, Allan Johnston, Jet Propulsion Laboratory, addresses basic and applied aspects of space radiation effects on photonics. He discusses the physics of how photonic devices work. Radiation environments and effects of interest for photonics are summarized. He includes radiation effects on lightemitting and laser diodes and annealing behavior. Absorption effects in optical fibers are addressed as well as fiber comparisons. He also covers radiation effects on photonic subsystems for space applications, including digital optocouplers and optical receivers. In Section IV, “Optical Detectors and Imaging Arrays”, Terry Lomheim, Aerospace Corporation, discusses radiation effects on visible and infrared detectors and arrays. He describes today’s leading technologies and discusses key effects of the space radiation environment. He includes displacement damage effects, ionizing radiation effects, and radiation-induced noise in arrays, plus gives overviews of hardening approaches and technology trends. Dr. Lomheim also addresses radiation effects on readout integrated circuits. In Section V, “Solar Cell Technologies, Modeling, and Testing”, Rob Walters, Naval Research Laboratory, addresses basic and applied aspects of radiation effects on solar cells. He discusses solar-cell device physics and the mechanisms of radiation-induced degradation, and describes cell technologies for present and future applications. Dr. Walters describes and compares the leading modeling techniques used to predict solar-cell degradation in space. Simulation testing approaches are discussed, and on-orbit performance predictions are addressed. On behalf of the 2004 NSREC Committee and the greater radiation effects community, I sincerely thank the five Short Course instructors for their sustained efforts in preparing and presenting the course material. They contributed a great deal of their time and expertise to ensure the success of the 2004 Short Course. Their efforts, and the efforts of their co-authors, are greatly appreciated and will continue to be of significant benefit to the community through the publication of this notebook. The comprehensive material contained herein is expected to serve as a valuable resource for radiation effects engineers and scientists and for space system designers. I also thank Lew Cohn of DTRA for his efforts in reviewing the Short Course and ensuring that the Short Course Notebooks were printed on schedule. In addition, Dale Platteter of NAVSEA Crane deserves our thanks for making all NSREC Short Courses available on an archival disk. Joe Srour Short Course Chairman 2004 IEEE NSREC

BIOGRAPHIES Joseph R. Srour is employed in a senior engineering position at the Aerospace Corporation. Prior to joining Aerospace, he worked for TRW where he managed the Radiation and Survivability Engineering organization. Before TRW, he worked for the Northrop Corporation where he held various technical and managerial positions. Much of his technical work has focused on nuclear and space radiation effects on materials, devices, circuits, and systems. He has also made technical contributions in the areas of optical detectors, semiconductor device physics, and microelectronics. Joe is a Fellow of the IEEE and is a member of Sigma Xi and Tau Beta Pi. He is the author of one technical book and 49 articles published in refereed technical journals. He received the Outstanding Paper Award six times for papers presented at the IEEE Nuclear and Space Radiation Effects Conference, and received the Meritorious Paper Award twice for papers presented at that same conference. He holds two U.S. patents. Joe received bachelors, masters, and Ph.D. degrees in electrical engineering from the Catholic University of America, Washington, DC. Gary K. Lum received a B.A. in physics at the University of California, Berkeley and M.S and Ph.D. in physics at the University of Oregon. He was a graduate student under Dr. C. Wiegand and Prof. E. Segré (Nobel Laureate) at Lawrence Berkeley National Laboratory. After joining Lockheed Missiles System Division in 1980, Gary headed the radiation effects analysis group. He joined Intel Corporation in 1984 to work as a device physicist. In 1986, he returned to Lockheed where his areas of study included IC fabrication processes, modeling of CMOS and bipolar technologies, and radiation effects in semiconductor devices. In 1988, he received the AIAA award for Best Design Engineer. Gary has published over 20 technical papers. He has served in various technical and management positions for the IEEE Nuclear and Space Radiation Effects Conference and the Hardened Electronics and Radiation Technology Conference, and serves as a technical paper reviewer for both conferences. At Lockheed Martin, he provides recommendations and technical guidance to designers, program managers and customers. Presently, he is an Engineering Fellow supporting space programs by providing training and technical guidance in parts selection and in the design of hardened systems. He lectures at Stanford University and also conducts studies to understand radiation effects on electronics and to mitigate those effects in satellite and missile applications. Ronald L. Pease received the B. S. in Physics from Indiana University in 1965 and pursued graduate studies in Physics at the University of Washington in 1966. He has been active in radiation effects characterization, modeling, analysis and hardness assurance for 38 years, having worked at NAVSEA Crane (1966-1977), BDM (1977-1979), and Mission Research Corp. (1979-1993). Mr. Ron Pease is the president and sole employee of RLP Research, which was formed in 1993. He is a technical advisor and senior scientist on several DoD contracts that address radiation response and hardness assurance, the most recent being in the areas of Enhanced Low Dose Rate Sensitivity and Single Event Transients in bipolar linear circuits. Mr. Pease is very active in the IEEE NPSS having held every technical position for the Nuclear and Space Radiation Effects Conference including serving as Conference Chairman in 2000. He has served as a Short Course Instructor and Short Course Chairman

for the NSREC, as well as a short course instructor at the Nuclear Science Symposium, the Commercialization of Military and Space Electronics and Vanderbilt University. He has over 90 technical publications in the area of radiation effects in electronics and has received several NSREC Outstanding and Meritorious Paper Awards, the most recent being the Outstanding Conference Paper Awards in 2002 and 2003. Allan H. Johnston received B.S. and M.S. degrees in physics from the University of Washington. He began his career at Boeing Aerospace Corporation, performing research studies on radiation effects in microelectronics and optoelectronics. He joined the Jet Propulsion Laboratory in 1992 where he supervises applied research on radiation effects in microelectronics for space applications. His technical interests include ionization and singleevent upset effects in semiconductor devices, with emphasis on low-dose-rate effects, latchup, and space applications of advanced technologies. Related interests include determining how new device technologies and device scaling will influence their radiation performance and reliability in space as well as radiation effects on optoelectronics. He has authored more than 80 papers in refereed journals. He received the Outstanding Paper Award at the IEEE Nuclear and Space Radiation Effects Conference (NSREC) in 1999, Meritorious Paper Awards in 1995 and 1996, and the Distinguished Poster Paper Award in 1987. He has been active in the IEEE NSREC, serving as Short Course Instructor for four conferences, Local Arrangements Chairman, Short Course Chairman, and Awards Chairman. He was Technical Program Chairman for the 1997 NSREC and General Chairman for the 2003 NSREC. He is a Fellow of the IEEE. Terrence S. Lomheim is a Distinguished Engineer in the Sensor Systems Subdivision, The Aerospace Corporation, El Segundo, California, where he has worked since 1978. He received a Ph.D. in Physics from the University of Southern California in 1978. He has performed detailed experimental evaluation of the electro-optical properties, imaging performance capabilities, and radiation effects sensitivities of visible scanning and staring CCD and CMOS devices and hybrid infrared focal planes for a variety of DoD and Civil Programs. He has also been involved in the design, performance assessment, modeling and diagnostics of point-source detection, broadband, multispectral, and hyperspectral imaging electro-optical sensor systems in the visible through longwave spectral regions. Dr. Lomheim has authored and co-authored 38 publications in the areas of applied optics, focal plane technology, and imaging sensor performance and has been a part-time instructor in the physics department at the California State University, Dominquez Hills since 1981. He is a Fellow of The International Society for Optical Engineering (SPIE) and is a member of the Optical Society of American and the American Physical Society. Robert J. Walters received his Ph.D. in Applied Physics from the University of Maryland Baltimore County in 1994. He has worked at the US Naval Research Laboratory since 1991. His area of expertise is in radiation effects in semiconductor materials and devices, and his primary area of focus is radiation effects in solar cells for space applications. His research group has produced a new technique for modeling the effect of irradiation on semiconductor devices, which has gained international acceptance. His group has also produced groundbreaking work on new space solar cell technologies, and they are currently building a solar cell space experiment to be flown on the International Space Station. In addition to

space solar cell research, Dr. Walters is also directing a project to develop advanced photovoltaic devices for micro-power systems. The end product will be a self-powered optical data link for use in a distributed autonomous sensor system. Dr. Walters lives in Alexandria, VA with his beautiful wife, PJ, and two wonderful daughters, Sarah who is 13, and Molly who is 8.

2004 IEEE NSREC Short Course

Section I

Hardness Assurance for Space Systems

Gary K. Lum Lockheed Martin Space Systems

Hardness Assurance for Space Systems Gary Lum, PhD., Lockheed Martin Space Systems Company, Sunnyvale, CA Marion Rose, Titan Corp., San Diego, CA Table of Contents 1.0 Introduction 1.1 Perspective 2.0 System Hardness Program 2.1 Hardness Assurance Management Plan 3.0 Requirements Generation 4.0 Program Initiation Phase 4.1 Space Radiation Environments 4.1.1 Composition of the Space Environment 4.1.2 Trapped Protons Dose 4.1.3 Trapped Electron Dose 4.1.4 Solar Cycles 4.1.5 Shielding Against Solar & Galactic Environments 4.1.6 Environment Highlights 4.2 Radiation Requirements 4.3 System Radiation Effects 4.4 System Strategies for Reducing Radiation Effects 4.5 System Hardening Approaches 4.6 Space Environment Modeling Tools 4.7 Shielding Example 4.8 Upset Rate / Risk Example 5.0 Hardening Against Protons 6.0 Key Integrated Circuit / Semiconductor Degraded Parameters & Effects 7.0 Total Ionizing Dose Effects 7.1 MOS Devices 7.2 Bipolar Devices 8.0 Proton Displacement Damage 8.1 Gain Degradation 9.0 Displacement Damage in Other Technologies and System Solutions 10.0 Comparison of Commercial versus Hardened Technologies 11.0 Radiation Test Facilities 12.0 Production / Deployment Phase 12.1 System Hardness Assurance 12.2 Maintenance / Surveillance Program 12.3 Types of Hardness Degradation 12.4 Implementing Hardness Assurance 12.5 Classifying Parts in a Space Program 12.6 Parts Screening Control 12.7 Document Flow Through HA 12.8 Manufacturing Support 13.0 Emerging Issues 13.1 Future Radiation Trends in Electronic Technologies 13.2 Single Event Upset, Transients, Latch-up 13.3 Extremely Low Dose Rate Sensitivity 13.4 SOI Future Technology 14.0 Conclusion 15.0 Acknowledgements 16.0 References

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1.0 Introduction Within a space program system hardness assurance (HA) plays a very important function. Although HA plays a major role at the parts level, HA at higher assembly levels, at the board, subassembly, package and system must be addressed. When radiation requirements are imposed on a system, an engineer must design the board or package to meet those requirements. Hardening designs must be traded against cost, shielding analysis performed against weight, parts tested for radiation hardness and then results documented. However, the job in the survivability community is only partially complete. Transitioning a developed product to manufacturing is the next step and that is not a simple task. One has to determine how to transition the design and work that has been accomplished to the manufacturing floor. Along the way if the circuit design, procurement of components or fabrication of a board were not well controlled or the translation was poorly interpreted by the assembler, margins achieved in the original design may not be ever manufactured. Hardness assurance provide the guidelines or rules that are established to assure that the final product will be what was designed and still maintain its original radiation design margins. Within a survivability program there has to be a hardness assurance plan. An overview will be presented of what this plan covers. Because HA involves hardening a design to a set of space environments, we will briefly review the types of radiation environments that various space systems have to encounter and understand the kinds of effects that can affect a system and what hardening techniques a designer can use. Radiation codes for analyses and radiation test facilities for testing will be described. An important section of system design is also the selection of parts. We will show how parts are selected and based on their hardness, how they must be maintained and monitored during the life of a program. Finally, some future issues will be presented. In light of the fact that we are designing systems better, faster and cheaper, we need to continue to find alternative ways of making sure that we can maintain the hardness of our systems. These are challenges that we have to face in the future. The first twelve references are for general reading on the subject of radiation [1-12] followed by references on the discussed topics. References [141-195] are military specifications, handbooks, test methods, and standards. 1.1 Perspective As one can imagine, building systems according to Fig. 1 to fly in space successfully is very expensive. Imagine the amount of time and dollars that goes into a design, assembly and qualification of systems, such as the Hubble Space Telescope, the Space Transportation System (Space Shuttle), the International Space Station or a launch system. Tens to hundreds of millions of dollars are spent and 5 to 15 years or more are spent developing, assembling, testing, launching and maintaining these space systems. If a system were to be lost during launch, the payload lose control during flight, the payload lose communication due to a misinterpretation of information due to poor documentation by the engineer to the assembler or manufacturer or poor quality control of the parts that go into the system, the prime contractor could lose incentives and profit from the contract. Setbacks could occur.

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If the systems were similar to the disasters of the Columbia or Challenger space shuttle incidents, setbacks in schedules would occur. If the problem were similar to the optical flaw in the Hubble Space Telescope, valuable scientific data would be lost because time had to be spent developing a solution to the problem. Until the cause is identified and a solution is found, large costs are incurred. • Development of a system is expensive • Challenges - Advanced electronics, Plastic Encapsulated Microelectronics (PEMs), COTS • Failure of a system in space results in loss of data, profit, and large program setbacks • Loss in 1998 was $1.6B during the advocation of faster, better and cheaper systems • Loss totaled $597M in 1999

International Space Station

Space Shuttle Hubble Space Telescope

• System Hardness Assurance is the methodology/discipline of assuring that the production processes do not adversely reduce the margins designed into our Space Systems during development. Figure 1. Perspective - Importance of Radiation System Hardness Assurance.

Radiation hardness assurance is defined as a piece of reliability for which the radiation experts are responsible. Their responsibility is to assure that the system they design has not lost its margins when going into manufacturing. This degradation may be the result of not monitoring a process change in a part or the result of not providing adequate design margins to account for radiation degradation. This implies that the role of radiation experts in the design and manufacture of a space system is to demonstrate that the methodology or discipline inherent in the parts used, the boards and packages one tested, analyzed and assembled or the system they integrate will still maintain the design margins during manufacturing and throughout the entire life of a program. The methodology or discipline will be described in this short course. In 1998 the space industry lost $1.6B, when there was a strong avocation to develop space systems faster, better and cheaper. The Mexican satellite, Solidaridad 1 failed on orbit. The loss per vehicle was around $50M or higher which is too many dollars. In 1999 about $611M was lost in the satellite industry due to various problems. A number of these problems occurred during launch. Whether or not this was the complete list of failures during that year, changes occurred to minimize many of the failures that were observed during 1998, the previous year. The message here is that even though failures may not be all associated with radiation hardness assurance issues, these could easily apply to us if we do not adhere to strict hardness assurance disciplines [16].

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2.0 System Hardness Program A system hardness program has two important phases as shown in Fig. 2. They are (a) Design and Development and (b) Manufacturing and Production. During the Design and Development phase, a program contract is defined, describing the type of space program, its mission objectives and the contractual requirements imposed by the customer on the contractor. Along with the program requirements, a system survivability program plan is defined. This may contain the radiation specifications that the system must meet, the transported radiation requirements at the parts and a system compliance document describing what types of radiation testing or analysis must be performed in order to satisfy compliance at the package or subassembly levels. At the system level compliance may not be a requirement because testing may not be achievable or may be cost prohibitive. Generally, testing or analyses performed at the lower levels, such as parts, boards and packages are applied to assess the hardness of the system. Manufacturing / Design / Development Phase Info / Production Phase Technology Mission Objectives

System Survivability Program

Transfer

Radiation Environments (free field) Hardness Compliance Plan Defines test / analysis approaches for compliance of packages, system

Hardness Assurance

Hardness Surveillance

Radiation Requirements (transported)

Hardness Characterization and Qualification

Hardened Design

Figure 2. System Hardness Program.

During the Design and Development phase various tradeoff concepts are performed. These concepts are important for optimizing cost and performance. The selection of candidate process and design technologies, such as CMOS versus bipolar or FPGAs (Field Programmable Gate Array) versus ASICs (Application Specific Integrated Circuit), are evaluated. Sample products from various suppliers are radiation tested. Cost tradeoff is made between procuring hardened versus radiation tolerant parts. Schedules are laid out to determine when parts should arrive versus when boards and packages can be assembled for evaluation. An approach may be to invest the time to design by applying a foundry’s layout rules to harden a design [132], called “Hardened by Design,” versus procuring radiation hardened parts. Radiation testing and documentation must be performed to qualify such a part. Parts engineering must determine eventually how to establish monitoring controls with the manufacturer in order for parts to be procured for the program. Going into Manufacturing and Production the circuit designs are fairly established. Parts procured during the development phase may have consisted of a small sample size and I-4

originated from a particular lot. Now if one had to procure larger quantities for manufacturing or production, it is possible that the parts to be ordered going into manufacturing may not be the same pedigree as those tested during development. Therefore lot sampling testing or life of buys from the same lot that the smaller samples originated must be considered. Based on lots tested during manufacturing, statistical analysis must be performed to determine how margins have changed as compared to those measured during development. Surveillance testing may also be required for parts with small design margins or critical to the system performance. More will be described below about what hardness assurance practices are needed during manufacturing. 2.1 Hardness Assurance Management Plan A typical Hardness Assurance (HA) management plan is described below. In Fig. 3 HA has a role in every facet of a program lifecycle as described above: (a) Program Initiation sometimes also referred to as Demonstration / validation (Demo/val), (b) Full Scale Development (FSD), and (c) Production [125, 126]. The activities that occur during the Program Initiation include the establishment of the radiation requirements, cost tradeoffs, evaluation of candidate process technologies and the establishment of hardening techniques and design concepts. Typically, this period spans 2-5 years. Program Life Cycle Program Initiation Phase (2-5 yrs)

Full Scale Development Phase (4-7 yrs)

Production Phase (5-10 yrs)

Engineering Radiation Requirements Cost/design trade offs Hardening techniques Implement techniques Board design Package design Parts procurement/qualification Testing & Evaluation - Radiation testing and analyses Test facility usage Rad code usage - Parts boards package compliance Hardness Assurance Plan • Procurement procedures • Lot sampling • Hardening procedures • Qualification documents • Lot sampling testing

• • • •

Configuration Mgt. Production Quality Assurance Program Control

Figure 3. Hardness Assurance Management Plan.

During FSD, designers focus on their selected architecture design and begin their concept demonstrations. Typically, this period spans about 4-7 years. Non-hardened form-fit and functional parts may be used in certain small scale circuits to proof out the design. FPGAs may be used to rapidly design a complex device for electrical performance evaluation. The parts procurement process starts. During the latter half of the FSD program, some of the radiation hardened parts are procured. Designs on FPGAs are transferred to a hardened process. Typically, the integrated circuit fabrication, radiation evaluation and board assembly will take 2-3 years. This assumes a minimum of two fabrication runs. When prototype hardened parts arrive during the middle of FSD, new boards are built. Board test I-5

plans are created. Once several boards or packages are completed with the first prototype radiation hardened parts, engineering will evaluate the electrical performance and radiation hardness. In the final years prior to production, package or subassembly testing may be conducted. Here refinements are made in the designs. After compliance has been completed, designs are pretty frozen, results are documented and guidelines are being established for the manufacturing of the packages or systems. The results of the analyses and testing are documented and guidelines are established as to how components will be procured and qualified. HA needs to manage the guidelines for manufacturing of the final system. HA is so important here. HA must determine that the radiation design margins (RDMs) have not degraded or changed from the development period until the start of manufacturing. A flight experiment may also be part of the Development phase for a satellite or launcher program to demonstrate design concepts suitable for space application. Generally, this flight experiment is used as a risk mitigation approach. If considered, it should be as early as possible in the program in order to minimize cost in design changes. Certainly, the parts selected must be hardened to the space environment. During the Production phase, HA has inputs to provide in establishing the procedures for lot procurement, lot sampling and surveillance testing. Hardness procedures and guidelines have to be established for transferring to manufacturing. Any changes or deviations in the manufacturing cycle must be carefully monitored and reviewed closely through a control board process [ 127, 129, 130, 131]. This control board process is what we call configuration management or managing the configuration. Changes must require many organizations of various responsibilities to now approve the modification. They may include disciplines from reliability, safety, survivability, procurement, subcontracts, system engineering, packaging, manufacturing, and support equipment. 3.0 Requirements Generation Radiation requirements for satellite programs are created in the following way as shown in Fig. 4. A mission objective is defined in a document that may be contractual, that is, if a problem occurs during the development phase, the prime contractor may be obligated to fix it. This may be a scientific or telecommunication mission. From the mission objective, orbital parameters are obtained, such as the launch time, location, altitude, inclination and mission duration. These parameters are inputs to a space environment code that will determine the proton, electron, and heavy ion flux or fluence spectra for the trapped radiation belt, the solar event activities and the galactic cosmic environment over the entire mission. The frequency and duration of these environments are also defined. This may also include meteoroid or orbital debris occurrences or the outgassing from surrounding system materials. A requirement that addresses spacecraft charging may require certain selections of insulating materials or a method for bleeding charge from the system. [12] Calculations are performed to transport the free field environments through a typical package shield to determine the radiation levels at the electronics. Ray-tracing analyses are important for determining the dose upon a part. For many low cost designs, 1D transport modeling is performed. However, this can be an overestimate of the actual dose, so 2D or 3D ray-tracing analyses are performed to refine the dose requirements. These latter dose analyses account for board and package materials, and other adjacent packages

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on the satellite. When these radiation levels are generated, they may be traded against several parameters: (a) cost of a system, (b) testability, (c) weight, (d) technologies for the system and (e) performance of the system. Technologies include hardened versus radiation tolerant or non hardened electronics. These latter parameters are important in driving the cost of a program and the complexity of a system design. The fewer the number of components, the higher reliability is the system. A super hardened system can be made, but cost or affordability may be so high that it can break the bank. Therefore, tradeoff studies are performed between cost and the radiation requirements until an agreeable median is found.

SPACECRAFT MISSION PARAMETERS • Orbital trajectory • Mission duration • Launch period

• Technology capability • Testability • Cost

• • • •

• • • •

SPACE RADIATION ENVIRONMENTS Trapped protons Trapped electrons Solar events Galactic cosmic

REQUIREMENTS Proton fluence Heavy ion LET Total ionizing dose Flare frequency, duration

Steps involved ... • Obtain orbital parameters, mission duration, launch time • Develop space environments, requirements • Conduct trade studies Figure 4. Generation of Space Requirements.

4.0 Program Initiation Phase During the program initiation phase the space environment specifications should be defined as one of the key documents going into a contract. It is important to have this document ready because it takes time to develop this material. Let us review some of the key characteristics of this environment. 4.1 Space Radiation Environment 4.1.1 Composition of the Space Environment The natural space environment is described by four important contributions as shown in Fig. 5 when we are dealing with the development of a space system. The top three will be the focus of this short course [13,14]. They are (a) the radiation from the Van Allen or trapped radiation belts, (b) the solar particle events (SPE) and (c) the galactic cosmic particles. The fourth environment consists of atmospheric neutrons that affect systems in the atmosphere or at ground level. The solar particle events can be described in several forms. The system designer considers the two types of solar flare events: the 90% worst case flare and what is sometimes called an Anomalous Large Solar flare are generally in one’s environment specification. Another type of solar event is called a CME or Coronal Mass Ejection. These first three environments are the primary radiation environments I-7

that a satellite system may have to deal with. For low altitude aircraft or ground systems, such systems have to deal with the atmospheric neutrons. These neutrons arise from the interaction of the galactic cosmic radiation with the atmospheric molecules. The population of these neutrons is greatest over the polar caps, while dropping by a factor of 2-3 over the equator.

• Trapped Radiation Belts (Van Allen) • Solar particle events – 90% worst case flare – Anomalous large solar flare – CMEs (Coronal Mass Ejections) • Galactic Cosmic Rays • Atmospheric neutrons Figure 5. Composition of the Natural Space Environment.

The earth has a magnetic core that is dynamic, constantly in motion. Magnetic field lines emanate from the north pole and reenters from the south. The field lines are compressed by the solar wind on the side facing the sun, expanding on the opposite side away from the sun several earth radii out and focusing down around the north and south poles. Some of the protons and electrons from the solar wind will be trapped by the geomagnetic fields of the earth. These particles will spiral along the earth's magnetic field lines entering from the north and south poles. With the right energy and momentum, the protons and electrons will spiral back and forth between the north and south poles. Since the protons and electrons have opposite charges, they also have a lateral velocity, such that protons and electrons drift in eastward and westward directions around the earth. Hence, this is how the radiation belts are formed. The two trapped radiation belts that girdle the earth are the inner and the outer belts. Protons and electrons from the sun constantly replenish the particles in the radiation belt. Without an iron core, there would be no magnetic field and we would constantly be exposed to the radiation from the sun. System designers take advantage of the geomagnetic shielding. For example, the Space Shuttle orbits beneath the geomagnetic field in order to optimize protection from extraneous exposure to the proton radiation. Likewise, commercial satellites orbit beneath the belts to minimize exposure to the radiation. By doing this, they can leverage on Commercial-Off-The-Shelf (COTS) electronics that may be sensitive to radiation. Depending on the type of mission, telecommunication, scientific experiments or surveillance, the orbit will determine the type of environments the satellite will see over I-8

the entire mission. LEO (low earth orbit, MEO (medium earth orbit) and GEO (geosynchronous earth orbit) are affected by the trapped radiation, from either the lower or the upper radiation belts. Solar events that emanate from the sun consist of protons and heavy ions. The protons have energies much less than those from the galactic cosmic environment. The solar wind carries these particles towards the earth and constantly replenishes the protons and electrons in the belt. These particles affect satellites that orbit over the north and south poles and for satellites that are far away from the earth's geomagnetic fields. These satellites could be planetary satellites or ones at the Langragian L1, L2 points in space. The galactic cosmic particles originate from outer space, from other stellar events. The galactic cosmic environment consists of protons and heavy ions of much higher energies than those coming from the sun. The galactic cosmic particles affect all orbits, especially those that fly over the poles or are outside the earth's geomagnetic field. Finally, the galactic cosmic particles interact with the atmospheric atoms, oxygen and nitrogen atoms creating neutrons in the reactions. Neutrons can penetrate into our atmosphere, effecting our aircrafts and ground equipment. One can learn more about the space environment today with monitors on several satellites [17, 18, 19]. If we look at the geomagnetic field lines more closely, the magnetic field is really not aligned with the rotational axis of the earth, but is tilted by 11 degrees with respect to the rotational axis of the earth and is skewed by about 200 km inward above Rio de Janeiro, Brazil. Here the magnetic field is weakest such that protons can penetrate to low altitudes, such as 300-400 km. On the opposite side, one would expect that the belt would be higher. Because the magnetic core is within the earth, the magnetic field lines are stationary with the earth. Hence, the belts rotate with the earth’s rotation. Over Rio de Janeiro, folks refer to this region as the South Atlantic Anomaly (SAA) region. Most satellites orbiting at low altitudes take advantage of the geomagnetic shielding and deal only with the protons in the SAA. We refer to these satellites as LEO or (Low Earth Orbit) satellites. As mentioned earlier many LEO satellites leverage on COTS technologies, giving them high performance, lower power and higher speed. However, because of the high sensitivity to radiation, care must be taken in the system design [20, 21]. Also to minimize the cost of hardening 2D or 3D ray tracing analysis are needed to determine whether COTS technologies may even be implemented with good design margins. If we look at the SAA region more closely, we find that these SAA protons are only located in a very small region of the earth. Hence, much of the LEO orbits that traverse this region may be 3-4 orbits out of 14 revolutions per day. 4.1.2 Trapped Proton Dose Between 800 to 4000 km the geomagnetic fields are strongest. The magnetic field lines are weakest around Brazil. As altitude increases, the proton isocontours begin to increase spreading around the earth, eventually forming the entire belt. The belts span approximately ±30-40° latitude. As one can see, by designing a satellite that skirts beneath the belt, one can avoid much of the space radiation and have to only deal with the radiation during a small fraction of the time. For LEO satellites with orbits within ±30°

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latitude, only the SAA and possibly the galactic cosmic particles are of concern. For LEOs that have an inclination angle above 30°, the SAA, galactic cosmic and solar events over the poles must be considered. Minimizing the amount of radiation allows the designer to leverage on COTS technologies. The Space Transportation Shuttle (STS) and Hubble Space Telescope (HST) have LEO orbits. The space shuttle takes advantage of this belt to help minimize any unnecessary radiation exposure to the crew during a solar event.

Total Dose [rad(Si)/yr]

Dose from trapped protons for circular orbits as a function of altitude and inclination. GEO does not see any trapped protons. 350-8000 km, 30 deg. inclination 2000 km, 0 deg inclination 2000 km, 60 deg inclination 2000 km, 90 deg inclination

1x105 8x104 6x104 4

4x10

2x104

LEO SAA protons

0

MEO 0

2000

4000

6000

8000

Altitude [km] Figure 6. Trapped Protons Dose Versus Altitude and Inclination.

Trapped protons are located at altitudes between 400 km up to around 9000 km as shown in Fig. 6. The SAA is located between 300 km to about 1000 km. Above 1000 km the belt is formed. As one can see the LEOs are between 400 to about 800 km. MEO (Medium Earth Orbit) lie in the heart of the belt where the satellite would be exposed constantly to the ionizing protons and electrons. Hardening is very difficult in this vicinity. At GEO, 35,000 km, the protons are gone. However, in Fig. 7 a GEO (Geosynchronous Orbit) satellite will have to face the outer electrons. 4.1.3 Trapped Electron Dose The location of the electrons is shown in the lower left plot in terms of dose versus altitude. We see that for LEO orbits, the protons really dominate. For the MEO and GEO satellites, the inner and outer electron belts dominate. They are represented by the two humps in the curve of Fig. 7 [22]. The external dose on a spacecraft depends on the location of the satellite and the internal dose depends on the material. For example, at LEO, the total dose for a ten year mission behind 200 mil of aluminum shield is around 20 krad(Si). For MEOs at 1000 to 3000 km, the dose can be as high as 600 krad per 10 yrs behind 200 mil of aluminum. For GEO at 35,000 km the dose is 60 krad(Si) for 10 years with 200 mil of aluminum shield. With thinner aluminum shields, the dose will increase.

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106 10-year electron dose behind 200 mils Al shielding

Protons (AP-8)

Electrons (AE-8)

104

GEO

MEO

105 105 104 103 102

LEO

Dose, rad(Si), Equivalent Al

Orbital altitudes significantly change the dose Van Allen Trapped Belts • LEO (550 – 1000 km) - 20 krad (Si) • MEO (1000 – 3000 km) - 600 krad (Si) • GEO (36,000 km) - 60 krad (Si)

103

102

103

104 105 4 3 2 1 0 1 2 3 4 5 6 7 8 9 10 Altitude (km) L- Shell (1 L-shell = 6370 km = 1 earth radius) Figure 7. Electron Dose Versus Altitude.

4.1.4 Solar Cycles Solar event activities follow a typical solar cycle that lasts for about 11 years as shown in Fig. 8. A system designer typically considers 4 years of low solar activities and about 7 years of active time from the sun. The galactic cosmic environment tracks in the opposite way. During the 4 years of low solar activities, the galactic cosmic environment is high. A satellite designer with a 5 to 7 year mission, might want to launch during this benign period in order to take advantage of the low radiation exposure. Fig. 8 shows a plot of the solar activities taken over three solar cycles. Each solar cycle is quite different with the largest solar event in terms of the proton fluence and energy. As one can see many of the 8 2 events have proton fluences about 10 to 109 protons/cm for protons that have energies greater than 10 and 30 MeV, respectively [23, 24]. 10

2

The largest solar events have protons as high as 1x10 protons/cm . The August 1972 and October 1989 flares were the large ones that were observed. They represent the Anomalous Large Solar (ALS) flares. The purple outline shows the number of sunspots as observed on the sun. There appears to be a correlation between the number of sunspots and the number of flare activities. The number of events and the number of occurrences of these protons versus fluence are plotted on a probability graph, one finds that they will lie fairly well on a linear line [25, 9 2 26, 27]. The 90% worst case events are represented by the 10 protons/cm occurrences. For the ALS flare, this falls in the 99.9% bracket. Such a flare has chance of 1 in 1000 to occur during a mission lifetime. The general design requirements are to account for the 90% worst case flares. That is, there is a 90% chance over the entire mission that the 9 2 satellite will be exposed to a flare of 10 protons/cm in fluence. For a GEO mission that may last for 15 years, one solar cycle needs to be accounted for such that, one ALS flare must be included in the environment specification. I-11

Solar max ~ 7yrs Solar min ~ 4yrs

Sunspot Number

Fluence (No. of protons/cm2)

Solar sunspots and solar activities during solar cycles 20, 21, and 22. August 1972 flare October 1989 flare

• August 1972 and October 1989 solar events (anomalous large solar flares, ALSF) are the largest ever observed over three solar cycles • Solar events of 109 protons/cm2 or less are considered the 90% worst case condition J. Barth, NASA/GSFC, 1997 IEEE Nuclear and Radiation Effects Conference Short Course, pg I-58.

Figure 8. Solar Cycles 20, 21 and 22 (typically 11 years).

Solar flares can last for several days. The largest solar flare, the October 1989 event, lasted for at least 8 days. Other lower intensity events lasted for a day or two. This is important to note when designing any memory refreshing or circumvention techniques into the system. Since the number of refreshes to memory can consume a large amount of computing time, a system engineer must factor this into one’s system maintenance plan in order to minimize the impact to the scientific data collection of a satellite. If an ALS flare were known to occur during a mission, the system engineer may have to scrub system only during the event. Recently studies show that there are two types of solar event activities. A solar flare is one that contains only protons of energies up to 600 - 700 MeV with little heavy ions and CMEs (Coronal Mass Ejections) contain electrons and heavy ions [28, 29, 31]. CMEs are also associated with disturbing the earth's magnetic field in terms of geomagnetic disturbances [30]. There are websites that illustrate these solar event activities. Today’s scientific missions with satellites and the electronics that are taking these pictures provide a marvelous source of scientific data. There are movies that show some of the solar event activities as detected by the SOHO project. Some of these movies were shown by Joe Mazur of Aerospace Corp in his 2002 short course. 4.1.5 Shielding Against Solar and Galactic Environments Fig. 9 illustrates the effect of shielding against the ions from a CME at GEO. This is a plot of the integral flux as a function of LET (Linear Energy Transfer) of the ions. LET is the amount of energy that is deposited per unit length by an ion. This integral curve is a composite of ion flux starting with protons to alphas, oxygen, silicon, nitrogen and ions up to iron. The curve continues with much lower ion intensities above iron to a LET of 2 100 MeV-cm /mg. The lower ion intensities contain heavier ions, such as zirconium,

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barium, platinum and lead. Note that by going from 100 mil to 3500 mil of equivalent aluminum shielding, one can attenuate the intensity of these solar ions by at least two orders of magnitude.

Integral Flux [ions/(m2-sr-s)]

At a LET of about 30 MeV-cm2/mg, the flux drops off dramatically • LET (Linear Energy Transfer) defined as amount of energy an ion can deposit along a unit path length

105 June 91 spectrum 104 GEO, Z = 1 - 92 3 10 2 10 100 mil 101 200 mil 0 10 500 mil protons 10-1 1000 mil Fe -2 10 2000 mil 10-3 3500 mil -4 10 10-5 alphas, O, Si, Ni 10-6 3.5-in. equivalent -7 10 -8 aluminum can 10 10-9 CHIME Zr, Ba, U reduce flux 10-10 -1 intensity by about 10 100 101 102 2 orders of LET [MeV-cm2/mg] magnitude

Figure 9. Combined Flare and CME Flux Spectra Versus LET and Shielding. Cosmic ray particles are much less intense, but extremely more energetic than solar ions. 3.5 inches of equivalent aluminum can reduce flux intensity by only 1 order of magnitude as compared to 2 orders of magnitude for solar particles. Cosmic Ray Integral Spectrum Integral Flux [ions/(m2-sr-s)]

Solar minimum, GEO, Z = 1 - 92

101 100 10-1 protons 10-2 Fe 10-3 10-4 10-5 alphas, O, Si, Ni 10-6 10-7 10-8 10-9 10-10 Zr, Ba, U 10-11 -12 10 Space Radiation 5.0 10-13 -1 10 100 101 102

100 mil 200 mil 500 mil 1000 mil 2000 mil 3500 mil

LET [MeV-cm2/mg] Figure 10. Effect of Shielding on Cosmic Rays: LET Spectrum.

The galactic cosmic particles consist of ions ranging from protons to uranium ions [32]. The most abundant element is hydrogen, followed by helium and on down to iron before the abundance drops dramatically. The ion energies of the solar events are also much lower than the energies from the galactic cosmic environment. The galactic cosmic ions have energies in the GeV range. It is postulated that these ions arrive from distant stars, supernovae or from the “Big Bang.” However, the fluxes of these particles are much lower than the fluxes coming from solar events. The galactic cosmic flux is about 3-4 orders of magnitude lower. For example, the flux for protons from a solar event is about 106

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protons/(m2-sr-s), while the galactic cosmic flux is 101 protons/(m2-sr-s). The galactic cosmic environment is most active during solar minimum or during the 4 years of minimum solar activity. Since the galactic cosmic energies are much higher in the GeV range than those from the sun, stopping these particles is almost impossible. Fig. 10 is similar to Fig. 9 showing the integral flux of the galactic cosmic ions versus their LET or stopping power at GEO. By going from 100 mil to 3500 mil of equivalent aluminum shielding, the intensity of the galactic cosmic flux only diminishes by 1 order of magnitude as compared to the particles from the sun which dropped by at least 2 orders of magnitude. Mitigating single event effects (SEE) from the galactic cosmic environment is generally done by other means other than shielding. Trapped Radiation (Van Allen belt) - protons, electrons • Trapped belts - MEO • South Atlantic Anomaly region - LEO – Geomagnetic axis tilted by 11° from rotational axis – Skewed by 200 km, intensity highest over Rio de Janeiro, Brazil LEO

Solar flares - protons, heavy ions, electrons • LEO, MEO, GEO • 11 yr solar cycle (7 active yrs) – LETs between 1 - 100 MeV-cm2/mg – flux ≤ 106 ions/(m2-sr-s) 90% worst case Aug. 72, Oct. 89 ~ 8 days CMEs (Coronal Mass Ejections) Galactic Cosmic Rays - protons, heavy ions • LEO, MEO, GEO • LETs between 1 - 100 MeV-cm2/mg • flux ≤ 10 ions/(m2-sr-s)

Solar Particle Events

Solar Cycles 20, 21, 22

Figure 11. Space Environment Highlights.

4.1.6 Environment Highlights Fig. 11 summarizes the characteristics of the space environment that LEO, MEO and GEO space systems may have to face as a requirement. In the trapped radiation belts, protons and electrons are the dominant contributors between 300-800 km. The protons are from a region where the magnetic field is weakest. This region, called the South Atlantic Anomaly region is located over Rio de Janeiro, Brazil. The weakness of the magnetic field comes about from a 11° tilt of the geomagnetic axis with the earth's rotational axis. The geomagnetic axis is also skewed such that it is 200 km closer over Brazil. At higher altitudes, the SAA spreads and merges into the belt itself. LEO satellites orbiting within ±30° latitude have the geomagnetic field to protect the spacecraft and only have to tend to the radiation from the SAA. At higher altitudes, the second belt contains predominantly electrons. GEO satellites are affected by total ionizing dose. Solar events occur predominantly during 7 years of a 11 year solar cycle. The particles primarily range from protons up to iron. Elements up to uranium may exist at much lower

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magnitudes, although this is a subject under investigation. The solar events can be described as three kinds. For spacecraft designers, the most familiar requirement is the 9 2 90% worst case flares that have fluences as high as 10 protons/cm for proton energies greater than 10 MeV. These flares are expected to have a 90% chance of happening during a mission lifetime. The second kind of solar event is the Anomalous Large Solar flare, 10 such as the August 72 or Oct 89 flare. These flares have proton fluences as high as 10 2 protons/cm or greater and occur possibly once during a solar cycle. The third kind of solar activity is called CMEs. These solar events have heavy ions and electrons. The LET 2 of the heavy ions can reach as high as 100 MeV-cm /mg, although the majority of the ions reach iron before falling off rapidly. Solar events impact satellites that orbit over the poles, since they orbit outside the earth's geomagnetic field. Galactic cosmic particles have ionized particles similar to those from the flares and CMEs. However, their energies are much higher, but the flux is orders of magnitude down. Shielding is difficult in stopping these particles. Their LETs range as high as 100 MeV2 cm /mg. 4.2 Radiation Requirements Within the framework of the various radiation environments, a set of radiation requirements can be developed. Examples of some of the data in a requirement are shown in Fig. 12. A dose depth curve based on the orbital parameters and the radiation environments the system will see is derived by various radiation environmental codes [33, 34, 35]. Then there are various integral flux and fluence plots for the galactic cosmic radiation, the solar event activities and the trapped belt radiation. The upper left plot is the dose per year versus shielding thickness as derived by radiation transport codes [36, 37, 38, 39]. The dose is the accumulation of all the environments the system will see over the entire mission. On the far right is an example of an integral flux versus LET curve for several shield thicknesses for a 90% worst case solar event. Integral Flux versus LET Curve 90% Worst Case Solar Event Integral Flux [ions/cm2-sr-sec]

Dose per year (rad(Si)/yr)

Dose Depth Curve

102 101 100 10-1 10-2 10-3 10-4 10-5 10-6 10-7 10-8 10-9 10-10 10-11 10-12 10-13 10-14

0.001

500 mil 300 mil 100 mil

0.01

0.1 1 LET[MeV-cm2/mg]

10

100

Others: • micrometeoroids • Dose depth curve • spacecraft charging • Trapped e- integral fluence/flux versus MeV • UV radiation • Trapped p+ integral fluence/flux versus MeV • Atomic oxygen erosion • Galactic cosmic integral flux versus LET • Outgassing • Solar flare event proton and heavy ion spectra • Solar electromagnetism Figure 12. Typical Space Environment Requirements.

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Other plots include integral flux curves versus LET curves of the galactic cosmic and ALS flare. Tabulated data of these plots are also included to allow the engineer to perform one’s own risk analysis. The radiation environments mentioned above penetrate into the electronic packages. There are other space environments that are included in the specification but will not be covered in this short course. They include (a) plasma, (b) solar electromagnetism, (c) UV radiation, (d) micrometeoroid and orbital debris, (e) thermal radiation, (f) atomic oxygen erosion and (g) out gassing [12]. This set of radiation specification provides the requirements within which a space program must comply. Let us understand what radiation effects a system can exhibit based on these requirements. 4.3 System Radiation Effects Taking the radiation specifications that were discussed in the previous chart to the system level, one finds the following results, shown in Fig. 13. For example, in a MEO mission, total ionizing dose contributed by protons and electrons depositing charge in the gate oxides of a MOSFET technology will result in voltage threshold shifts in the n and p channel transistors. Leakages will occur making n channel transistors harder to turn-off. At the package level consumption of supply current will increase possibly on a number of parts, exceeding the power capability of the system. Eventually, the parts will fail to function properly and the result will lead to a system hard failure. For a LEO mission that orbits in the trapped radiation belt, the accumulation of dose can lead to system hard errors as mentioned above. With COTS technologies, one needs to be aware of another effect called Single Event Latch-up (SEL). An unprotected latch-up without current limiting and power recycling can lead to the burn-out of a device with the possibility of severing a bond wire or creating an opening of metallization from electromigration. Transient and single event upsets that disturb the memory state of a register or a memory cache are considered as soft errors in the system. These errors generally do not cause a catastrophic problem. They can be corrected by refreshing the memory back to its original state. In other cases, it can be an issue if a processor were to shut down due to double errors within a word that can not be corrected without a reboot. Reboot of a system may require times longer than a few minutes and may impact the mission of a program. In order to apply the discipline of hardness assurance to our designs, it is necessary to have an understanding of the important radiation phenomena in order to apply techniques properly. Radiation as described in the environment section, comes in the form of protons, electrons and heavy ions. In space the majority of the ionizing particles are protons. When these particles penetrate into electronic technologies, such as MOS (metal oxide silicon) or bipolar transistors, they will ionize along their path depending on the LET they have and create electron-hole pairs. It takes 3.6 eV to create an electron-hole pair in silicon and 17 eV in silicon dioxide [40, 41]. When a particle traverses a semiconductor device, the particle will ionize along its path passing through the passivation layers or oxides before entering into the silicon material. Charges that are created in the oxide may lead to radiation effects called Single Event Gate Rupture (SEGR) in MOSFETs or linear devices, Single Event Dielectric Rupture (SEDR) in Field Programmable Gate Arrays, and

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Total Ionizing Dose (TID) in MOSFETs and bipolar technologies. In MOSFETs TID is in the gate oxide, while in bipolar technologies, the TID is in the field oxide. Charges created in the silicon material results in mobile charges that cause radiation effects called Single Event Upsets (SEU), Single Event Transients (SET), Single Event Burnout (SEB) or Single Event Latch-up (SEL). A few of the more important effects will be discussed below. The following references [43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57] describe various types of single event effects in electronic components. Several of these effects will be discussed in further detail in this short course. • Soft errors can be cleared by refreshing the electronics

• Hard errors are permanent (i.e., latchup, total dose, gate rupture, single event burnout) Natural Environment

Effect on Electronics

System Results

Trapped Protons

SEU, SEL, transients, ionizing & damaging dose

Soft and hard errors

Trapped Electrons

Total dose ionization

Hard errors

Solar Flares

SEU, SEL, transients, ionizing & damaging dose


Galactic Cosmic Rays

SEU, SEL, transients


Atmospheric neutrons

SEU, SEL, transients


Figure 13. Environments and System Level Radiation Effects.

SEL stands for Single Event Latch-up that is the result of a pnpn path that latches into a high current state. SES (Single Event Snapback) is found in NMOS devices. This is a result of the parasitic npn inherent beneath an NMOS transistor. SEB is the result of a SES causing the parasitic npn to draw high current and to burn out the transistor. SEFI stands for Single Event Functional Interrupt that is associated with processor upsets. MBU are multibit upsets from the same single particle event. In a system one is concern with MBUs from a single word. EDAC (error detection and correction) addresses the detection and correction of single bit errors, but can not correct double bit errors within the same word. When an ionizing particle strikes a semiconductor device, the event happens very quickly, on the order of several hundred picoseconds. The electron-hole pairs that are created, drift or diffuse, depending on the presence of an electric field. Electrons drift very rapidly, leaving the holes behind to move slowly. 4.4 System Strategies for Reducing Radiation Effects Depending on the mission objective, a system engineer must balance the performance criteria and minimize exposure of the system by taking advantage of the radiation environment as described in Fig. 14. One approach would be to limit the orbit beneath the trapped radiation belt to minimize exposure to the solar events or the galactic cosmic

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environments, such as the Space Shuttle and the Hubble Space Telescope. If a LEO mission can stay within ±30° latitude, the only radiation requirement would be from the SAA protons and the galactic cosmic particles. Other techniques that a system designer can choose from include localized shielding, software mitigating methods, such as error detection and correction, parity checking or refreshing, watch-dog timing and verifying a write or read command before executing the instruction. Many of these methods are addressed above the parts level. Know characteristics for space environment well – Take advantage of geomagnetic shielding – Orbit between ±30 deg latitude, below 1000 km – Orbit between inner and outer belts – Orbit outside belts – Short orbital missions – Know realistic number of flares Localized shielding Package shadowing for more sensitive packages Electronics mitigating techniques – current limiting, EDAC, redundancy, recycling power or watch-dog timing Parts selection Software mitigating techniques – parity checking, scrubbing or refreshing Hamming codes, verifying write before execution Figure 14. Other Strategies of Reducing Radiation Effects.

4.6 System Hardening Approaches System design encompasses a variety of different hardening techniques as described in Fig. 15. It should be noted that component hardening at the Integrated Circuit (IC) level is just one possible hardening solution. IC hardening is supported by DTRA (Defense Threat Reduction Agency), Sandia National Labs, NASA and various European agencies. The suppliers of hardened parts include Honeywell, BAE Systems, ST Microelectronics, and Intersil. The radiation tolerant devices may include: Xilinx, Actel, Analog Devices, Aeroflex, National Semiconductor Corporation and others. System hardening techniques cover (a) parts selection, (b) shielding, (c) circumvention, (d) current limiting, (e) watch dog-timing, (f) security coding, (g) fault-tolerant software, (h) degraded electrical performance design, (i) environment modeling, (j) IC modeling, and (m) GBS (grounding, bonding and shielding) for EMI /EMC (Electro-Magnetic Interference / Electro-Magnetic coupling) protection. Along with these hardening techniques, there are various associated software codes to support the analysis. The codes are used to help perform radiation transport for shielding analyses, simulate a circuit response to SEE, determine the SEE rate [122, 124], perform

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risk analysis [121] and help determine parameter limits. In addition studies include the interpretation of heavy ion and proton data [123] and how they can be used interchangeably [119, 120]. System design encompasses a variety of different techniques. Parts hardening is just one of those disciplines. System Hardening Techniques

Supporting Codes

Parts selection Fault-tolerant software Shielding - materials SpaceRad, CREME96, NOVICE Circumvention, recycle power PSPICE Current limiting PSPICE Port entry protection PSPICE Watch-dog timer PSPICE • Degraded parameters PSPICE • Environment modeling SpaceRad, CREME96 Radiation physics FLUKA, GEANT4, SRIM • IC modeling PSPICE, WINSPICE

Parts Hardening Silicon process hardening is funded by various organizations, i.e., • DTRA, NASA, JPL, etc. Typical foundries are • STMicroelectronics • BAE Systems • Honeywell • National • Analog Devices • Intersil, etc.

Figure 15. System Hardening Approaches. Radiation Effect

Solutions

Ionizing dose - electron and proton

Shield Replace with hardened or rad-tolerant parts Account for component degradations

Displacement damage

Replace with hardened CMOS Account for bipolar gain degradation Current Transfer Ratio in electro-optics

Single-event latchup

Current limit and cycle power Shield Use thin epitaxial, SOS, SOI technologies

Single-event upset

Refresh, error detection, and correction, redundancy or watch-dog timing Replace with hardened or rad-tolerant parts Shield

Other suggestions: 1. Review parts lists for sensitive parts 2. Verify by irradiation or acquire radiation data

Figure 16. Hardening Approaches.

It's also important that the parts list be reviewed by a radiation analyst in order for a designer to know what steps must be taken to design a robust system that will meet the radiation requirements. Generally, a radiation analyst will search for radiation data that might exist in any of the open literatures, internal databases or websites at NASA/GSFC, ERRIC or JPL. If data does not exist, the analyst would recommend testing the part. I-19

Several hardening solutions have been mentioned along the way. Fig. 16 summarizes a number of these solutions at the parts, board, package and system levels [128]. For ionizing dose effects, shielding can reduce the electron dose for a GEO mission. However, for other orbits, such as LEO, MEO or even a mission to Jupiter, protons are difficult to shield. The best solution is to select hardened parts with extra design margin added to account for degraded parameters in the circuit design. For proton displacement damage, MOS technologies are generally immune to this effect. For bipolar transistor technologies, gain loss is serious and must be accounted for by including degraded DC and AC parameters in the circuit design. Single event latch-up can be mitigated with current limiting and recycling power. However, radiation testing is needed to determine the latch-up sensitivity of the parts and to determine whether current limiting and recycling power will work. Single event upsets can be mitigated at the part and system levels. At the parts level, LETs 2 of greater than 20 MeV-cm /mg will allow the parts to survive through a proton environment such as a large solar flare. However, with upsets in a memory device or in a processor, an EDAC circuit and a watch-dog timer are necessary to status the health of these complex devices. Other software error checking techniques may be needed, such as Reed Solomon or Hamming decoding. 4.7 Space Environment Modeling Tools There are analysis codes that support the radiation analyst [37, 38, 39]. In Fig. 17 a list of space environment codes is available to support satellite or launcher programs. A few can be found on the web [33]. For a satellite program, the codes will support circular and elliptical orbits. For a launcher program Space Radiation [35] has the capability to generate a ballistic trajectory. Many of the codes will support radiation transport calculations of heavy ions and protons. Space Radiation has the capability to model neutron effects at atmospheric levels. In addition some of these codes will calculate the total ionizing dose that passes through a shield and onto an electronic part. Others will also calculate the Single Event Effects rates. These space environment models have improved over the years. One of the major accomplishments has been the revision of the heavy ion spectrum originating from the solar events. The based line code was CREME developed by Naval Research Laboratory. Using the earlier model would have given overly conservative SEE rates that made many satellite programs difficult to leverage on COTS. With the reductions made in the heavy ion content based on the October 89 and March 91 solar events, many satellite programs can now leverage on COTS parts because their SEE rates are more reasonable.

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Codes are used to: • Model vehicle's space environments • Transport radiation through spacecraft wall/package materials • Calculate rate of occurrence of a radiation effect Satellite Launcher Heavy Protons Total Parts ions transport dose prediction transport

Code Space Rad 4.0/5.0

X

CHIME

X

X

X X

X

X

X

CREME96 – web based

X

X

Boeing-MACREE

X

X

X

SPENVIS – web based

X

X

X

X X

X

• Improvement in space environment models helped pave the way for insertion of COTS in space systems • Predicted heavy ion flux from flares reduced by several orders of magnitude Figure 17. Space Environment Codes.

4.8 Shielding Example Typical dose versus shielding thickness curve

At about 100 mils electrons fall off rapidly, while further shielding does not provide any further benefit from energetic protons

Dose per year (rad(Si)/yr)

Curves represent • Trapped electrons • Bremsstrahlung radiation • Solar event protons • Total

650 km, 90° inclination orbit Trapped electron Bremsstrahlung Trapped proton Solar Proton Total Natural Dose

1x106 1x105 1x104 1x103 1x102 1x101 1x100 1x10-1 0

100

200

300

400

500

Shielding Thickness [mils of Al]

Figure 18. Total Ionizing Dose versus Shielding.

Determining the dose at a part is important in determining the level of hardness required for the parts. If the margins are high, surveillance may not be necessary. If the margins are low, lot sampling may be required. As one can see ray tracing or transport analysis is important in determining accurately whether one will have adequate margin without additional testing later. Therefore it is important to generate a dose versus shielding thickness curve. As an example of a shielding calculation, Fig. 18 shows the dose per year versus shielding thickness for a LEO system. The trapped electrons are negligible above 200 mil of aluminum. The trapped protons or solar flare protons are more difficult to

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shield. As one can see in Fig. 18, adding 500 mil of shielding only dropped the dose by a factor of two from 1krad(Si) to 400 rad(Si). Example: 650 km, 90° inclination circular orbit How much shielding is needed for a 3krad (Si) commercial part? Given: Need 3X margin and there's already 150 mil of package shielding available around the part Shield(mil)

Trapped e-

brems. e-

Trapped p+

90% w/c flare

ALSF*

Total Dose

0.1 0.5 1 3 5 7 10 20 50 60 80 100 200 300 400 500

5.02E+05 4.80E+05 3.50E+05 1.53E+05 8.85E+04 5.82E+04 3.57E+04 1.26E+04 3.21E+03 2.39E+03 1.41E+03 8.75E+02 1.02E+02 1.01E+01 1.10E+00 1.17E-01

1.54E+02 1.53E+02 1.51E+02 1.04E+02 7.63E+01 6.11E+01 4.68E+01 2.59E+01 1.26E+01 1.09E+01 8.48E+00 6.96E+00 3.77E+00 2.70E+00 1.73E+00 1.84E+00





* ALSF - Anomalous large solar flare

Figure 19. Total Dose Calculation.

Solution: Design margin = Failure threshold/Mission lifetime dose on part or Failure threshold / Design margin = Mission lifetime dose on part 3krad (Si) commercial part / 3X margin of safety = 1.0 krad (Si) – According to table, one obtains 1k- 861rad (Si) with 190-200 mil of aluminum – With 150 mil already available, then 200 mil - 150 mil = 50 mil more to achieve 3X margin – If volume space is a problem, use tantalum, ρTa/ρAl = 6:1 effective shielding to aluminum (i.e., 200 mil/6 = 33.3 mil of tantalum) – Location of tantalum shield must be analyzed for dose enhancement

Figure 20. Shielding Calculation for Total Ionizing Dose.

Let us look at a shielding problem. Suppose the designer must choose a part that is three krad(Si) radiation hardened for a commercial space program. The board will be contained in a 150 mil aluminum walled package. A margin of 3X was determined to be

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appropriate for this program. Fig. 19 tabulates dose from the different space environment constituents. By dividing the hardness of the part by the design margin, one obtains the minimum dose at the part over the entire mission. In this example, the dose would be 1 krad(Si). According to Fig. 20 1krad(Si) to 861 rad(Si) requires 190 to 200 mil of aluminum. Therefore, with about 150 mil from the package itself, only an additional 50 mil of aluminum is required. If the package did not allow enough room to accommodate the additional 50 mil of aluminum, one can use a higher atomic number material. For example, the equivalent amount of tantalum to aluminum is the ratio of their mass densities. Therefore, 200 mil of aluminum is equivalent to 33.3 mil of tantalum. More elaborate transport models can be used to refine the dose at a part.[37] Most 1D analysis does not account for unique angles where the walls may be thinner allowing more dose at a particular section of a part. A system analyst has to assess the vulnerability of the part. With possible part to part variation, a commercial part may degrade below the anticipated design margin due to process variations. With a 3X margin surveillance testing is recommended for this part according to Mil. Handbook 814/815. 4.9 Upset Rate / Risk Example If a designer has to select a higher performance memory device, one of the common questions that arises is “what is the risk and upset rate for 200 memory devices in a LEO satellite”. An assumption is given that the critical exposure time is 8 min during a 90% worst case solar event. Also the LET threshold of the memory devices is 3 MeV-cm2/mg with a saturated cross section of 2x10-2 cm2/device. Fig. 21 shows the integral flux spectrum versus LET of such a solar event. At a LET of 3 MeV-cm2/mg, the integral flux is 8x10-6 ions/(cm2-sr-s). LET spectra of ion contribution from 90% worst case solar event through 500 mil Al shielding, 650 km, 90° inclination

Integral Flux [ions/cm2-sr-sec]

90% Worst Case Solar Event, 500 mil Al 102 101 100 10-1 10-2 10-3 10-4 10-5 10-6 10-7 10-8 10-9 10-10 10-11 10-12 10-13 10-14

Integral Flux = 8x10-6 ions/cm2-sr-s

0.001

LET = 3 MeV-cm2/mg

0.01

0.1 1 LET[MeV-cm2/mg]

10

Figure 21. Solar Event LET Heavy Ion Spectrum.

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100

By taking the product of the integral flux at a LET of 3 MeV-cm2/mg with the saturated cross section, an estimate of the upset rate is obtained. It is assumed that the upset cross section of the device has a sharp knee. The upset rate in an 8 minute interval is determined to be 0.19 upsets in a system of 200 memory devices as shown in Fig. 22. The risk is determined by using the Poisson probability distribution and placing the upset rate into the argument of the exponential function as shown in Fig. 23. Including the 8 min duration into the equation, we obtain an 18% chance for an upset to occur during the 8 min interval.

Problem: For spacecraft at 650 km, 90° inclination the system operates for 8 min over the pole. Calculate the upset rate and assess the risk for 200 memory devices with a LET threshold of 3 MeV-cm2/mg and a cross section of 2x10-2 cm2/device behind 500 mil of aluminum. Number of upsets = cross-section of device x ion flux x exposure duration = 2x10-2 cm2/device x 8x10-6 ions/(cm2sr-s) x 8 min x 60s / min x 4πsr x 200 devices = 0.19 upsets/system Figure 22. Upset Rate Analysis Use Poisson's distribution to determine the probability of an upset in the system: Poisson's equation: P(k;t) = e-λt(λt)k/k! Probability of no upsets in the time interval t is P(0;t) = e-λt, where λ is the upset rate and the total number of bits. Probability of success, Psuccess = exp (- no. of bits x upset rate per flight per bit x bits/device x no. of devices x flight time x no. of flights) Flight time = exposure time of 8 min. No. of flights = 1 No. of devices = 200 Psuccess = exp[ - number of upsets/system-flare] = exp[- 0.19 upsets/system-flare] = 0.82 Pfail = 1 - Psuccess = 0.18 or 18% chance that an upset will occur during the 8 min exposure period

Figure 23. Determining the Risk or Probability of Success.

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By performing this risk analysis, one can establish the guideline for procuring parts during the manufacturing phase of the program. For example, if parts tested during the development phase exhibited a LET of 3 MeV-cm2/mg, but was discovered to have a LET lower than 3 MeV-cm2/mg when purchased during production, then the margin is reduced and the system may exhibit a higher probability for upsetting. Likewise, in the above example of calculating the dose at a part, if the parts procured later exhibited a higher sensitivity dose, the 200 mil of aluminum or the 3X would not be adequate. 5.0 Hardening Against Proton Since protons are the predominate specie in the space environment, one needs to understand how they interact with the specific electronics in a semiconductor device and how their effects can be mitigated. Protons interact differently than heavy ions in causing SEEs (Single Event Effects). One uses different units to characterize protons versus heavy ions. For protons MeV is used as the unit of energy, whereas for heavy ions the stopping 2 power or Linear Energy Transfer (LET) in MeV-cm /mg is used as the unit. It should be noted that for protons greater than 200 MeV, the energy lost per unit length, LET, is very small, such that very little charge is deposited in the silicon material. Yet a large number of upsets is observed with protons. Here lies the reason. proton

28Si 14

29P 15

LET, Stopping Power [MeV-cm2/mg]

Primarily one of the following reactions occur

4He 2

24 24Mg 12 12

+

27 27Al 13 13

+

Proton (>50 MeV) + Si nucleus results in by-products • Al+, Mg+ ions (1-5 MeV), alpha, p+ • LET (Al+, Mg+) < 14 MeV- cm2/mg Hydrogen

proton

+ protons

+

102

Helium Magnesium Aluminum

LET = 13.9 MeV-cm2/mg

101

The maximum linear energy transfer (LET) of Al+ or Mg+ establishes the necessary proton hardness, i.e., 20 MeVcm2/mg

100 10-1 10-2 4 10

105

106

107

108

109

Energy [eV]

Figure 24. Proton Physics. 23

When an energetic proton enters a silicon material as shown in Fig. 24, it sees 10 silicon nuclei, i.e., Avogadro’s number of atoms in a mole of material. Based on the nuclear capture cross section between protons and a silicon nucleus, there is a probability for a proton to be captured by a silicon nucleus. Silicon-28 now becomes phosphorus-28 that is unstable and decays in a few microsecs to either an aluminum-27 nuclei plus two protons or a magnesium-24 nuclei plus an alpha and a proton. The energies of the aluminum and magnesium ions range between 5-10 MeV. The chart in Fig. 24 is a plot of the LET of a proton, alpha, aluminum and magnesium ions versus energy. According to this chart, the 2 aluminum or magnesium ions have a maximum LET of 14 MeV-cm /mg between 10-50 I-25

MeV. For this reason, if one wanted to design a circuit that could be immune to proton 2 upsets, the LET hardness of the circuit has to be at least 15 MeV-cm /mg or greater. This would make the circuit very difficult to upset with protons. Similar principles can apply to neutron single event effects because of similar nuclear reaction properties. From a hardness assurance viewpoint, if a new supply of parts were to be procured, and the supplier made improvements to the part by making a faster part, it may result in the lowering of the LET hardness to upsets. Similarly if the supplier eliminates the need for an epitaxial silicon layer, the LET threshold for latch-up may drop from 26 to 10 MeV2 cm /mg. Process controls need to be applied in order to monitor these changes. Heavy ions can exhibit the same reactions as protons in space. However, the ion energies at a test facility, such as Brookhaven National Labs are lower so their predominant reaction in silicon is primarily direct ionization rather than from nuclear interactions. 6.0 Key Integrated Circuit / Semiconductor Degraded Parameters and Effects When one looks at radiation effects at the semiconductor level, one can distinguish two types of effects, those that are transients and those that exhibit long term degradation. Fig. 24 highlights some of these effects, the cause and how they impact the MOS or bipolar electronic technologies [42]. For example, dose ionization is caused by trapped charges in the oxide. For MOS devices, there would be voltage threshold shifts from the gate oxide and from the field oxide. Leakage currents can occur due to an inversion of the silicon beneath the gate oxide between a drain and a source of a transistor. These leakage currents can slow the charging or discharging of a capacitive node, hence change the propagation delay times or rise and fall times of the AC characteristics of a circuit. Similarly, for a bipolar transistor, the increase in base leakage current decreases the Hfe or DC gain performance of a bipolar transistor. The AC and DC circuit parameters are degraded. Effect

Cause

MOS

Bipolar

1. Ionizing Dose

Oxide – trapped charge

Threshold voltage shift Leakage current Increased current Degraded AC/DC parameters

Current gain degradation Increased base leakage Increased current Degraded AC/DC parameters

2. Very low dose rate

Interface traps

Threshold voltage shift

Gain degradation

3. Particle interaction

Lattice damage

minimal

DC gain degradation (increased base leakage) Degraded AC/DC parameters

Other effects 4. Particle ionization

Capacitance discharge

Single-event upset Single-event upset Single-event transient Single-event transient

5. Gate oxide rupture Particle ionization Power MOS – SEGR 6. Parasitic SCR (pnpn) Initiation

Latchup

SEGR in linear caps.

Single-event latchup

Latchup

Single-event induced breakdown

Figure 24. Key Integrated Circuit / Semiconductor Degraded Parameters and Effects.

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Displacement damage arises from elastic or inelastic scattering of protons with a silicon atom recoil creating trapping sites in the base region of a bipolar transistor. Other effects include SEGR [105, 106, 107, 108], SEU, SEB [104] and SEL [58, 59, 61, 63] of which some will be discussed later [109]. Having an awareness of these effects is important in not only choosing the correct hardening technique in the design, but also to know what effect might occur that could impact the design margin when a supplier changes their process. Hardness assurance is to make sure that the hardening techniques still apply during manufacturing and that the design margin has not changed since the beginning of the design. 7.0 Total Ionizing Dose Effects 7.1 MOS Devices Total ionizing dose (TID) in MOS devices is created by the ionization of a particle in the insulating materials, such as SiO2, in a semiconductor device as shown by the semiconductor band diagram of Fig. 26. For an MOS device, the two regions that are sensitive to TID are the gate oxide and the field oxide. The field oxide is the isolation region separating individual transistors. For a bipolar device the sensitive area would only be the field oxide region over the base and isolation regions. With positive gate bias on an electrode over an oxide, electrons are swept rapidly towards the gate electrode leaving holes behind either trapped in the oxide or transported through the oxide by drift or diffusion towards the silicon dioxide/silicon interface. Holes will recombine along the way towards the silicon dioxide/silicon interface. However, some holes will react along its path creating hydrogen that in turn will drift towards the interface. If a hydrogen atom does reach the Si-SiO2 interface, it will create an interface trap or a dangling bond at the Si-SiO2 interface. This dangling bond is defined as an interface state. Band diagram of an MOS capacitor with a positive gate bias. Illustrated are the main processes for radiation-induced charge generation and trapping.

e-h pairs created by ionizing radiation

Nit: interface trap formation (Pb )

SiO2

Si

+ + +_ _ _ Not: deep hole + _ _ + trapping (E’) + near interface +

+ gate bias

proton transport

gate proton release

+

H+ hopping transport of holes through localized states in bulk SiO2

Figure 26. Radiation Effects of a MOS Device.

For the holes trapped in the oxide that we refer to as oxide traps, a negative bias is required to compensate for these extra positive charges. Therefore one observes a shift of the

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transconductance I-V curve as shown in the traces of Fig. 27. When the I-V curve exhibits a parallel shift as indicated by the arrow, only positive charges are observed. However, with a change in the slope of the curve, interface states are then involved. Leakage currents between the drain and source can be observed, created by the inversion of the silicon beneath the field oxide around the edges of a transistor as shown in the traces of Fig. 27. Measuring techniques such as the subthreshold I-V method can be used to extract the number of oxide traps and interface states created by the total ionizing dose. However, if leakage currents are too high, then the dual transistor measurement technique is recommended. As mentioned above charges are generated by the ionizing particles that traverse the device. Charges are generated in the field oxide located in the bulk of the passivation layers and in the gate oxide of a MOS transistor. These extra charges will cause voltage threshold shifts in an n or p channel device. For an n channel device, one observes a negative voltage shift. Similarly, there is a negative threshold shift in the p channel as shown in Fig. 27.

Drain Current [A]

P- Channel Power MOSFET I-V characteristic

1.E+00 1.E-01 1.E-02 1.E-03 1.E-04 1.E-05 1.E-06 1.E-07 1.E-08 1.E-09 1.E-10 1.E-11 -10

Pre-Irrad 1 krad 10 krad 100 krad 250 krad 450 krad 1000 krad -8

pre 1Mrad(Si)

-6

-4

-2

0

Gate Voltage [V] Solutions: • Account for threshold voltage shifts and leakage currents in the n and p channel transistors Figure 27. Radiation Total Ionizing Dose Effects in MOSFETs. [196]

If parts are annealed with time, bias and temperature, as recommended by the Military Standard 1019.4 methodology, this will allow some of the holes to migrate towards the SiSiO2 interface. The creation of interface states is commonly known as the “rebound” effect. The hydrogen that is generated will migrate to the interface, creating a dangling bond or an interface state. For n channel transistors, the interface state is a negative charge that causes the transconductance curve to shift to the right. If the voltage shift passes the original pre-irradiated threshold voltage of the device, this phenomenon is known as the "rebound" effect, known to exist only at low dose rates such as in the space environment. For n channel transistors, the “rebound” effect requires a higher voltage to turn on the I-28

device [81, 84, 85]. The “rebound” effect has been observed in MOS devices. However, this should be distinguished from the Extremely Low Dose Rate Sensitive (ELDRS) effect that has been only associated with particular bipolar devices. Irradiation has been performed at varying rates with leads shorted to various biasing conditions. This topic will be further discussed in the Future Issues section below. Other related areas where total ionizing dose effects are being studied include PEMs (Plastic Encapsulated Microelectronics) and post manufacturing processes. Microelectronics packaged in plastic provide a low cost approach to design. However, the radiation effects are not that well understood [83]. Studies are being conducted to look at high temperature stress steps during packaging and burn-in that may impact the radiation sensitivity of a part [82, 86]. 7.2 Bipolar Devices In a bipolar transistor one observes total ionizing dose effects from the charges that are trapped in the field oxide. Here we observe base leakages that are evident in the drop of the DC gain or Hfe at Icollector currents below 100 mA as shown in Fig. 28. The gain of the transistor is normally above 100. However, after each level of irradiation, the gain of the device diminishes as indicated by the arrow on measured data of a pnp transistor, a 2N2907. The gain drops dramatically with small Icollector currents, i.e., 1 mA or less. This degradation in Hfe must be accounted for in a circuit design. In other words, the designer should use degraded parameters from radiation and should not just use the degradations called out in reliability standards.

Hfe, Collector / Base Current

Gain degradation from gamma irradiation Pre-Irrad 5 krad 10 krad 25 krad 50 krad 100 krad 250 krad 500 krad 1000 krad

pnp Bipolar Transistor

180 160 pre 140 120 100 80 60 40 20 0 1Mrad(Si) -20 1.E-13 1.E-11 1.E-09 1.E-07 1.E-05 1.E-03 1.E-01 1.E+01

Collector Current [A] Solutions: • Account for gain degradation and leakage currents in the npn & pnp transistors Figure 28. Radiation Total Ionizing Dose Effects in Bipolar Transistors. [196]

8.0 Proton Displacement Damage Displacement damage is a known problem in the military world or when parts are near a nuclear reactor. On the space side there are similar problems with protons. From a

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physicist view point, neutrons and protons are considered to have similar nuclear interaction properties, because they both belong to the baryon nucleon family of elementary particles. Protons, as distinguished from neutrons, can ionize along its path and deposit charge besides causing displacement damage. Note again that the ionization by a proton is still less than the ionization contributed by a heavy ion. This is described in the Hardening Against Proton section. It has been shown that the clusters of damage created by a 50 keV recoiling silicon atom from elastic scattering of high energy protons can be on the order of 0.1 x 0.1 micron square in cross section. With future COTS technologies, feature sizes of 0.1 x 0.1 micron square are achievable. This raises the concern of how vulnerable future electronics will be to damage from incoming particles. 8.1 Gain Degradation In bipolar technologies, as mentioned earlier, damage in the base region will create recombination or detrapping sites that will lower the gain of a transistor as shown by the data on the left in Fig. 28. •

Bipolar technologies with base regions with fT < 10 MHz are most susceptible Hardening solution: Account • 1/hfefinal - 1/hfeinitial = kΦprotron for degradation in hfe gain hfe Degradation from with adequate radiation 140 Displacement Damage design margin, RDM 46 mA, hfe = 136 hfe

design level

pre-irradiation hfe 14.0/div

100 46 mA, hfe = 52 50

post-irradiation 0

10-11

RDM = 2X

0 9 1010 1011 1012 1013 10-1 10 proton fluence, Φ (p+/cm2)

IC [A], decade/div.

Figure 29. Total Dose Proton Displacement Damage.

Bipolar transistors are sensitive to displacement damage with protons from the trapped radiation belt or from solar flares, especially those with large base depths with fT less than 10 MHz. (fT is defined as the frequency of a bipolar transistor at which the gain is unity.) If dose ionization can be separated out, the displacement degradation in the DC gain of a bipolar transistor can be characterized by the Messenger-Spratt equation: 1/Hfefinal 1/Hfeirrad = kφ, where k is the proton damage constant and φ is the proton fluence. The DC gain, Hfe, is defined as the ratio of IC over IB.

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Similar to neutron damage, the gain of a bipolar transistor will degrade as a function of IC as shown in the left set of measurements. At 46 mA, the initial DC gain was 136. After a given fluence, the DC gain has degraded to 52. If one were to plot the degradation of Hfe where the gain is highest with IC as a function of proton fluence, one obtains the curve 11 2 shown on the right. Initially, Hfe decreases slowly at fluences less than 10 protons/cm . 13 2 Above this the Hfe drops rapidly. Above 10 protons/cm the Hfe does not appear to decrease rapidly, but approaches values between 1-4. In finding a hardening solution, an engineer needs to account for the degradation in Hfe. To allow for robustness in a circuit design or some radiation design margin (RDM), a designer needs to include additional degradation other than the gain at the operational limit. The margin can be applied to the circuit or to the radiation dose. For the radiation dose, RDM applies to the radiation fluence level, whereas for a circuit performance a design margin (DM) applies to the gain of the device. Also note that at low IC currents, the DC gain degrades very rapidly. Therefore, for robustness in a design, a designer should try to avoid designing at low IC currents. The IC current at the maximum DC gain is generally recommended. From a HA point of view, any degradation in the Hfe gain of a bipolar transistor can impact the original RDM or DM. Likewise, similar degradation in the voltage threshold of a MOS transistor can affect the original DM. In terms of affecting the ionizing dose requirement, this would impact the RDM. 9.0 Displacement Damage in Other Technologies and System Solutions In electro-optical devices it was observed by Johnson et al. that damage mainly in the LED could result in degradation to the current drive that supports the operation of the device. With inadequate current drive capability designed in, again, not using radiation degraded parameters, failure can occur [98, 99, 100, 103] to the system. • Low-frequency opto-couplers are sensitive to CTR (Current Transfer Ratio) degradation • Solution: Design for adequate LED drive (i.e., >1 mA) and design for receiver gain loss

Vcc 3

Output, 2

npn

100k ohm

Input 5

7

GND

300 ohm

Current Transfer Ratio (Ic(5V)/Iforward)

4N49 Opto-coupler CTR Degradation with Proton Energies

193, 45-MeV protons

1 beam normal incidence to chip

10

192.4 MeV protons 45 MeV protons 0

10

-1

10

-2

10

-3

10

-4

10

109

Preirradiation

1010 1011 Proton Fluence [protons/cm2]

Figure 30. Electro-Optical Device Degradation.

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1012

In Fig. 30 we show a typical electro-optical circuit with a 300 Ω load in series with the LED (Light Emitting Diode). The npn transistor is connected with a 100 kΩ load. On the right is a plot of the current transfer ratio (CTR) defined as the npn transistor collector current divided by the LED current in the forward biased mode. The CTR as a function of 11 2 proton fluence is shown on the right in Fig. 30. As we approach 10 protons /cm , the CTR begins to roll off rapidly for a given LED drive current. The photoionization current collected by the transistor is dropping due to the inefficiency of the LED to generate adequate light. Higher drive currents on the LED will provide more light emission, hence hardening the circuit design. Note also that by going from 192 to 45 MeV protons, slightly more displacement damage is observed. Hence, a designer needs to use degraded CTR data at low proton energies. Several LED drive currents should be measured in order to allow a designer to select the best optimal design performance for a mission. In addition to bipolar discrete transistors that are sensitive to displacement damage, other semiconductor devices such as analog or linear devices show similar effects [101] as described in Fig. 31. These include operational amplifiers, linear regulators, voltage references, comparators, Analog to Digital (A/D) converters and Digital to Analog (D/A) converters, and pulse width modulators. These technologies have similar effects in their bipolar transistors as shown in Fig. 29. For example, an operational amplifier will exhibit degradation with the open-loop gain. A voltage reference may degrade in the precision to output the proper voltage. A/D and D/A converters may lose accuracy by starting in the least significant bit (LSB) first due to offset, bias and reference degradation and then extending to higher bits. In addition to linear devices, as already mentioned, there are electro-optical devices that also exhibit displacement damage as described above. These include optocouplers, opto-isolators and light emitting diodes (LEDs) [102]. Other devices that can degrade from displacement damage are diodes. At extremely high fluences, diodes can become ohmic.

Displacement Damage (Non-Ionizing Energy Loss) in linear devices • Op amp, linear regulator, voltage reference, comparators, A/D, D/A, pulse width modulator Displacement Damage (NIEL) in electro-optical devices • Optocouplers, optoisolators, LEDs, CCDs Displacement Damage (NIEL) in discretes • Diodes, bipolar transistors Single event upset (protons & neutrons) • Linears, sequential logic devices, i.e., SRAMs, FFs, registers in CPUs, DRAMs, SDRAMs Figure 31. Proton Effects on Other Electronic Devices.

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A recent issue in bipolar devices is the effect of single event upsets or transients created by 10 the neutron reaction with B, an isotope of boron. Besides proton SEUs, the by-products 10 of the n- B reaction can occur for devices with LETs (Linear Energy Transfer) less than 3 2 MeV-cm /mg. This SEU effect is similar to the proton interaction with a silicon nucleus as described in Section 5.0. Boron is commonly found in the passivation layer over an integrated circuit chip called BPSG (borophosphate silicate glass), in the source-drain junctions of p channel devices, in the active region of n channel transistors, in the base region of an npn and the emitter and collector of a pnp. The nuclear capture cross section is high at low neutron energies. Neutrons are generated at low altitudes by the interaction of the galactic cosmic environment with the oxygen and nitrogen in the atmosphere. Neutron SEUs have been observed by various ground and aircraft systems. Very similar to the p+ - Si reaction, neutrons behave in a similar way, in the sense that the neutron byproducts are the ions that will cause the single event effect. In order to mitigate the neutron SEE problem, parts with a LET greater than 14 MeV-cm2/mg should be selected. Another solution that some semiconductor foundries are doing is to eliminate 10B from their ion implantation or passivation process. There are a number of hardening solutions to protons as listed in Fig. 32. For BJT devices, as mentioned earlier, one needs to account for gain degradation that will occur when a device is exposed to very high fluences, such as from an ALS flare, in a MEO through the proton enriched trapped radiation belt or a mission to the intense trapped radiation belt of Jupiter. To harden against displacement damage, several gain stage design can be considered in the circuitry. Time dependent annealing can be considered. • Commercial manufacturers are working with the submicron feature sizes, i.e., size of a defect cascade • Silicon recoils from proton inelastic scattering can creat a cluster of damages within 0.1 x 0.1 µm2 area

Solutions • Account for gain degradation in BJTs • Use several gain stages in circuitry • Account for time dependent annealing • Account for CTR loss in optical couplers • Filter transient glitches in linears • Circumvent and refresh memory • Use CMOS, power MOSFETs, GaAs, Si-Ge HEMT Figure 32. Proton Hardening Solutions.

In designing electro-optical devices, such as optical couplers, a designer needs to account 12 for the degradation in the CTR (current transfer ratio). Otherwise, after a fluence of 10 2 protons/cm , there may not be enough drive to switch the transistors. For linear devices, I-33

such as op amps, voltage regulators and references, transient glitches can be removed by adding filter capacitors on the signal lines. Software codes can be used to check for upsets or glitches and sequential logic devices should be refreshed to correct for any corrupted data that is inadvertently loaded into memory. Hardened technologies to displacement damage include CMOS, power MOSFETs, GaAs and silicon germanium strained HEMT (high electron mobility transistor) devices. These technologies are extremely hard to proton damage and to total ionizing dose effects. 10.0 Comparison of Commercial versus Hardened Technologies Fig. 33 is a comparison of commercial versus hardened technologies that are available to a designer. Considering the application of the program, one can choose commercial parts, but one has to be aware of their radiation sensitivity. Care must be taken to prevent any catastrophic failures when these are implemented in a system. The SEUs and dose ionization sensitivity levels of these parts are high. For example, the TID of COTS can be as low as a few krad(Si). The bipolar TID hardness is based on Mil Std. 1019.4 radiation testing at 50-300 rad(Si)/s. However, with ELDRS effect, low dose rate testing is needed because some linears can fail at low doses. Some of the hardened technologies include CMOS/epi, CMOS/SOS and CMOS/SOI processes. These parts have total ionizing dose hardness levels as high as a Mrad(Si) and -10 single event upset rates less than 10 upsets/bit-day in a 90% worst case galactic cosmic environment. • Commercial technologies - sensitive to SEUs, low total dose hardness and possible lot-to-lot variation – Commercial bipolar is SEU/SET sensitive and soft to total dose at very low dose rates. It is also sensitive to lattice displacement damage from protons. • Hardened CMOS is immune to SEUs and has a high total dose hardness Technology

Single Event (upsets/bit-day)

CMOS/epi commercial

10-5 – 10-8

Bipolar/bulk commercial

10-6

CMOS/epi, SOS, SOI (hardened)

Ionizing dose* (rad(Si)) 5k – 60k ~100k (>10 rad/s) 30 – 50k (10% of their initial energy, thereby calling into question the validity of constant LET, and (b) what fraction actually lost all of their energy (i.e., came to a stop within the sensor). The results are listed in Table 3-5. These results demonstrate that the constant LET approximation is tolerable but far from perfect for simulating an actual environment. Table 6. Constant LET approximation.

Silicon Silicon Silicon HgCdTe HgCdTe HgCdTe

Shielding (mils Al) 100 200 500 100 200 500

Fraction Slowed (%) 1.2 2.3 3.3 2.0 4.0 5.3

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Fraction Stopped (%) 0.25 0.58 0.86 0.40 0.8 1.28

Two obvious post-processing uses of these simulated images are to: (a) overlay the data with optical imagery similar to what might be acquired on-orbit in order to investigate the significance of the proton-induced artifacts on image quality, and (b) evaluate false-alarm suppression algorithms in star trackers or missile detection payloads.

Figure 49. Collected charge per pixel for a 15 micron pixel pitch silicon focal plane in a mid-latitude environment with 100 mils Al shielding and a 25 msec integration time. Histogram bins have 500 electron increments; last bin contains all pixels with charge exceeding 50,000 electrons.

Figure 50. Collected charge per pixel for a 50 micron pixel HgCdTe focal plane in a mid-latitude environment. Histogram bins have 2000 electron increments; last bin contains all pixels with charge exceeding 400,000 electrons.

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5.3

Updates to Proton Effects Modeling and Recent Transient Simulations

The simulation described in these notes is a useful tool for quantifying the detailed effects of proton transients on visible and infrared focal planes used in advanced spaced-based electrooptical sensors. The tool is not static and model upgrades are continuously implemented. As previously mentioned, the simulation can now be applied to photodetectors other than the original CCD focal-plane pixels. A simulation tool upgrade, which has been implemented, is described below and planned improvements are listed. A previous version of the simulation tool suffered from a deficiency in the proton-inducedtransient charge generation and collection model. The model deficiency manifested itself in proton events that produced rare, anomalous, unphysical results that originated from a divergence in the model. The anomalous results were cast out based on comparisons with experimental and empirical data. The divergence originates from the charge collection model described previously (Eq. (5-7). A new physical charge generation and collection model that does not exhibit the unphysical results of the previous model has been implemented. The proton-induced ionization track is now modeled as a spatially extended solid-state plasma filament of electron-hole pairs. Several additional improvements are planned that are aimed at increasing the fidelity of the predicted results. These include: (a) accounting for the loss in proton energy in the overlying substrate of an infrared photodiode focal plane, since this substrate can modify significantly the energy spectrum of incident protons for glancing angle impacts; (b) accounting for Bragg peak effects in the charge collection model when the incident proton energy is appropriately low (here dE/dx varies rapidly with distance); (c) accounting for small-angle scattering effects, which are more likely to be significant for infrared focal planes; (d) improving the diffusion model so that it properly reflects lifetimes and diffusion lengths typically encountered in infrared arrays; and (e) investigating an analogous simulation based on energetic electrons and x-rays. In addition, we plan to compare the results of this simulation with on-orbit EO sensor data whenever such data are available. Several transient simulations have been performed recently for infrared detector arrays [127][130]. Those simulations have elements in common with the modeling work described here in Sections 5.1 and 5.2 and provide useful extensions. 5.4

Impact of Electrons, Shielding, and Bremsstrahlung

Electrons trapped in the earth's magnetic field can penetrate spacecraft shielding to induce transient charge in focal-plane arrays. Because the electron mass is much smaller than the proton mass, the transport of electrons through shielding and focal-plane detectors differs significantly from that of protons. Most geomagnetically trapped electrons can be stopped with 0.5 in. of aluminum shielding; however, electrons lose a large portion of their energy through the production of secondary x-radiation (bremsstrahlung), which is very penetrating and has transport characteristics [131] completely different from the primary radiation. Figure 51 shows examples of the secondary IV-77

x-rays on spectra produced from a given input electron spectrum. On a per-particle basis, the x-ray flux is lower than the input electron flux by two to three orders of magnitude. This ratio can be made even smaller by the use of compound shielding consisting of layers of low-Z and high-Z materials. Visible and infrared focal-plane detector arrays typically used in space applications have a relatively small cross section for detecting x-rays. Figure 52 shows the attenuation lengths of x-rays as a function of x-ray energy for various detector materials. An x-ray having an absorption length of 1 cm, as might be appropriate for an 80-keV electron traversing silicon, has only a 0.1% probability of being absorbed in a detector that is 10- µ m thick. Just as with protons, focal-plane detector elements are efficient detectors of primary electron radiation. Accurate estimation of the transient effects of electrons and electron-induced x-rays on focal plane signals requires that detailed radiation transport and interaction calculations be performed. (A semi-empirical model to estimate focal-plane response to a gamma flux is discussed in Section 4.3.1.)

Figure 51. Differential x-ray fluxes produced by electron transport through solid Al spheres.

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Figure 52. Absorption length of x-rays as a function of x-ray energy in typical detector materials.

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6.0

Radiation Effects on MOS Readout Integrated Circuits

6.1

Background Information

Multiplexer or readout integrated circuits (ROICs) for large visible and infrared arrays for staring (i.e., non-scanning) applications, shown in block diagram form in Figure 19, have experienced a significant increase in functional complexity over the last two decades. Silicon technology feature sizes used to implement mixed-signal analog circuits have been reduced, which has allowed increased circuit layout densities to be achieved. Figure 53 shows the complexity increase versus calendar year for infrared focal-plane arrays [132]. The complexity level of arrays for infrared imaging applications is expected to increase significantly during this decade (i.e., MW MCT in Figure 53). Figure 20 shows a cross– sectional schematic of the indium-bump-bonded construction used for hybrid focal-plane arrays, which consist of an infrared detector array mated to a silicon CMOS array containing the pixel readout electronics (i.e., ROIC).

Number of detectors per chip

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Typical chip size 1024 x 1024

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Figure 53. Infrared focal-plane array size trends

With the advent of micron feature-sized CMOS transistors, ROIC designers were able to reduce the area occupied by a single pixel circuit to less than 25 µm2 for visible imagers. For the visible imager case, one of the simplest pixel circuits containing only three transistors, namely the source follower per detector (SFD) [133] circuit shown in Figure 54, can be designed to be compatible with a detector pixel pitch as small as a few microns. This socalled “3T” SFD pixel circuit, used primarily for visible imager applications, electrically isolates the detector integration node from stray capacitances allowing for realization of IV-80

integration capacitances of 5-10 fF or less per pixel. A voltage ramp is generated on the integration node as the reverse-biased detector diode current is integrated on this capacitance, CI, and subsequently is sensed at the gate of Q2, the source-follower transistor input. At the “3T” SFD pixel output, the analog voltage sample is read-out through the series transistor switch Q3 to the column interconnect trace when selected by the x-y addressing scheme as shown in Figure 54. SFD pixel simulation results showing these integration node ramp waveforms are included in Section 6.2.

Figure 54. Schematic of source follower detector (SFD) pixel electronics

For submicron-feature-sized CMOS circuits, very small values of pixel output capacitance can be achieved along with high values of photoconversion gain GC(ω), defined as the ratio of the pixel output voltage to photocurrent [134]: GC(ω) = q GV(ω)/C where q is the electronic charge, GV(ω) is the electronic circuit gain, and C is the pixel integration node capacitance. For a pixel integration capacitance in the range of 5-10 fF, the ideal conversion gain can be as large as 16-32 µV/e-, although losses in the pixel readout source follower, due to a less than unity gain characteristic and nonlinearity over the full dynamic range, may reduce pixel conversion gain to 10-20 µV/e- or less. A single transistor current sink Q4, as shown in Figure 54, is used to terminate each column and to enhance column settling time by selection of optimum current as required by the IV-81

frame rate. During pixel readout, a single pixel source follower along a column supplies current to the column sink and the column voltage settles to a source voltage corresponding to the analog sample at the source follower input, which is proportional to the photocurrent integrated on capacitance CI. Typical active power dissipation for visible imager designs can be as low as 30-50 µW per column for a typical array size of 4096 x 4096 pixels operating at 30 frames per second, and can be further reduced by power-gating the column current sink transistor consistent with the imager global timing constraints. An example of 3T pixel layouts for visible image arrays is shown in Figure 55, with the various photomask layers shown. [162] Indicated in the figure are the three NMOS transistors corresponding to Q1, Q2, and Q3 in Figure 54. The pixel dimensions in this layout are 5 µm x 5 µm, with the largest single area occupied by the photodiode for this APS pixel layout. The ratio of photodiode area to total pixel area, known as pixel “fill factor”, is one element which determines the detector effective quantum efficiency for mid-band spectral response in the visible band, typically 0.4 µm to 0.9 µm. As shown in Figure 55(c), thirdlevel interconnection thin-film metal is deposited over the three transistors as a light shield to eliminate spurious photo-response from these devices. A relatively high fill factor is achieved due to the circuit configuration in which transistors Q1 and Q2 share a common drain and Q2 and Q3 share a common source-drain region. Furthermore, these pixel cell layouts generally merge the source implanted region of Q1 with the tailored photodiode cathode region in order to reduce the total implanted area to optimize dark current and integration node capacitance as well as conversion gain. In addition, the layouts shown in this figure can be geometrically arrayed within the same pitch as the pixel dimension due to pixel layout design optimization. Note that for commercial pixel designs, NMOS device technology is favored due in part to the higher surface mobility of electrons as compared to holes, which allows transistors Q2 and Q3 to be reduced to approximately half the area required for a PMOS implementation operated at the same frame rate. More importantly, for commercial CMOS imager applications, NMOS-based pixels contain a photodiode consisting of an n-well implanted into the p-type substrate with typical thickness of several millimeters, resulting in acceptable spectral response. For a PMOS-based pixel photodiode, the corresponding heavily doped ptype implant into an n-well results in a much thinner collection volume, and corresponding loss of spectral response in the red and near infrared. [162]

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(a)

(b)

(c) Figure 55. Scaled layout drawings of 3T pixel.

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6.2

Total Dose Hardening of Visible Imager ROIC Designs

The SFD circuit shown in Figure 54 can be implemented with all NMOS or all PMOS devices, or a combination of device polarities. However, an important design consideration is the production by total ionizing dose of trapped positive charge at or near the silicon-silicon dioxide interface in gate regions of the “intrinsic” transistor as well as in the field oxide or, for many deep-submicron processes, LOCOS (i.e., local oxidation of silicon) regions surrounding the intrinsic transistor. This positive oxide charge shifts the NMOS and PMOS transistor turn-on threshold voltages such that NMOS transistors shift from enhancement mode (i.e., normally “off” state) to depletion mode (i.e., normally “on” state). The effect for PMOS devices is to continue to remain operating in the enhancement mode due to an opposite polarity shift in threshold voltage. For small-feature-sized devices, for which the gate oxide thickness is below 10 nm, these threshold shifts are small and on the order of a few percent of the pixel voltage supply. Measured NMOS device flatband voltage shift, which is the primary component in threshold voltage shift for both device types, versus gate oxide layer thickness is shown in Figure 16. That figure clearly demonstrates one of the potential benefits for the selection of smaller feature-size technology for many electronic circuit applications operating in a total-dose environment. An important aspect to understanding total-dose effects at the transistor level involves the NMOS device physical construction within the region in which the polysilicon gate layer is terminated over LOCOS field-oxide regions (i.e., ~500 nm), which is much thicker than the gate oxide (i.e., 5 nm) over the conducting channel. Post-total-dose measurements on transistors of various length and width dimensions and differing layout configurations demonstrate that most of the total-dose-induced NMOS transistor drain current at zero gate bias can be attributed to a drain-to-source leakage at the field oxide-silicon interface where the gate polysilicon is terminated over field oxide, as shown in Figure 56. This is due in part to the fact that holes trapped in LOCOS field-oxide regions prevent the device from full turnoff at zero gate bias. This is the result of trapped holes attracting electrons (i.e., minority carriers in the p-type silicon) to the surface where they can form a conductive channel between transistor source and drain. This effect (“channeling”) occurs at the surface of the lightly doped p-type bulk silicon regions surrounding NMOS transistors in many commercial foundry processes. Note that for PMOS transistors, the trapped holes within the corresponding gate regions terminated over field oxide results in the accumulation of electrons (majority carriers in the n-type bulk silicon) thereby preventing channeling due to the formation of a surface inversion layer of holes to conduct current from source to drain. These trends for total-dose transistor parameter shifts for PMOS and NMOS devices operating at gate voltages less than the threshold voltage (i.e., subthreshold) are illustrated in Figure 57. [135] Measured increased leakage for conventional NMOS transistors is shown in Figure 17 and Figure 58(a) for conventional device layouts. [135] Significant decreases in post-total-dose leakage for enclosed NMOS transistors are shown in Figure 18 and Figure 58(b). [135] The so-called enclosed device layout, as shown in the inset drawing of Figure 58(b), eliminates the polysilicon gate termination over field oxide and the corresponding drain-to-source leakage path due to surface inversion caused by holes trapped in the field oxide, as described previously. [136]

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Figure 56. Idealized Drawing of Oxide-Isolated NMOS Transistor Showing Nominal and Edge Leakage Current Paths

Figure 57. Effects of Total-dose Generated Oxide Traps (ot) and Interface Traps (it) on Transistor Drain Current vs Gate Voltage

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(a)

(b) Figure 58. Drain Current vs Gate Voltage For Total Integrated Dose from 50 krad(Si) to 400 krad(Si) for 0.18-µm feature-sized NMOS Transistors with a) Conventional Layout, and b) Enclosed Layout

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As applied to the SFD circuit shown in Figure 54, total-dose-induced leakage current for the reset transistor, Q1, can add significantly to photodiode dark current at the integration node, resulting in an unacceptable bias voltage shift as well as loss of dynamic range, which is detrimental particularly for non-astronomy imager staring-array applications that typically operate at integration times of tens of milliseconds. As an example, for a typical imager operating at a frame rate of 30 frames per second (fps) with interface to a 12-bit analog-todigital converter with full scale of 1.024 V, the total leakage at a 10 fF integration node is presumed to be dominated in a total-dose environment by the reset transistor operating in very weak inversion (i.e., subthreshold). A concern for visible imagers is that this leakage current, which would otherwise benefit by cryogenic cooling for infrared applications, cannot exceed 42 aA (i.e., 42 x 10-18 amp) in order to avoid adding an offset to the pixel sample voltage of more than ½ LSB for operation at 30 frames per second. The selective use of enclosed NMOS transistors can be an alternative solution for total-dose hardening to avoid the effects of low-level leakage paths provided that photodiode area and the corresponding fill factor can be traded off with the added layout area occupied by the enclosed device. Efforts to achieve hardness-by-design using enclosed-transistor layouts for imaging arrays have been reported for moderately large feature-size devices. [137] However, in general the increased transistor layout area and corresponding increased capacitances appear to be significant disadvantages to their use for small pixel designs. Figure 58(a) shows pre- and post-total-dose drain current versus gate voltage data measured for conventional NMOS devices fabricated by a 0.18 µm commercial wafer fabrication line. [135] The values of drain current at zero gate voltage measured after 50 krad(Si) and 100 krad(Si) are approximately 40 pA and 100 nA, respectively. Typical values of post-total-dose drain current versus gate voltage for commercial NMOS transistors are incorporated into a large-signal circuit simulation of the SFD operating at an active 30 mS integration period. For these simulation results (Figures 59 and 60), the post-total-dose pixel integration node waveform is significantly degraded as compared to the pre-irradiation case due primarily due to the increase in subthreshold current of Q1 (Figure 54) resulting in a pixel waveform offset voltage. In these two figures, the dark current was set to zero, and values of photocurrent were increased from 200 fA to 1 pA in steps of 200 fA. The total-dose-induced transistor leakage model in the Figures 60 and 61 simulations consisted of a nonlinear leakage current source connected in parallel with Q1, with nominal leakage current at low drain-source voltage of approximately 10 fA and increasing (with decreasing integration node voltage) to beyond several hundreds of femtoamps. Figure 61 contains estimates of the effects of total-dose-induced reset NMOS leakage in degrading pixel conversion gain. In this case, the conversion gain is derived from the simulation results over the first 25 mS of a 30-mS integration period. The estimated conversion gain, specified in the conventional units of µV/e-, is shown to approach zero at a “half-full” well integration node signal of 50K photoelectrons at approximately 50 krad(Si). This mode of operation would seriously limit the imager application for ionizing dose levels less than 50 krad(Si) due to the fact that the conversion gain will continue to degrade with total dose.

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Figure 59. Pre-Total-Dose Pixel Integration Node Waveforms for Various Photodiode Currents for SFD Pixel Implemented with Commercial NMOS

Figure 60. Post-Total-Dose Pixel Integration Node Waveforms for Various Photodiode Currents for SFD

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Figure 61. Input-to-Output Conversion Gain versus Photodiode Current for SFD Pixel Implemented with Commercial NMOS (T = 220 K)

Another approach that may be used to overcome the problems of NMOS threshold voltage shift with TID utilizes a reverse bias applied to the NMOS transistor p-well region in order to increase the bulk silicon charge component of the device operating turn-on threshold voltage, frequently referred to as threshold voltage body effect (i.e., (1/COX) x (qεSINAVSB)1/2), which occurs in the case of NMOS devices when the source-to-bulk silicon voltage (VSB) becomes positive. [138] However, this approach has the disadvantage of limiting the analog voltage dynamic range for a transistor used as a linear series switch, which is characteristic of many sample-and-hold and analog multiplexer applications within imaging arrays. Because many available CMOS foundries utilize an n-well process with a common p-type epitaxial layer to contain all NMOS transistors, the effect of a substrate reverse bias would be to increase the threshold voltage via the body effect for each NMOS transistor irrespective of its application, which may be unacceptable for the design of peripheral circuits integrated within the readout chip (ROIC). [138] These ROIC imager support circuits include linear amplifiers and digital logic circuits used for on-chip multiplexing, array self-scanning, ADCs, and output buffer circuits. A design modification to selectively replace low-leakage analog switch functions with PMOS transistors can also be incorporated into SFD circuit designs. For certain applications, substituting PMOS transistors in place of NMOS pixel reset transistors avoids the detrimental effects of increased leakage with total dose. This benefit also applies when a PMOS IV-89

transistor is used in place of a source follower or row select switch transistor (Q2 and Q3 in Figure 54), and overcomes another problem associated with total-dose-induced leakage from the large number of deselected row transistors within the image array, as discussed subsequently. It should be pointed out that one potential disadvantage of using PMOS transistors for these applications, in which the transistor source terminal is operated near the mid-point of the pixel output voltage range, is that the pixel linear dynamic range can be reduced more severely than for an NMOS transistor. This is due to the fact that the effect of total-dose threshold shift for a PMOS transistor is additive to the transistor body effect, whereas for NMOS transistor the body effect is reduced by the total-dose-induced threshold voltage shift. In addition, for n-well CMOS processes, the PMOS body effect can be larger due to the higher surface concentration of the n-well as compared to the p-type substrate surface concentration used for NMOS devices. Another potential benefit of a PMOS device used as the integration-node reset transistor Q1 (Figure 54) is that it allows the full reset voltage derived from a precision voltage reference, VRESET, to be transferred through the PMOS drain to the integration node. Note that if an NMOS transistor were substituted in the same circuit with the same set of nominal voltages used (i.e., typically 3.3 volts), the integration-node reset voltage transferred from the NMOS source would at best approach a value of VRESET – VTO, where VTO is the effective turn-on threshold voltage of the NMOS reset transistor, including the body effect term that adds to the pixel output offset voltage distribution. This distribution will generally be the result of imperfect matching of NMOS reset transistor parameters associated with random spatial distribution of impurities implanted into the bulk silicon as well as gate oxide charges. It is expected that total-dose-induced threshold voltage shifts will further complicate the process of pixel offset voltage subtraction during image post-processing. The distribution of this integration node offset voltage, described above, is one example of several terms referred to as fixed pattern noise or FPN. Active FPN cancellation can be achieved with the use of an analog difference circuit known as a correlated double sampler (CDS) [139], which is used while reading the analog pixel samples. The CDS first stores an analog sample of the current pixel during readout immediately after integration node reset, and subtracts this analog level from the integrated signal. For this implementation of FPN subtraction, CDS circuitry is included within the readout circuitry for each column within the array. Figure 62 shows the reported performance of FPN reduction circuitry achieving removal of FPN to less than 0.04% of the full pixel signal with no increase in the residual for operation up to 10.2 Mrad (Si). [140] Figure 63 contains a comparison of a non-hardened array with the measured increase in total-dose-induced dark current for a non-hardened and two total-dose hardened 512 x 512 arrays up to 10.2 Mrad (Si), showing negligible dark current increase up to approximately 200 krad(Si). A dose rate of 2.7 rad(Si)/sec was used to irradiate the samples using a Cobalt-60 source. [140] The pixel array can be degraded by total-dose-induced column offset voltage for the 3T pixel cell implemented with commercial NMOS technology. Figure 64 shows a simplified model of the origin of the leakage path at the column of an array, wherein the effect of the totaldose-induced leakage components of the deselected pixels within a given column contribute to a spurious current that serves to shift the column voltage positive during pixel read. To

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estimate the magnitude of the leakages, assume that for a 4096 x 4096 array the columns are subdivided in length to contain 2048 pixels per active subcolumn. During an individual pixel read operation, the total-dose-induced leakage current from the remaining deselected 2047 pixel read switches can contribute to a combined current that approaches or exceeds the nominal value of the drain current of Q4 (Figure 54), which has been specified for this “point” design as 10 µA. Compare that design value of current with the sum of the currents for the deselected transistors, which is shown in Figure 58(a) to be 40 pA per transistor at zero gate voltage. [135] Note that this condition is consistent with the worst-case column voltage for a full-well pixel voltage.

Figure 62. Residual Fixed Pattern Noise versus Total Ionizing Dose

For this example, the total column leakage current is estimated to be 82 nA at 50 krad(Si) and in excess of 200 µA at 100 krad(Si). As the leakage current increases between these two limits, typically a current approaching the nominal current sink design value of 10 µA, the pixel sample voltage will be significantly corrupted by this leakage induced offset voltage. Figure 65 contains a simulation result showing that the pixel sample voltage contains approximately 100 mV offset (i.e., 400 counts for 12 bit ADC resolution) at the total-dose level corresponding to column leakage values between 8 µA and 9 µA, well below a totaldose upper limit of 100 krad(Si). The simulation results show that as the column leakage increases beyond a value corresponding to the current sink design point of 10 µA, the column voltage increases the drain-to-source voltage of Q4 to accommodate the added current. Design mitigation to overcome this total-dose effect includes the use of enclosed NMOS transistors within the 3T pixel layout for implementing the source-follower and row-select functions (Q3 and Q4 in Figure 54). Differential voltage sensing at the column line may also

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be applicable for image arrays, which include a light-shielded (i.e., unilluminated) reference column.

Figure 63. SFD Pixel Dark Current Increase with Total Ionizing Dose

Figure 64. Simplified Diagram Showing Imager Selected and Deselected Pixel Currents

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Figure 65. Predicted Pixel Sample Offset Voltage versus Deselected Pixel Current in Active Column

6.3

Approaches to ROIC Design for Infrared Imagers

For infrared imagers, the pixel area is generally relaxed to 20 µm x 20 µm or larger based on tailoring to the infrared optical blur circle. The larger pixel pitch (compared to a visible pixel pitch of less than 10 µm) allows increased functional complexity within the pixel electronics and potentially the selective use of enclosed NMOS transistor layouts to avoid increased total-dose-induced leakage. A frequently used infrared-imager pixel circuit, commonly referred to as a capacitance transimpedance amplifier (CTIA) [134], is shown in Figure 66 and consists of a reset integrator implemented with an active linear CMOS transimpedance amplifier and smallvalued feedback capacitor used for photocurrent integration. A sample-and-hold circuit at the transimpedance amplifier output is used to provide a snapshot of the pixel analog signal at the end of the integration period such that one entire image frame can be transferred to the output of the imager during the integration time of the next frame. As shown in Figure 66, the combination of an NMOS source follower transistor and an NMOS series analog switch transistor at the pixel output interface is typically used to transfer the sampled pixel voltage to the output column bus line when the row address in enabled. A PMOS source-follower implementation can also be used to achieve total-dose hardening without the use of enclosed transistor layouts to avoid the effects of increased leakage from deselected row transistors within the image array, as discussed in Section 6.2.

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Figure 66. Schematic of CTIA Pixel Electronics

For the CTIA, pixel conversion gain at the integration node, GC(ω), is given by qGV(ω)/C, as noted in Section 6.1, where C is typically dominated by the integrator feedback capacitor, C1. in Figure 66. Practical values of pixel conversion gain can exceed 10-20 µV/e- because the active CMOS feedback amplifier input behaves as a virtual ground for photodetector current, thereby greatly reducing the effects of stray input capacitance in parallel with the detector, a feature that is lacking in the SFD pixel configuration. This feature is an advantage for hybridized infrared arrays, which are illustrated in Figure 20, since the added stray capacitance of the indium bump interconnection is largely eliminated. The active CMOS amplifier also provides improved output dynamic range for the CTIA pixel, as compared to the SFD pixel circuit. However, while the SFD circuit dissipates power only when accessed during the pixel read cycle, the CTIA active amplifier dissipates continuous power. However this can be as low as 75 nW, as demonstrated for a low-power design. Testing of a 16 x 16 readout array designed with enclosed NMOS transistors showed minimal performance shifts for total-dose exposure up to 1 Mrad(Si). [141]

6.4 Infrared ROIC Circuit Design for Total-Dose Hardness For the CTIA pixel electronics, the formation of leakage paths, which degrade the analog signal voltage stored on small-valued capacitor, remains the most significant problem for total-dose hardening. As discussed previously, with the use of submicron feature-sized CMOS for implementation of imagers, a typical value of CTIA feedback capacitor C1 in IV-94

Figure 66 is 10 fF or less. As discussed previously for staring arrays operated at rates of 10 fps to 30 fps, total-dose-induced leakage currents on the order of 10 aA to 40 aA can result in significant degradation of the integrated pixel signal. In Figure 66, the CTIA integration capacitor reset switch Q3 represents a potential leakage current path, which would result in a loss of integrated signal. However, in most CTIA applications, this reset transistor can be implemented using a small-geometry PMOS device. When the gate of this device is pulsed to a voltage sufficiently low to turn-on the PMOS channel, the integration capacitor can be discharged. Thereafter, its gate voltage can be returned to a positive voltage level, forcing the device into cutoff. For this device, total-dose exposure will result in a transistor threshold shift toward enhancement mode, thereby eliminating increases in subthreshold current as discussed in Section 6.2. The CTIA pixel sample-and-hold electronics presented in Figure 66 shows a conceptual design including a two transistor NMOS (Q4) and PMOS (Q5) transmission gate analog switch used to implement the sample-and-hold circuit series switch function and an NMOS shunt switch (Q6) used to reset the hold capacitor. For the sampler switch, an NMOS transistor (Q4) is used to extend the sampled CTIA output voltage dynamic range. This is because the use of a PMOS switch alone would limit the sampled voltage to a value greater than about twice the absolute value of its threshold voltage. However, many practical CTIA designs eliminate the NMOS transistor (Q4) for this analog switch, since for low-level sensing applications the voltage level transferred is sufficiently positive such that the PMOS transistor (Q5) does not limit or “clip” the signal. Similarly, NMOS transistor Q8 may be eliminated from the pixel series output switch, although the gate-source voltage of the source follower transistor Q7 will tend to reduce the dynamic range margin for signal levels approaching “full well”. Similarly, signal charge stored on the hold capacitor can be corrupted by total-dose-induced leakage of the hold capacitor reset transistor Q6. Post-total-dose leakage currents for either of these NMOS transistor applications should be limited to 10 aA to 40 aA for imager operation at 10 fps to 30 fps. For many infrared applications, the use of NMOS transistors utilizing design-hardening techniques, including enclosed or other tailored layout approaches, can potentially be used to achieve moderate levels of total-dose hardness for submicron-featuresize technology. These design-hardening layout techniques are used to mitigate the effects of field-oxide hole trapping and the significantly reduced thermal annealing rates at cryogenic temperatures, as is briefly described subsequently. Furthermore, for staring infrared array imagers operated at 100 K or below, the use of radiation-hardened foundry capabilities for ROIC development provides several advantages, including the use of nonenclosed NMOS transistor designs, which provide an arbitrary range of channel aspect ratios to select from during design optimization. For example, Figure 67 shows subthreshold drain current versus gate voltage for an enclosed NMOS transistor with 0.9-µm gate length and a total area approximately 4 times larger than a minimum channel length transistor. Pre- and post-total-dose measurements of subthreshold current for this transistor are shown in Figure 67. The measurement technique is as follows. The enclosed NMOS transistor was operated functionally as Q6 in Figure 66 as the hold

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capacitor reset switch. However, for this test structure, the gate of Q6 was available for external adjustment and was used to generate the data in Figure 67, which displays the subthreshold drain current versus the gate voltage for Q6. The drain current was derived from the discharge slope of the hold capacitor resulting from the subthreshold current of Q6. Hence, the data points in Figure 67 were generated from measured values of discharge slope over time periods up to 100 ms, with the discharge waveform captured by a digital processing oscilloscope using waveform averaging. The test samples measured were biased with a steady-state voltage of 2 volts applied to the gate of Q6 as a worst-case condition. [141] These results verify the performance of enclosed NMOS devices fabricated with a 0.5µm process in maintaining the pre-irradiation subthreshold current-voltage slope and in avoiding significant increases in subthreshold current for total-dose exposure up to 100 krad(Si) at ambient temperatures. This technique also provides a methodology for subthreshold current measurement in the fA range.

Figure 67. Subthreshold Current versus Gate Voltage for Enclosed NMOS Transistor

For operation at cryogenic temperatures, it might be assumed that a significant improvement in reducing total-dose-induced increases in subthreshold current would be associated with its exponential temperature dependence. [142] However, it is expected that this improvement only applies to enclosed NMOS transistors. For nonenclosed devices, the subthreshold current is dominated by the component due to electron channeling at the field-oxide interface under the polysilicon gate. However, another factor in mitigating the effect of total dose on subthreshold current is that, for a typical imager application, the hold-capacitor reset-transistor gate voltage would normally be pulsed at a duty cycle less than 0.1% during array operation. Consequently, for this mode of operation, which is typical of many imager capacitor reset functions, these IV-96

transistors would be expected to exhibit a reduced subthreshold current increase during operation than predicted by the measured data for the previously discussed enclosed NMOS device under continuous-voltage bias during exposure. [143] During the CTIA design optimization process, to reduce the effects of total-dose threshold shifts as well as those effects on transimpedance amplifier performance associated with operation at cryogenic temperatures, current-mirror biasing is frequently used. [144] This bias technique, commonly used to bias transistors for image array peripheral circuits such as low-power operational amplifiers used in the end-to-end signal chain, utilizes a reference voltage to produce a fixed current that is relatively independent of the device parameters, including the transistor threshold voltages. The reference voltage, which generates the current-mirror reference current, is distributed to each of the transimpedance amplifiers within the array, resulting in a common bias current for each amplifier. This technique has been successfully used to generate ROIC array bias currents as low as 25 nA per amplifier for a 0.5-µm-feature-sized 16 x16 pixel test array using enclosed NMOS devices operated at ambient temperature to 1 Mrad(Si). [141] Fortunately, current-mirror bias schemes are almost always used for pixel amplifiers in order to provide temperature compensation at cryogenic temperatures. In practice, for these circuits biased at subthreshold current levels, the drain-to-source current is dominated by diffusion current and is exponentially related to the ratio of applied gate voltage to the thermal voltage, kT/q. To first order, the primary total-dose effects are related to the interface trap effects on surface mobility. However, the design of pixel array peripheral circuits, including linear buffer amplifiers and comparators used to implement analog-to-digital converters, must consider the effects of transistor threshold voltage shifts with total-dose. For some ambient temperature designs in which a stable voltage reference is needed to establish a reference current independent of threshold voltage shifts, a substrate pnp transistor is used. Because the pnp emitter-base voltage trends with temperature and current can be modeled, stable current references can be designed. [144] With proper hardened-by-design layout applied to the pnp device design, this device can be made insensitive to total-dose-induced surface effects. As an example of various aspects of the use of enclosed NMOS transistor hardness-by-design techniques for a 16 x16 element CTIA test array, the pre-and post-total-dose results for measured dynamic range and linearity are presented. These results for measurements made at ambient temperatures are shown in Figures 68 and Figure 69, respectively. [141] The CTIA pixel amplifier current sink (Q2 in Figure 66) was operated at 25 nA during irradiation and subsequent testing. Photoconversion gain, dynamic range, and linearity remained nearly constant with exposure up to 1 Mrad(Si). Linearity was seen to degrade at 500 krad(Si), but this occurred beyond the designed maximum well-capacity design of 100 kiloelectrons. A Cobalt-60 source was used to irradiate the samples with power applied at a dose rate of approximately 72 rad(Si)/sec. Note that the readout array testing was accomplished with the use of 40 fF capacitors incorporated into the inputs of each of the 256 CTIA cells and used as photocurrent simulators when driven with a voltage ramp.[141] While these results for the test array were satisfactory, it should be noted that the enclosed transistors were each surrounded by a heavily doped p-type implanted guard ring to avoid

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any channeling between its source terminal and another n-type implanted region, particularly for operation at cryogenic temperatures. However, the added guard ring area increased the pixel layout area from an initial estimate of less than 32 µm x 32 µm to a final pixel area of greater than 50 µm x 50 µm. In the absence of a concern for total-dose-induced electron channeling in the field areas, clearly a concern at cryogenic temperatures, the guard rings would have been eliminated with a significant reduction in pixel area. With the use of a totaldose-hardened field-oxide wafer foundry process to reduce electron channeling in field regions and to reduce subthreshold current for switches, the pixel layout area could likewise have been reduced. Additional aspects of radiation hardening for ROIC operation at cryogenic temperatures are now considered. While the general trends for total-dose hardening of circuits operating at cryogenic temperatures (< 120 K) are consistent with those measured at ambient temperature, some oxide hole trapping mechanisms at cryogenic temperatures result in reduced pixel circuit operating margins. For example, the transport of holes within the silicon dioxide gate dielectric is greatly reduced at cryogenic temperatures, resulting in a relatively uniform hole distribution over the gate dielectric thickness. As a result, the use of an oxide growth process that is tailored to achieve hardness by avoiding the formation of traps at deep energy levels at or near the silicon dioxide-silicon interface appears to be less effective in reducing transistor threshold voltage shift for cryogenic use, partly because fewer holes are transported to this interface. Furthermore, the annealing rate of holes at room temperature due to hole transport and tunneling at the silicon dioxide-silicon interface is also believed to be greatly reduced at cryogenic temperatures, except for very thin gate oxides, typically less than 2 nm. Note that 2-nm gate-oxide technology is not currently utilized for infrared imager development to any large extent due to the breakdown voltage limits. For a typical imager application, the pixel output dynamic-range requirement of approximately 1 volt implies an electric field in the transistor gate oxide dielectric region in excess of 1 MV/cm for a supply voltage greater than 2 volts. Also note that the typical range of pixel supply voltage is 3-4 volts. For pixel circuits with amplifier transistors (Q1 in Figure 66) biased in the subthreshold regime, the total-dose-induced device threshold shift (i.e., translation along the x-axis for plots similar to Figure 57) is larger than for transistors biased into strong inversion. This is due to the competing mechanisms of electron-hole recombination at low electric fields within the device channel versus enhanced hole transport at high electric fields. Experimental results show that for practical device geometries, the maximum transistor channel electric field can be quite high, approaching 1 MV/cm, which is typical for subthreshold operation. [145] Therefore, the effects shown in Figure 57 are more significant for the typical amplifier transistors used for pixel electronics than for imaging-array peripheral circuitry with transistors biased into strong inversion.

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Figure 68. Mean Value of Output Voltage for 16 x 16 CTIA Test Readout Array

Figure 69. Mean Value of Nonlinearity for 16 x 16 CTIA Test Readout Array

Increased low-frequency transistor noise must be considered for pixel circuits operating at cryogenic temperatures. The voltage-noise power spectral density for NMOS transistors has IV-99

been often modeled as proportional to the number of carriers in the MOSFET channel for operation in strong inversion [146]: Sv = (K/fα) [VD2/(VG-VTO)2] Relatively recent experimental results support the hypothesis that low-frequency MOSFET noise is dominated by a thermally activated process rather than by tunneling. [147] For NMOS devices with a gate oxide thickness of 4.5 nm irradiated to 500 krad(Si) using a 10keV x-ray source, the low-frequency noise increased approximately by tenfold over an exposure range of 85 K to 285 K. Increases in both SV and α at a reference frequency of 1 Hz were observed. However, significant differences among devices measured from the same wafer were seen at low temperature, leading to the preliminary conclusion that roomtemperature noise screening may not be reliable for cryogenic applications. [147] For SFD pixel cells used for ambient and cryogenic temperature applications, the lowfrequency noise charge (i.e., noise electrons) can be estimated using the following [148]: 1/ 2

N amp

⎡ ∆f ⎤ 1 − cos 2πft ) ⎥ ( 2⎢ 2 ≈ Vn ( f ) df ⎥ SV ⎢⎢ ⎡1 + ( 2πfT )2 ⎤ ⎥ D ⎢⎣ ⎦⎥ ⎦ ⎣

∫

where SV is the SFD conversion gain (V/e-), Vn2(f) is the 1/f noise voltage PSD, ∆f is the pixel cell frequency bandwidth, and TD is the sampling time constant for a post-pixel CDS circuit used to reduce reset noise. Generally, total-dose-induced increases in low-frequency noise at cryogenic temperatures may be more important for the on-chip image array peripheral circuits, such as linear amplifiers and comparators, than for the transistors within the SFD or CTIA pixel cell electronics. This is due to the fact that with the small integration-node capacitors used to achieve photocurrent conversion gain, the mean square noise voltage component, kT/C, associated with the reset transistor (Q1 in Figure 54 and Q3 in Figure 66) typically dominates the noise level, including the amplifier transistor low frequency noise voltage integrated over the SFD or CTIA frequency bandwidth. For a CTIA pixel layout design with a 20-µm pitch or larger, amplifier transistor geometry can generally be designed to minimize 1/f noise. An equivalent expression for equivalent noise charge in noise electrons for a CTIA pixel cell for the reset-noise-limited case is given as [149]

( Cdet + Cfb ) nkTCfb = 2 CL ( Cfb + Cdet ) + Cfb Cdet q 2

N read

where n is a design parameter (typically 2), Cfb is the CTIA feedback capacitor, Cdet is the photodetector capacitance, and CL is the frequency band-limiting capacitor.

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Large imaging arrays contain significant functional complexity associated with the peripheral circuitry (i.e., circuits outside the two-dimensional array containing only pixel cells). These peripheral circuits contain functions including the following: dc bias generation, image array scanning and timing pattern generation, and analog multiplexing. Analog-to-digital conversion (ADC) is another function which may be included on the array, and the design options vary from the use of a limited number of converters operated at a high sampling rate to an approach referred to as “massively parallel” in which large numbers of ultra-low-power ADC circuits are abutted to the pixel array columns and each ADC processes only a limited number of columns. Total-dose hardening of ADC circuits included within contemporary ROIC designs is beyond the scope of the present discussion. Bias generation circuits are based on the design of a precise and stable current reference, and many CMOS designs have appeared in the literature.[144] For both visible and cryogenic infrared image arrays, it is critical to establish the target current value at either an ambient or cryogenic operating temperature depending on the application. However, the combined effects of total-dose-induced threshold voltage shift and temperature coefficients of threshold voltages increase the difficulty of the current reference design. Most practical solutions to developing a precise and stable current reference resort to its implementation with discrete total-dose-hardened, temperature-compensated circuitry external to the ROIC. External totaldose-hardened digital-to-analog converters (DAC) may also be used to provide fine adjustment to the current reference within the ROIC by means of calibration data downloaded to the DAC from an external data port. Image array scanning circuitry consists of digital logic elements that can be total-dose hardened using enclosed NMOS transistors. Typical digital logic cells containing enclosed NMOS layouts have been described. [135] The 16 x 16 element CTIA readout array discussed previously made extensive use of enclosed NMOS transistors within the scanning circuitry. This array was tested to 1 Mrad(Si) with no functional failures and minimal degradation of CTIA performance. [141] Timing pattern generator design for a total-dose environment can utilize hardened digital logic elements implemented with enclosed NMOS transistors. Array scanning and timing pattern-generator applications can be satisfied with typical total-dose-hardened cell library elements. Analog multiplexing is used for two primary applications. The first is to format the serial analog pixel video stream and to input this stream to a set of analog output amplifiers for designs that do not include an ADC on the ROIC. The second is to format and store analog pixel samples for the input to an ADC within the ROIC. Both applications make extensive use of low-loss analog switches and polysilicon-gate capacitors for the formatting and storage functions. Low-power operational amplifiers, generally used as voltage followers, provide buffering to the ADC input capacitance. CMOS operational amplifier design is well covered in the technical literature. Typical input offset voltage shifts with total dose must be accommodated, but many techniques used to provide operational amplifier offset cancellation have also been discussed and are relevant to total-dose hardening. [144] Clearly the use of enclosed NMOS transistors used for low-level analog switches can be an advantage in avoiding spurious leakage, which would degrade analog pixel samples stored on polysilicon-gate capacitors. Analog output buffer circuitry is similar in function to the

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internal operational amplifier circuits. Transfer of the video stream from the imaging array to the image processing electronics can be simplified using a differential approach. In this approach, external differential signal sensing compares each pixel sample with a lightshielded reference channel, with the total-dose-induced common-mode voltages eliminated by the external receiver circuitry.

6.5

ROIC Hardening by Design for Single Event Effects

Mitigation of single event effects (SEE) [150] as related to ROIC designs fabricated using currently available submicron feature sized bulk-silicon foundry CMOS has been largely limited to reducing the effects of single-event upset (SEU) rates for various latch designs used in the image array peripheral circuitry. These peripheral circuits primarily include the digital logic latches used to implement on-chip timing pattern generators and digital logic state machines used in conjunction with on-chip digital image processing. Other functions frequently included within the ROIC are analog-to-digital converters (ADC) for pixel conversion and, in some cases, digital-to-analog converters (DAC) embedded within highperformance pipelined ADC converters. Generally, the upset rates for unhardened bulk CMOS latches are considered inadequate for many critical digital logic array functions. Hardening-by-design can be employed to reduce latch upset rate, and one of the most efficient SEU-hardened latch designs is based on a circuit topology that has a very low probability of logic state upset due to a single heavy-ion strike. A latch design of this type contains internally redundant circuitry within the data retention path to avoid the upset of the latch due to a single node state transition. [151] These latches can be used to implement critical digital counters (i.e., binary or grey-code counters) used primarily for on-chip ADCs, for array timing pattern generators, and in certain cases to SEU harden latches used to implement shift-registers used to implement image array scanning. Another approach to SEU hardening consists of a technique commonly referred to as triple modular redundancy (TMR) [152] used with digital logic majority voting circuitry, indicated as “MAJ”, as shown in Figure 70. Also illustrated in this figure is another technique, known as temporal data sampling, used to avoid capture of any data that may have been corrupted by a single-event transient (SET), which may occur during the clock period that occurs during the data (or counter bit) transfer to the voting logic. [153], [154] Hence, the critical data or binary counter state can be protected from an SEE transient event as well as steadystate latch upset. As applied to the SEU hardening of the master counter function used for a timing pattern generator, each of the output bits of three identical counters are compared and corrected on a “bit-slice” basis by the voting or “majority” logic circuitry designated as the block “MAJ” in Figure 70, which is operating synchronously with the input data clock. The corrected master count is provided as a set of input bits to the decoding logic used to generate the various image array timing pulses. For ROIC designs that incorporate a high level of image processing, partial or full pixel frame buffers may be implemented on the ROIC. Pixel data are more efficiently stored in cross-coupled static RAM (SRAM) cells as compared to general-purpose digital logic

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registers. Figure 19 contains a schematic of an SEU-hardened SRAM cell containing two cross-coupled latches, as compared to the single cross-coupled latch used for non-hardened SRAM cells [155]. The nominal operation of the SEU-hardened cell prevents data corruption due to latch upset from a single-particle strike at a critical latch node. The SEU hardness is achieved due to a unique aspect of this latch design, including redundancy in the form of a dual latch, which, if uncorrupted, will restore the initial state of the upset latch node. In addition, due to the known opposite polarity voltage transitions in response to a heavy ion strike of the n-implanted versus the p-implanted transistor regions, this knowledge can be used to implement a latch that retains a nearly incorruptible zero state and a corresponding latch retaining a nearly incorruptible one state. In Figure 71, the upper cross-coupled latch retains an incorruptible one state and the lower latch retains an incorruptible zero state. Since these two latches form the essential incorruptible set to achieve data retention, this cell design can be used to avoid particle-induced upsets due to high-energy protons and heavy ions. [155] While this SRAM cell requires approximately twice the number of transistors as the conventional non-hardened SRAM, its use can reduce the hardware complexity associated with the alternative approach consisting of hardware-based error detection and correction (EDAC), including the elimination of the memory to store the parity bits used for EDAC implementation.

Figure 70. SEU Mitigation Circuit Implemented with Temporal Sampled Latches and Synchronous Majority Voting

For imaging arrays used in low earth orbits, continuous imaging may not be required. Furthermore, increased SEU rates may be limited only to locations in the orbit that pass over IV-103

the region of the South Atlantic Anomaly (SAA) [150], which contains increased proton flux levels known to result in higher SEU rates for some CMOS technologies. In such cases, SEU mitigation can be achieved after transiting through the SAA by updating critical latch data within the ROIC, during each orbital pass, from external SEU-hardened SRAM. Thereafter, the probability of latch SEU may be limited to a lower rate associated with galactic-cosmicray-induced upsets, which are less frequent compared to protons within the SAA affected region of the orbit. Single-event latch-up (SEL) can occur in technologies containing a 4-layer pnpn path, as in the bulk CMOS cross-sectional drawing shown in Figure 72. During nominal CMOS operation, the pnpn junctions would each be either at zero bias or reverse bias. However, under certain extreme bias conditions, including those of a heavy ion strike, during which local current densities develop junction forward bias, the formation of one or more active conducting pnpn structures can occur.

Figure 71. Schematic of SEU Hardened SRAM Cell

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Figure 72. Cross Sectional Drawing of PNPN Structure Within Bulk CMOS

The cross-sectional drawing in Figure 72 contains a simplified transistor model of this pnpn structure, with the equivalent npn device emitter formed by an NMOS transistor source terminal which is permanently connected to the negative supply voltage. The equivalent pnp device emitter is formed by a PMOS transistor source connected to the positive supply voltage. The base terminal of the equivalent npn consists of the p-type contact to the pepitaxial or p-type bulk substrate and can become locally forward biased due to a current transient flowing through the epitaxial layer or substrate bulk spreading resistance. The base terminal of the equivalent pnp is nominally tied to the positive supply voltage as connected through the n-well spreading resistance, and can likewise become forward biased due to a voltage developed by a current transient flowing through the n-well spreading resistance. In general, for conventional bulk silicon CMOS, pnpn conduction can be initiated by electrical triggering, pulsed laser photocurrent, and by a heavy ion strike and in some cases by a proton strike. Figure 73 contains a typical pnpn I-V characteristic, in which the device can sustain the high current state in part due to thermally induced regeneration within the structure, given that the npn and pnp transistor current gain values increase with increasing temperature and that the junction voltages decrease with increasing temperature generated locally by the pnpn high current density. Figure 73 also shows the critical pnpn latching voltage, VL, the holding voltage, VH, and holding current, IH. For a given CMOS technology primarily utilized for digital logic applications, these values of VL, VH, and IH can be determined from device characterization data for a specific set of layout design rules. Consequently, if the ROIC supply voltage is less than VL, SEL will generally not occur in the absence of significant stray series inductance. Similarly, if the external power supply operating current is limited to a value below IH at which the voltage drops below VH, the SEL will not be sustained. The CMOS foundry pnpn characterization data may not be fully applicable to ROIC designs, especially those which exceed the nominal die size used for digital logic. For example, the layout design rules used for digital logic generally assume that substrate and implanted well regions contacts have been incorporated into an extensive gridwork within array of digital logic cell layouts, but for ROIC designs a similar gridwork may not be present due to pixel IV-105

area limitations. Furthermore, for ROIC packaging the elimination of the backside ohmic contact to the bulk silicon substrate is not an uncommon practice, which further increases the likelihood of pnpn triggering. Hence, for ROIC designs, due to the lack of an extensive gridwork of low-resistance metal interconnects nominally used to shunt the substrate and implanted well region spreading resistance, SEL margins developed from characterization of digital logic test structures should not necessarily be applied, at least for those imagers operated at non-cryogenic temperatures. For infrared and visible imagers operated below temperatures at which significant thermal carrier generation occurs, cryogenic temperature operation results in reduced bipolar device (i.e., npn or pnp) current gain, commonly referred to as carrier freeze-out, and consequently SEL cannot be sustained. This effect is a consequence of an additional criterion for pnpn regeneration that requires at the pnpn operating point, IH and VH, the sum of the npn and pnp common-base current gains must exceed unity. For n-well CMOS technology, typically the wide-basewidth npn device current gain is very low at cryogenic temperatures. However, readout wafers are routinely screened for initial functionality during testing at ambient, and pnpn latchup can damage die during this screening. During testing, damage to the readout die can be avoided by proper powersupply sequencing and current limiting of voltage supplies used under the assumption that localized latchup sites can be electrically triggered.

Figure 73. Typical Current vs Voltage Characteristic of CMOS Parasitic PNPN Structure

Detector response to proton and heavy ion strikes has been studied for visible APS as well as for infrared imagers. [156], [157]. For the APS imagers, it was found during image array testing that the number of pixels collecting charge induced by heavy ions is smallest for an angle normal to the surface of the imager die and greater for shallow grazing angles. In some cases, the heavy-ion-induced charge along a 1-mm track within the bulk silicon appears to IV-106

spread over a roughly circular area with a diameter of 250 µm. These results for heavy ion testing are consistent with testing of ROIC circuitry designed for infrared applications, and this reported effect may be related to the spurious charge collection efficiency of the critical junctions within CTIA circuitry. Apparently, similar results for charge spreading are also obtained when certain digital memory circuits are tested with heavy ions. Charge collection times extending to integration times between 5 mS to 25 mS are attributed to a slow collection mechanism with heavy-ion-induced carrier diffusion deep into the in the field-free regions of the lightly doped bulk silicon substrate. For ROIC wafer fabrication, the use of conventional micron-thin lightly doped expitaxial layers on heavily doped substrates exhibiting shorter minority carrier lifetimes may be preferred over bulk silicon die on the order of several millimeters in thickness. The use of bulk silicon thinning of the ROIC die after wafer processing may also reduce the effects of charge spreading associated with a particle strike.

6.6

Conclusions

ROIC development for visible and infrared imaging has reached levels of complexity, in terms of transistor count, that are approaching that for CMOS digital logic processors for space applications. This is an even more surprising outcome when one considers that ROIC designs typically include very large sets of both minimum and non-minimum geometry transistors used for various applications within the imager readout electronics. At present, commercial foundries remain a preferred fabrication source for ROIC designs due to factors including the availability of photostepper level reticle photocomposure of very large arrays, which can be designed to exceed the currently available reticle field limits of approximately 2.2 cm x 2.2 cm. Furthermore, the commercial foundry history of achieving predictable yields for large die, and the routine use of large-diameter wafers allows for maximizing the number of large-area ROIC die sites per wafer. With the advent of deep-submicron feature-sized CMOS, the task of total-dose hardening at the transistor level has largely been reduced to employing hardening-by-design techniques to suppress edge-effect leakage paths. Commercially available nanometer gate oxide transistors typically exhibit threshold shifts at 100 krad(Si) and beyond of on the order of a few percent of the analog supply voltage. Additionally, cancellation of total-dose-induced shifts in threshold voltage and dark current at the pixel level has been successful using analog background subtraction techniques. Also, the use of current-mirror techniques for biasing pixel amplifier and peripheral image array analog functions results in first-order compensation of threshold voltage shifts due to total dose as well as operation at cryogenic temperatures. Design considerations and performance up to 1 Mrad(Si) and beyond for both SFD and CTIA pixel types have been discussed. For example, 512 x 512 pixel image arrays implemented with SDF pixels have been irradiated up to 10.2 Mrad(Si) with no apparent change in the operation of the FPN reduction circuitry contained within the array column readout electronics. [140] In another case, 0.5-µm-feature-sized circuits containing a 16 x 16 element CTIA test array that uses enclosed NMOS devices have successfully been operated at IV-107

ambient temperature up to the 100 kiloelectron well capacity with no appreciable loss of photoconversion gain, dynamic range, or linearity after 1 Mrad(Si) Cobalt-60 exposure. [141] Mitigation of heavy-ion single-event upset effects is likewise managed using hardening-bydesign techniques. The use of hardened latch topologies results in greatly reduced upset rates as compared to unhardened latch circuit designs from cell families available for use with commercial foundry offerings. Reduced upset rates using triple modular redundancy and majority voting logic circuitry provides further mitigation when applied either at the latch level or at the functional level, such as for implementing critical timing pattern generators. It is believed that with increased use of on-chip deep-submicron-feature-sized digital logic functions to implement more powerful digital image processing within an ROIC chip (including digital subtraction of scene background and fixed pattern noise sources as well as image frame averaging), the end-to-end sensor performance in space radiation environments will continue to improve.

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7.0

Summary and Concluding Remarks

Over the past two decades, advanced focal-plane arrays operating in the visible and infrared regions of the electromagnetic spectrum have undergone remarkable technological advancement in terms of array size, sensitivity, thermal performance, operating speed, mode programmability, and ease of use. This progress has enabled the design and development of new classes of highly capable electro-optical (EO) space-based sensor systems. The interaction of advanced focal planes found in these EO sensors (and their associated microelectronics) with the natural space radiation environment has been a subject of concern and often a central theme in their development. Careful consideration of the pertinent space radiation environments, including their quantitative spatial/temporal characteristics, coupled with understanding the details of radiation transport through sensor shielding and assessing sensor performance degradation induced by radiation-induced transient and permanent damage, are essential steps for EO sensor designers. That process in turn provides clear insight into the requirements for radiation-hardened focal-plane technology. It also serves as a guide to the development of system-level strategies for mitigating transient and permanent radiation effects. This tutorial provides an overview of key aspects of the process outlined above. In Section 2, the principal models used for predicting the structured earth-bound space-based radiation environment (such as NASA’s AP-8 and AE-8 codes) are discussed. More recent (and in many cases more accurate) modeling approaches, based on the CRRES and APEX databases, are also considered. The implications of spacecraft shielding and the models used to accurately perform shielding analysis are discussed. Examples are provided of useful parametric plots, such as annual dose-depth curves and external proton/electron flux versus particle energy parameterized by orbital position. This information is presented for wellknown example orbits. In addition, the effects of solar flares are discussed. In Section 3, radiation effects on all of the key types of silicon-based visible focal planes are reviewed, including charged-coupled devices, active pixel sensors, charge-injection devices, and silicon hybrid technologies. The effects of ionizing and nonionizing radiation that are common to all of these sensors (e.g., the increase in average dark current and the formation of dark-current spikes) are detailed. The displacement damage effect that is unique to CCD technology - the increase in charge transfer inefficiency (CTI) - is described. Other topics covered include dark-current enhancement due to high-electric-field regions, annealing effects, random telegraph noise, and potential hardening approaches. In Section 4, radiation effects on infrared focal planes are reviewed. The discussion in that section focuses on effects in the focal-plane layer that serves as the infrared detecting material (i.e., indium antimonide, mercury cadmium telluride, doped silicon, etc.), which is generally operated at cryogenic temperatures. (The radiation susceptibility of cryogenic silicon CMOS readout circuits used to extract small signals from infrared detector arrays is described separately in Section 6.) The roles of displacement and surface damage are described, as are the systematic effects of ionization-induced transients. An aspect unique to

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infrared detector arrays – damage to the infrared material surface passivants (i.e., ZnS and CdTe) - is described. Section 5 provides detailed information on both empirical measurements and simulationbased modeling of focal-plane responses to transient ionizing radiation (primarily due to incident protons). Simulation results are given for visible (silicon) and infrared (HgCdTe) focal planes as well as for devices with complex pixel/multiplexing architectures. The focus is on understanding the spatial and amplitude characteristics of these radiation-induced transient effects - in an image format - to facilitate the understanding of their system-level implications and potential mitigations. Section 6 describes radiation susceptibility and hardening strategies associated with the readout-integrated-circuit portion of visible and infrared detector arrays. Whereas in the earlier sections of this tutorial the emphasis was on the impact of radiation on detector pixels, here the on-focal-plane microelectronic circuitry responsible for converting pixel photocurrents into usable signals is addressed. The impact of radiation on field-effect transistors that act as building blocks for these signal conversion circuits, such as the sourcefollower-per detector and the capacitive-feedback transimpedance amplifier, are described as well as selected design and process mitigation strategies. The dependence of radiation susceptibility and permanent damage on cryogenic operating temperature is also reviewed. This tutorial aims to provide a systematic review of the important aspects of space radiation effects on visible and infrared focal planes, spanning from the system end of the problem (i.e. environments for specific orbits and shielding) to the technology details of the focal-plane devices and their microelectronic components. Hopefully, the background information provided in this course will be useful to space-system instrument designers and to technology planners for future applications and the associated need for radiation-hardened focal-plane technologies.

8.0

Acknowledgements

One of us (AHK) thanks Robert E. Mills of Raytheon Vision Systems for providing two figures from one of his papers.

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9.0

References

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[125] S. Kirkpatrick, “Modeling Diffusion and Collection of Charge from Ionizing Radiation in Silicon Devices," IEEE Trans. Electronic Devices, Vol. ED-26, 1979, pp. 1742-1753. [126] J. C. Pickel and J. T . Blandford, “Cosmic Ray Induced Errors in MOS Memory Ce l ls," IEEE Trans. Nuclear Science, Vol. NS-25, 1978, pp. 1166-1171. [127] J. C. Pickel et al., “Radiation-induced charge collection in infrared detector arrays,” IEEE Trans. Nucl. Sci., vol. 49, no. 6, pp. 2822-2829, December 2002. [128] R. Ladbury et al., “Characteristics of the Hubble Space Telescope’s radiation environment inferred from charge-collection modeling of near-infrared camera and multi-object spectrometer darkframes,” IEEE Trans. Nucl. Sci., vol. 49, no. 6, pp. 2765-2770, December 2002. [129] J. C. Pickel et al., “Proton-induced secondary particle environment for infrared sensor applications,” IEEE Trans. Nucl. Sci., vol. 50, no. 6, pp. 1954-1959, December 2003. [130] P. W. Marshall et al., “Proton-induced transients and charge collection measurements in a LWIR HgCdTe focal plane array,” IEEE Trans. Nucl. Sci., vol. 50, no. 6, pp. 1968-1973, December 2003. [131] E. G. Stassinopoulos, “Charged Particle Radiation Exposure of Geocentric Satellites," American Institute of Physics Conference Proceedings, Vol. 186, High Energy Radiation Background in Space, 1987, pp. 3-63. [132] T. S. Lomheim, Infrared Systems and Technology Course, 2002. [133] M. J. Hewitt, et al., “Infrared Readout Electronics: A Historical Perspective,” SPIE, vol. 2226, 1994. [134] L. J. Kozlowski, “Low-noise capacitive transimpedance amplifier performance versus alternative IR detector interface schemes in submicron CMOS,” Proc. SPIE vol. 2745, pp. 2-11, June 1996. [135] R. C. Lacoe, NSREC Short Course, July 2003. [136] R. C. Lacoe, J. V. Osborn, R. Koga, S. Brown, and D. C. Mayer, “Application of Hardnessby-Design Methodology to Radiation –Tolerant ASIC Technologies, “ IEEE Trans. on Nuclear Science, Vol. 47, No.6, pp. 2334-2341, December 2000. [137] W.J. Snoeys, T.A. Palacios Guiterrez, G. Anelli, “A New NMOS Layout Structure for Radiation Tolerance, “ IEEE Trans. on Nuclear Science, Vol. 49, No. 4, pp. 1829-1833, August 2002. [138] J. K. Shreedhara, et al., “Circuit Technique for Threshold Voltage Stabilization Using Substrate Bias in Total Dose Environments,” IEEE Trans. on Nuclear Science. Vol. 47, No. 6, pp. 2557-2560, December 2000. [139] M. H. White, et al., “Characterization of Surface Channel CCD Imaging Arrays at Low Light Levels,” IEEE Transactions on Solid State Circuits, vol. SC-9, pp. 1-13, February 1974. [140] J. Bogaerts, et al. “Total Dose and Displacement Damage Effects in a Radiation-Hardened CMOS APS,” IEEE Transactions on Electron Devices, Vol. 50, pp. 84-90, No. 1, January 2003. [141] D. E. Romeo and C. S. Paul, unpublished report, The Aerospace Corporation, 2001. [142] S. M. Sze, Physics of Semiconductor Devices, 2nd Edition, Wiley, New York, 1981. [143] J. A. Felix, et al., “Bias and Frequency Dependence of Radiation-Induced Charge Trapping in MOS Devices,” IEEE Transactions on Nuclear Science, pp. 2114-2120, vol. 48, no. 6, December 2001. [144] J. Baker, H. W. Lee, D. E. Boyce, CMOS, Circuit Design, Layout, and Simulation, pp. 427457, IEEE Press, New York, 1998. [145] J. C. Pickel, IEEE NSREC Short Course, July 1993. [146] D. M. Fleetwood, T. L. Meisenheimer, J. H. Scofield, “1/f-noise and Radiation Effects in MOS Devices,” IEEE Transactions on Electron Devices, vol. 41, pp 1953-1964, December, 1994. [147] H. D. Xiong, et al., “Temperature Dependence and Irradiation Response of 1/f-Noise in MOSFETs,” IEEE Transactions on Nuclear Science, vol. 49, no. 6, December 2002. [148] M. Loose, et al., “HAWAII-2RG: a 2K x 2K CMOS Multiplexer for Low and High Background Astronomy Applications,” SPIE Vol. 4850.

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[149] L. Kozlowski, et al., “Performance Limits in Visible and Infrared Imager Sensors,” IEDM, December 1999. [150] G. C. Messenger, M. S. Ash, The Effects of Radiation on Electronic Systems, Second Edition, pp, 416-494, Van Nostrand Reinhold, New York, 1992. [151] R. Velazco, et al., “SEU-Hardened Storage Cell Validation Using A Pulsed Laser,” IEEE Trans. on Nuclear Science, vol. 43, no. 6, pp. 2843-2848, December 1996. [152] R. Katz, et al., “SEU Hardening of Field Programmable Gate Arrays (FPGAS) for Space Applications and Device Characterization,” IEEE Trans. on Nuclear Science, vol. 41, no. 6, pp. 2179-2186, December 2000. [153] D.G. Mavis and P.H. Eaton, “SEU and SET Mitigation Techniques for FPGA Circuit and Configuration Bit Storage Design,” Proc. 2000 Mil. Aero. Appl. Prog. Dev. Tech. Conf., 2000. [154] D.G. Mavis and P.H. Eaton, “Soft Error Rate Mitigation Techniques for Modern Microcircuits,” Proc. of 2002 Intl. Rel. Phys. Symp., pp. 216-225, Apr. 2002 [155] M. N. Liu and S. Whitaker, “Low Power SEU Immune CMOS Memory Circuits,” IEEE. Trans. Nuc. Sci., vol. 39, no. 6, pp. 1679-1684, Dec 1992. [156] X. Belredon et al., “Heavy Ion-Induced Charge Collection Mechanisms in CMOS Active Pixel Sensor,” IEEE Trans. On Nuclear Science, Vol. 49, No. 6, pp. 2836-2843, December 2002. [157] C. J. Marshall et al., “Heavy Ion Transient Characterization of a Hardened-by-Design Active Pixel Sensor Array,” IEEE NSREC Radiation Effects Data Workshop, pp. 187-193, July 2002. [158] J. R. Janesick, Scientific Charge Coupled Devices, SPIE Press, Bellingham, WA, 2001. [159] E. R. Fossum, “Active pixel sensors: Are CCDs dinosaurs?,” Proc. SPIE, vol. 1900, pp. 2-14, 1993. [160] H-S Wong, “Technology and device scaling considerations for CMOS imagers,” IEEE Trans. Electron Devices, vol. 43, pp. 2131-2142, 1996. [161] Y. Bai et al., “Hybrid CMOS focal plane array extended UV and NIR response for space applications,” Proc. SPIE, vol. 5167, pp. 83-93, 2004. [162] C-Y Wu, Y-C Shih, J-F Lan, C-C Hsieh, C-C Huang, and J-H Lu, “Design, optimization, and performance analysis of new photodiode structures for CMOS Active-Pixel-Sensor (APS) imager applications,” IEEE Sensors Journal, vol . 4, no. 1, pp.135-144, February 2004.

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2004 IEEE NSREC Short Course

Section V

Solar Cell Technologies, Modeling, and Testing

Robert J. Walters Naval Research Laboratory

2004 NSREC Short Course

Solar Cell Technologies, Modeling, and Testing Robert J. Walters, S. R. Messenger, G. P. Summers, and E. A. Burke Naval Research Laboratory 1 2

Introduction ...........................................................................................................................................................2 Radiation Effects on Solar Cells............................................................................................................................3 2.1 Introduction .....................................................................................................................................................3 2.2 Solar Cell Device Physics ...............................................................................................................................3 2.3 Radiation-Induced Degradation Mechanisms .................................................................................................7 3 Modeling Techniques ..........................................................................................................................................12 3.1 Background ...................................................................................................................................................12 3.2 The JPL Method ............................................................................................................................................14 3.3 NRL Method .................................................................................................................................................17 3.4 Correlating the RDCs with NIEL ..................................................................................................................25 4 On-Orbit Solar Cell Performance Predictions .....................................................................................................26 4.1 Environment Calculations .............................................................................................................................26 4.2 Shielding Calculations...................................................................................................................................27 4.2.1 JPL Shielding Calculations..................................................................................................................27 4.2.2 NRL Shielding Calculations ................................................................................................................28 4.3 Solar Cell Performance Predictions...............................................................................................................32 4.4 Mission Examples .........................................................................................................................................33 5 Specific Solar Cell Technologies.........................................................................................................................38 5.1 Single-junction, Crystalline Semiconductor Solar Cells ...............................................................................38 5.2 Multijunction Solar Cells ..............................................................................................................................38 5.2.1 Mechanisms for Multijunction Solar Cell Radiation Response ...........................................................38 5.2.2 Modeling Multijunction Solar Cell Radiation Response .....................................................................47 5.3 Thin Film Photovoltaics ................................................................................................................................49 5.3.1 Amorphous Si ......................................................................................................................................49 5.3.2 CuIn(Ga)Se2 .........................................................................................................................................54 6 On-Orbit Solar Cell Performance Predictions .....................................................................................................62 7 Special Topics in Solar Cell Radiation Response................................................................................................69 7.1 Solar Cell Response at High Degradation Levels..........................................................................................69 7.1.1 Region II ..............................................................................................................................................70 7.1.2 Region III.............................................................................................................................................71 7.2 Case of Nonuniform Damage Deposition .....................................................................................................72 8 Testing Approaches .............................................................................................................................................79 9 Summary..............................................................................................................................................................81 10 References .....................................................................................................................................................82

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1 Introduction This short course presents a study of the effects of exposure to the space radiation environment on the electrical performance of a variety of solar cell technologies and approaches used to model them. The discussion begins with a review of the basic physics of the photovoltaic effect and the operation of a solar cell. The basic mechanisms controlling the solar cell radiation response are then described for the case of a simple, single-junction device. Building on this background, two techniques for modeling solar cell performance in a radiation environment are presented, the equivalent fluence method developed by the US Jet Propulsion Laboratory and the displacement damage dose method developed by the US Naval Research Laboratory. The primary goal of both procedures is the correlation of degradation data taken after irradiation by different particles at various energies. The main difference between the two methods is that in the JPL method the energy dependence of the relative damage coefficients is experimentally determined, whereas in the NRL approach the energy dependence is calculated. The procedures for accounting for shielding are studied in detail. The two methods are then exercised to produce performance predictions for a single-junction GaAs solar cell in an Earth orbit, and the two methods are shown to give the same results. With the basic physics of a simple solar cell device understood and methods for modeling and predicting the solar cell performance in a space radiation environment established, the discussion expands to include more complex solar cell technologies. In particular, the radiation response mechanisms of multijunction and thin film solar cell technologies are discussed. In the case of the thin film solar cells, the annealing characteristics are also investigated in detail. The result is a comprehensive understanding of the response of these technologies to exposure to the space radiation environment. Using this understanding, a section of the short course is dedicated to making on-orbit solar cell performance predictions for state-of-the-art multijunction and thin film technologies. Predictions are made assuming both a typical rigid honeycomb solar array and a lightweight, flexible array. Orbits representative of LEO, MEO and GEO are considered. The results are used to highlight the strengths and weakness of each solar cell technology in realistic operational scenarios. The short course also includes a discussion of two aspects of solar cell radiation response termed "special topics". One such topic is the response of a solar cell to irradiation to high fluence levels. The damage mechanisms operative in this high fluence regime are described to produce a more comprehensive understanding of solar cell radiation response. The other special topic is the case of a solar cell response to non-uniform radiation-induced damage. A calculational method is presented for accurately modeling the solar cell response in this case, which also leads to a method for predicting the response of an arbitrary solar cell given knowledge of the cell structure and an estimate of the minority carrier diffusion length in the material. This method is applied to both single-junction Si and GaAs solar cell as well as more complicated structures such as multijunction InGaP2/GaAs/Ge solar cells. The discussion concludes with a look at approaches for ground based radiation testing of solar cells. It is shown how knowledge of the basic physics governing the solar cell radiation response can be used to craft a radiation experiment that produces maximum results while being both time

V-2

and cost efficient. Specific aspects of solar cell radiation testing that require special attention are highlighted. 2

Radiation Effects on Solar Cells

2.1 Introduction A solar cell consists essentially of a p-on-n junction in a semiconductor material that, when under illumination by solar photons, produces a voltage, called the photovoltage, by the photovoltaic effect. If an ohmic contact is placed on p and n sides of the junction, then the photogenerated charge carriers can be extracted from the device, thus generating a photocurrent. If the solar cell is connected across an electronic load, then power can be extracted and used to drive a system. Photovoltaics are, by far, the primary space power source for earth orbiting satellites and many interplanetary spacecraft. The space environment is often characterized by a harsh radiation environment; therefore, to be used in space, the radiation response of a solar cell must be well understood. In this section, the radiation response of a solar cell is described. The section begins with a brief review of the device physics of a solar cell and then describes the mechanisms by which particle radiation affects solar cell operation. 2.2 Solar Cell Device Physics A schematic drawing of a basic, single-junction solar cell is shown in Figure 1. This figure depicts an n+p structure where the n-type dopant concentration is one or two orders of magnitude higher than that of the p-type region so that a one-sided, abrupt junction exists. When the solar cell is illuminated, the photons penetrate the material, and those photons with energy above the semiconductor bandgap are absorbed and create electron-hole pairs. Each solar cell material has specific absorption characteristics. Examples of the absorption coefficient for Si and GaAs are shown in Figure 2. As the wavelength increases, the absorption coefficient decreases so that the longer wavelength photons are absorbed deeper into the solar cell. The absorption coefficient for GaAs is seen to be over an order of magnitude larger than that of Si for most of the wavelength shown. This is the case because GaAs is a direct band-gap material, so the photon can be directly converted into an electron-hole pair. Si, on the other hand, being an indirect band-gap, requires the absorption of a phonon during photon absorption in order to conserve both energy and momentum thereby reducing the photon absorption efficiency. Because of this, full absorption can be achieved in a GaAs based solar cells that is as thin as 2 µm while a Si solar cell must be 100 µm thick or more to achieve full photon absorption.

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Incident sunlight

depletion n-type emitterregion ~ 10 18- W cm -3

LOAD

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p-type base ~ 10 16 cm -3

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Figure 1: This is a schematic drawing of a single junction solar cell.

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The absorption coefficient data for a given material as shown in Figure 2 must be combined with the incident photon spectrum to determine the solar cell efficiency. Solar cell efficiency refers to the efficiency with which a solar cell converts the incident photons to electrical energy. For space solar cells, the incident photon spectrum is the air mass zero (AM0) solar spectrum, i.e. the spectrum encountered outside the Earth’s atmosphere (Figure 3). For reference, the AM1.5 spectrum which is representative of the spectrum on the Earth’s surface is also shown in Figure 3. Calculations of ideal solar cell efficiency as a function of band-gap are shown in Figure 4 [1].

Si GaAs

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Figure 2: Absorption coefficient data for Si and GaAs.

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Figure 3: AM0 and AM1.5 solar spectra. AM0 is the spectrum encountered in space. AM1.5 is representative of the spectrum on the Earth’s surface.

Figure 4: Ideal solar cell electrical conversion efficiency as a function of bandgap for both the AM0 space spectrum and the terrestrial AM1.5 spectrum. [1]

While Figure 4 gives ideal solar cell efficiency values, efficiencies achieved in practice are typically lower due to internal loss mechanisms within the absorber material. Once a photon is absorbed and the electron-hole pair is created, the free charge carriers must be separated and collected. Depending on the polarity of the semiconductor material in the region where the photon is absorbed, one of the charge carriers will be a minority carrier. It is the extraction of this minority carrier that gives rise to the photocurrent; therefore, a solar cell is a minority carrier

V-5

device. If the electron-hole pair is created away from the junction in a field-free region, then the minority charge carrier must diffuse through the material until it reaches the junction. Once at the junction, the minority charge carrier is swept across the depletion region by the junction electric field and collected. If the photon is absorbed within the depletion region, then the photogenerated charges are immediately separated by the field and collected. Since collection by drift along the electric field is much faster than diffusion through the bulk, carrier collection is much more efficient in the depleted region. The response of a solar cell to illumination by monochromatic light is referred to as the quantum efficiency (QE) [2]. There are two types of QE measurement. An external QE measurement does not account for reflections from the cell surface; whereas, an internal QE measurement accounts for surface reflections. External QE measurements made on several single-junction solar cell technologies are shown in Figure 5. For reference, the AM0 solar spectrum, normalized to the maximum value, is superimposed on the QE data. Referring back to the absorption coefficient data (Figure 2), the longer wavelength photons are absorbed deeper into the cell, so as the wavelength increase, the QE data are representative of the response of the emitter, depletion region and then the base of the solar cell. The cutoff in the QE data at the longer wavelength values corresponds to the band-edge of the semiconductor material. Si, with the smallest band gap of 1.12 eV has the widest spectral response, InP with a band-gap of 1.34 eV cuts off at about 920 nm, while GaAs with the largest band gap of 1.43 eV has the narrowest spectral response. Note how the GaAs and InP data show a sharp cutoff at the band-edge while the Si data show a more gradual decrease. This is again due to the fact that GaAs, and InP, are direct band-gap materials in contrast to Si. 1.0

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Figure 5: External QE data measured on several single-junction solar cell technologies. For reference, the AM0 spectrum is also shown where the AM0 data has been normalized to the maximum value.

It is clear that the efficient operation of a solar cell is critically dependent on the mobility of minority charge carriers, and the primary physical parameters that influence solar cell

V-6

performance are the minority carrier lifetime (t) and diffusion length (L). The other physical attribute of the n/p junction that strongly affects the photovoltaic performance is the dark current characteristic which is the current generated across the junction under applied forward bias while the device is in the 4dark. The forward bias dark current flows in opposition to the photogenerated current thereby draining away some of the photovoltage. This can be seen in Figure 6, which gives an example of a current vs. voltage (IV) curve measured on a solar cell in the dark and under simulated solar light. In the following sections, the effect of particle irradiation on the minority carrier diffusion length and junction dark current will be investigated. The electrical output of a solar cell is typically characterized by parameters extracted from the IV curve. Three common parameters are illustrated in Figure 6. The current measured at short circuit is called the short circuit current (Isc). The voltage at open circuit is called the open circuit voltage (Voc). The maximum electrical power generated by the solar cell is given the symbol Pmp. The efficiency at which the solar cell converts the incident solar energy to electrical energy is given by dividing Pmp by the appropriate solar constant, which, for the space air mass zero (AM0) spectrum, is 136.7 mW/cm2. The shape of the IV curve is characterized by the fill factor (FF). The FF is defined as the ratio of Pmp to the product of Isc and Voc. The FF gives an indication how well the IV curve fills the maximum power rectangle, which is the rectangle formed in the forth quadrant by a vertical line passing through Voc and an horizontal line passing through Isc as shown by the dotted lines in Figure 6. 150

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Figure 6: This is a plot of typical IV curves measured on a single-junction solar cell. Illuminating the solar cell pulls the IV curve down into the forth quadrant so that power can be extracted from the device. The dark current is seen to oppose the photogenerated current. Typical parameters used to characterize solar cell performance are shown. The dotted lines define the “maximum power rectangle”.

2.3 Radiation-Induced Degradation Mechanisms From the point of view of photovoltaic operation, the primary effect of particle irradiation of a solar cell is displacement damage where atoms in the semiconductor lattice are moved from their equilibrium position to form point defects like vacancies and interstitials or defect complexes like vacancy-impurity clusters. These defects can form energy levels within the forbidden gap of

V-7

the semiconductor forming charge trapping centers. The existence of these defect centers affects charge transport in essentially five basic ways as shown schematically in Figure 7 [3].

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Tunneling Figure 7: This is a schematic representation of the effects that radiationinduced defect levels can have on current transport in a solar cell [3].

Carrier generation (labeled #1 in Figure 7) occurs when the existence of the defect energy level makes it statistically favorable for a charge carrier to move from a bound to a free energy level. The liberated charges are then swept away by the junction field, which produces a current. This causes the dark IV characteristic to increase which degrades the photovoltage (i.e., Voc). Recombination (labeled #2 in Figure 7) occurs when it is statistically favorable for an electronhole pair to recombine at the defect site. When this occurs, free charge carriers are lost resulting in degradation of the photocurrent (i.e. Isc). Trapping (labeled #3 in Figure 7) occurs when a defect level is able to capture and temporarily localize free charge carriers which are then thermally reemitted. The forth mechanism illustrated in Figure 7 is referred to as compensation. Compensation occurs when a defect level permanently localizes a free charge carrier supplied by the dopant atoms. This reduces the majority charge carrier density and is referred to as carrier removal. The fifth mechanism is trap-assisted tunneling where the position of the defect level effectively lowers the tunneling potential. The discussion will now relate the effect of the radiation-induced defects to degradation of the solar cell photovoltaic output. This discussion will focus on single-junction, crystalline technologies. Other solar cell technologies like multi-junction and thin film cells will be addressed later. The effects of irradiation by 1 MeV electrons on the maximum power output of several technologies are shown in Figure 8. The crystalline Si data are from [4], and these cells are representative of the standard Si solar cells that served as the workhorse of the solar power industry for both terrestrial and space applications for many years. The GaAs/Ge data are from [5]. The GaAs/Ge devices consist of GaAs layers grown epitaxially on Ge substrates. These were the first III-V based devices to gain wide use as a photovoltaic device, and the GaAs/Gebased technology has since about 1990 replaced Si as the baseline for space power systems. The InP data are from [6], and these cells consist of InP layers grown expitaxially on InP substrates. V-8

The InP technology is another III-V based technology that was considered for space use due to its high efficiency and radiation resistance, but this technology is not currently in use. 30

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Figure 8: The radiation response of single-junction crystalline semiconductor solar cell technologies that have been developed for space use.

The general shape of the degradation curves for the different technologies is similar. When plotted against the log of the particle fluence, as in Figure 8, the PV parameter data remain essentially constant up to a certain fluence level beyond which the data degrade nearly linearly with the log of the fluence. This fluence level can be used to give an evaluation of the relative radiation hardness of the technology. The fluence level for the Si data, for example, is about 1x1012 cm-2 while that for the InP technology is about 5x1014 cm-2, indicating the InP solar cells to be much more radiation resistant that the Si cells. The curves in Figure 8 have the same general shape because the radiation-induced degradation mechanisms are similar in the different technologies. It has been shown in InP, GaAs, and Si cells that the primary degradation mechanism is the decrease in L due to radiation-induced recombination centers, and the implication is that this mechanism applies generally to all crystalline semiconductor-based photovoltaic devices [7,8,9]. Degradation of L reduces the carrier collection efficiency since those photogenerated carriers created in the cell base are less likely to reach the junction. This effect is seen in the QE data where most of the radiationinduced degradation appears in the response to longer wavelengths of light which are absorbed deeper in the cell (Figure 9). Degradation in the shorter wavelength response is apparent at higher fluence levels, and the mechanism for this degradation will be discussed in a later section. Degradation of the solar cell QE results in degradation of Isc which is reflected in the decrease in Pmp seen in Figure 8.

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Figure 9: The degradation in QE of an InP solar cell due to 3 MeV proton irradiation. The particle fluence is given in the legend in units of cm-2. The long wavelength response degrades due to diffusion length degradation.

Using the formalism of Hovel [2], an estimate of L can be extracted from analysis of the QE data, which has been done for the data of Figure 9 [6]. The decrease in L with the introduction of defects is given by [4]: Equation 1 σ υI 1 1 1 = 2 + ∑ i ti φ = 2 +K L φ D L (D d ) L 0 L0 2

where Lo is the pre-irradiation value of L, σi is the minority carrier capture cross section of the ith recombination center, Iti is the introduction rate of the Ith recombination center, ν is the thermal velocity of the minority carriers, D is the diffusion coefficient, and φ is the particle fluence. As shown in Equation 1 the specific parameters for each defect are typically lumped into a constant, KL, referred to as the damage coefficient for L. By determining L at various fluences, KL may be determined (Figure 10). The introduction of recombination/generation centers also causes an increase in the dark IV characteristic of the solar cell which degrades Voc. An analysis of dark IV data measured in an irradiated InP solar cell is shown in Figure 11 [6]. The irradiation is seen to cause an increase in the dark IV characteristic. The solid lines in the figure represent fits of the measured data to a theoretical expression for diode dark current. As observed in Figure 6, it is the current at the higher voltages (> ~ 0.65 V in this case) that most strongly affects the photovoltaic output. This portion of the dark IV curve is the diffusion current as given by Shockley [10], in which the magnitude of the diffusion current is shown to be inversely proportional to L. Thus, the introduction of recombination/generation centers by irradiation causes L to degrade which

V - 10

degrades Isc and the dark IV characteristic to increase which degrades Voc. These effects combine to reduce the solar cell Pmp. data determined from QE measurments least squares fit

40

KL = 2.2x10-7 1/L2 (µm-2)

30

20

10

0

n+p InP/Si

0

2

4

6 8 10 12 14 16 18 1 MeV Electron Fluence (x1015 cm-2)

20

22

Figure 10: Minority carrier diffusion length data determined from analysis of QE data as a function of particle fluence (Figure 9). The line represents a linear regression of the data from which the diffusion length damage coefficient, KL, can be determined according to Equation 1.

10-1 10-2 10

-3

10-4

InP/InP solar cell Irradiation with 3 MeV protons Fluence = 1x1013 cm-2

Current (A)

10-5 10-6 10-7 10-8 before irradiation fit to before irradiation data after irradiation fit to after irradiation data

10-9 10-10 10-11 10-12 0.0

0.2

0.4 Voltage (V)

0.6

0.8

Figure 11: Dark current data measured in the InP solar cell from the preceding figures. The proton irradiation causes an increase in the dark current. Analysis of these data shows that this increase is due an increased diffusion current brought on by radiation-induced recombination/generation centers.

The dark IV curve at lower voltages also shows an increase due to the irradiation. This is due to an increase in the recombination/generation current as described in Figure 7, which is the dark current produced by defects within the depleted region of the diode junction [11,12]. The magnitude of this current is directly proportional to the defect concentration and exponentially dependent on the difference between the trap energy level and the intrinsic Fermi level. The closer the trap level lies to mid-bandgap, the more efficient it will be as a

V - 11

recombination/generation center and the larger the recombination/generation current will be. This portion of the dark current is typically several orders of magnitude less than the diffusion dark current, so it has a proportionally smaller effect on the illuminated IV curve. However, at large enough defect concentration levels, the recombination/generation current can become significant, which results in a degradation of the FF. 3 3.1

Modeling Techniques Background

In the previous section, the basic mechanisms controlling the radiation response of crystalline, single-junction solar cells were described. In this section, the techniques employed to model solar cell performance in a space radiation environment will be reviewed. Solar cell degradation in space is caused primarily by incident protons and electrons either trapped in the earth’s radiation belts (the Van Allen belts) or ejected in solar events. These particles have energies that range from near zero up to several hundred MeV. In planning a space mission, engineers need a method of predicting the expected cell degradation in the space radiation environment. This is not a simple calculation because the rate of degradation for a given type of cell depends on the energies of the incident protons and electrons. In addition, the front surface of the cell is usually shielded by coverglass, and the back surface by the substrate material of the cell and the supporting array structure, so that the incident particle spectrum is ‘slowed down’ before it impinges on the active regions of the cell. Finally, as observed in Figure 8, different cell technologies have their own specific radiation response characteristics depending on the solar cell material, the thickness of the active regions, and the types and concentrations of dopants employed. In any method to predict solar cell response in a space radiation environment, several steps are necessary. First, a way is needed to correlate the degradation caused by particles of different energies, i.e. the energy dependence of the cell damage coefficients must be determined. Second, the radiation environment needs to be accurately specified, including the effects of any shielding materials present. Finally, a method must be found to convolve the energy dependence of the damage coefficients with the radiation environment for the duration of the mission in a way that allows comparison with ground test data. The discussion in this section will describe methods for correlating solar cell radiation damage. In the section to follow, it will be shown how these data are used to make on-orbit performance predictions for solar arrays. There are currently two main approaches being used to model solar cell degradation in space. The first method was developed at the U.S. Jet Propulsion Laboratory (JPL) and has been described in four extensive NASA publications [4,5,13,14]. The goal of this approach is the determination of the normal-incidence 1 MeV electron fluence, which produces the same level of damage to the cell as a specified space radiation environment. The JPL method has been used successfully over a number of years by many workers in the space community and serves as the present industry standard. JPL first published a comprehensive description of the equivalent fluence method in 1982 in the Solar Cell Radiation Handbook [4]. The model was applicable to several kinds of Si solar cells having average initial efficiencies of ~13% (AM0, 1 sun, 25oC). An appendix was included in the handbook containing a FORTRAN program entitled EQFLUX, which enabled predictions of Si cell degradation in a space environment to be made for the short V - 12

circuit current (Isc), open circuit voltage (Voc), and maximum power (Pmp). The user was required to enter an input file, which specified the incident omnidirectional radiation spectrum for the mission in question. The program incorporated experimentally determined relative damage coefficients (RDCs) for bare cells for normally incident electrons and protons. The calculations were updated in 1984 and 1989 to take into account newer types of Si cells as well as LPE GaAs cells, which were just coming into use [13,14]. The results for Si cells were deemed to be in substantial agreement with the earlier work, with only minor modifications being required. In 1996, JPL published the GaAs Solar Cell Radiation Handbook [5], which contained a similar analysis for the widely used GaAs/Ge solar cells (1990 vintage, grown by Applied Solar Energy Corporation). These cells had initial efficiencies of ~18% (AM0, 1 sun, 25oC). An appendix contained a FORTRAN code entitled EQGAFLUX, which was similar to EQFLUX but with results applicable to GaAs/Ge cells. EQFLUX and EQGAFLUX have been used widely by the space power community to make satellite end-of-life (EOL) power predictions. The second method was developed over the past decade at the U.S. Naval Research Laboratory (NRL) and has been described in a series of publications and conference proceedings [15,16,17,18,19,20,21]. The essence of the NRL method is the calculation of the displacement damage dose (Dd) for a given mission. The Dd is calculated from the nonionizing energy loss (NIEL) for protons and electrons traversing the cell material. This analysis draws on studies in which calculations of the NIEL were used to correlate displacement damage effects in microelectronic devices. The so-called ‘NIEL approach’ to correlating displacement damage effects is now widely used, especially in the particle detector field. For solar cell radiation response analysis, NRL and NASA have been working together [22] under NASA funding [23] to produce the Solar Array Verification and Analysis Tool (SAVNT), which is WindowsTM based program that implements the NRL method [24]. The NIEL is the rate of nonionizing energy loss of an irradiating particle in a material. As such, the NIEL indicates the rate at which energy from the incident particle is transferred to displacements in the target material and is expressed in terms of units of energy per unit of material (MeV/g). It is a calculated quantity involving interaction cross sections and recoil kinematics for different kinds of incident particles and target materials. It is the direct analog for displacement damage of the stopping power (or linear energy transfer, i.e., LET) used to describe ionization effects [16]. The NRL work showed that the radiation response of crystalline solar cells is determined only by the energy deposited into atomic displacements, just as the response of many biological and other systems to ionization depends only on the deposited ionizing dose. (The distinction of crystalline is used here since, as will be seen in a later section, ionization and not displacement damage may be the dominant degradation mechanism in non-crystalline devices.) The analogy was taken further with the establishment of a new quantity, i.e., the displacement damage dose (usually represented by Dd). Although the basic units of dose are energy per unit mass, ionizing dose is usually measured in rads(X), where X is the material in question. However, there is as yet no special unit for displacement damage dose and the usual unit employed is MeV/g. Displacement damage dose enables a characteristic degradation curve for crystalline solar cells to be determined, which is independent of the incident particle.

V - 13

Particle-induced degradation in GaAs/Ge cells will now be used to illustrate both the JPL and NRL methods, beginning first with the JPL approach. Note that the present discussion, for the most part, assumes the irradiating particle produces uniform damage as it passes through the solar cell, which is the case when the irradiating particle energy remains essentially constant as it passes through the solar cell active region. For electrons, this is true for all of the cases of interest here. The situation is much different for protons. The lower the proton energy, the greater the energy loss as the proton traverses the cell, so there is a lower energy limit below which care must be taken in applying the analyses to be presented. The value of this limit is dependent on the solar cell material and the thickness of the solar cell. A detailed discussion of the analysis of lower energy proton irradiation will be addressed in Section 7.2. 3.2 The JPL Method A flow chart describing the steps involved in the JPL method is shown in Figure 12. In the JPL method the RDCs for shielded cells are calculated from experimentally determined values for incident particles impinging perpendicularly on uncovered cells. It is, therefore, first necessary to measure degradation curves for all the typical photovoltaic parameters over a wide range of energies for both protons and electrons. In the case of the GaAs/Ge cells reported in the GaAs Solar Cell Radiation Handbook [5,25], these measurements included four electron energies (0.6, 1, 2.4, and 12 MeV) and eight proton energies (0.05, 0.1, 0.2, 0.3, 0.5, 1, 3, and 9.5 MeV). For each incident particle, eight fluence steps were typically required to generate a sufficiently detailed degradation curve and, to obtain good statistics for each fluence level, several cells needed to be irradiated and measured. It can be seen that many hundreds of current-voltage measurements were required to generate the necessary data. As an example, the results for Pmp are shown in Figure 13 [25]. It should be noted that the symbols shown in Figure 13 are not original data points as measured by Anspaugh. They are points read off the lines drawn on the figures shown in Reference [25].

JPL Equivalent Fluence Method Measure PV Degradation Curves (~4 electron and ~8 proton energies)

Determine Incident Particle Spectrum (e.g. AP8)

Determine Damage Coefficients for Uncovered Cells

1 MeV Electron Degradation Curve

Calculate Damage Coefficients for Isotropic Particles w/ Coverglasses of Varied Thickness

Calculate Equivalent 1 MeV Electron Fluence for Orbit (EQGAFLUX)

Read Off EOL Values

Figure 12: Flowchart describing the JPL equivalent fluence model for space solar cell end-of-life (EOL) prediction.

V - 14

The RDCs are determined from the measured data in the following way. For each incident particle type, the fluence at which a particular photovoltaic parameter is reduced to 75% of its initial value is determined from the respective degradation curve. These so-called ‘critical fluences’ are found for normally incident protons and electrons at all measured energies. The ratio of the critical fluence for 10 MeV protons to the critical fluence for other proton energies is then taken as a measure of the RDCs for protons. Similarly for the case of normally incident electrons, the critical fluences for different electron energies are normalized against the effect of 1 MeV electrons. The normalized values for all proton and electron energies are then plotted versus particle energy, giving the energy dependence of the RDCs (Figure 14). Using these RDC values, the proton fluence data from Figure 13 can be converted to an equivalent 10 MeV proton fluence, and the electron data can be converted to an equivalent 1 MeV electron fluence as shown in Figure 15. All of the radiation data are now seen to be correlated, as the electron and proton data have been reduced to a single curve for each dataset. To bring the electron and proton datasets into alignment, an empirically determined parameter is used. This is referred to as the ‘proton to electron damage equivalency ratio’, and is here given the symbol Dpe. This is an experimentally determined parameter that converts the 10 MeV proton fluence to an equivalent 1 MeV electron fluence. In the case of Si cells, Dpe has the same value for each of the photovoltaic parameters, i.e. ~3,000 [4]. However, for GaAs cells, Dpe has a different value for each parameter, and the result of the calculation is, therefore, different in each case (Isc = 400, Voc = 1400, Pmp = 1000 [25]). It should be noted that the degradation curves for different proton and electron energies are not exactly parallel (Figure 13), so that slightly different RDCs would be obtained if the critical fluences were taken when the degradation was different than 75%. However, this effect has a relatively small influence on the final results of the calculations.

Normalized Pmp

1.0

0.8

Proton Energy (MeV)

0.6

0.2 0.3 0.5 1 3 9.5

0.4

Electron Energy (MeV) 12 2.4 1 0.6

0.2 10

8

10

9

10

10

10

11

12

13

10 10 10 -2 Particle Fluence (cm )

14

10

15

10

16

10

17

Figure 13: Electron and proton Pmp degradation of GaAs/Ge solar cells as a function of fluence [5,25]. The points shown on the curves were read off the lines drawn on the figures shown in Reference [25].

V - 15

Electron Proton

1

10

0

RDCs

10

0.1

1

10

Energy (MeV)

Figure 14: Relative damage coefficients for normal incidence proton and electron irradiation of bare GaAs/Ge solar cells [25]. The data point at 50 MeV proton was taken from GaAs cells of an earlier vintage (1987) [5]. -2

Equivalent 1 MeV Electron Fluence (cm )

Normalized Pmp

1.0

0.8

Proton Energy (MeV)

0.2 0.3 0.5 1 3 9.5

0.6

0.4

Electron Energy (MeV) 12 2.4 1 0.6

0.2 10

9

10

10

11

12

13

14

10 10 10 10 10 -2 Equivalent 10 MeV Proton Fluence (cm )

15

10

16

Figure 15: GaAs Pmp degradation data from Figure 13 plotted as a function of equivalent fluence (1 MeV for electrons and 10 MeV for protons) determined using the RDCs of Figure 14.

With the RDCs so determined, the response of a given solar cell to irradiation by a spectrum of particles can be calculated. The particle spectrum must be expressed in differential form (dΦe(E)/dE and dΦp(E)/dE). The differential particle spectrum is then integrated with the appropriate damage coefficients (RDCe(E) and RDCp(E)) to determine an equivalent 1 MeV electron fluence for the spectrum in question. These integrals are shown in Equation 2 and Equation 3 for the cases of electrons and protons, respectively. The results of the integrals are

V - 16

the fluences of 1 MeV electrons that would cause the same damage as irradiation by the particle spectrum. The total equivalent 1 MeV electron fluence in a mixed proton and electron environment is obtained by adding the results. Once the total 1 MeV electron fluence equivalent to the particle spectrum is calculated, the predicted degradation can be obtained for each photovoltaic parameter from the experimentally determined 1 MeV electron degradation curves. In EQGAFLUX and EQFLUX, the 1 MeV electron degradation curves are fit to a power expression in natural logarithms out to fifth order. Equation 2

Φ 1MeV electrons, electrons = ∫

dΦ e ( E ) RDC e (E) dE dE

Equation 3

Φ 1MeV electrons, protons = D ep ∫

dΦ p (E) dE

RDC p (E) dE

The next step is apply this analysis to a solar array in a space environment which involves adapting the RDCs for normally incident particles on bare cells to the case of omnidirectional particles incident upon cells with coverglass and mounted on supporting solar array substrates. This will be performed in Section 4. 3.3 NRL Method A flow chart describing the steps involved in the NRL method for predicting radiation-induced solar cell degradation is shown in Figure 16. In this case, the first step is to calculate the NIEL for electrons and protons incident on the solar cell material of interest. The steps involved in the calculations are described in detail in References 16, 17, 18, 26, and 27, and only a relatively brief outline of the calculations will be given here. The calculations determine the energy transferred to the target atoms as a result of several possible interactions with incident protons or electrons. In order to perform the calculations, accurate values for the differential cross sections for Rutherford, nuclear elastic, and nuclear inelastic interactions are needed. For incident protons, Rutherford scattering dominates over the energy range extending from the threshold for atomic displacement (usually about several hundred eV) up to a few MeV. These differential scattering cross sections are quite accurately known from analytical calculations. The nuclear elastic component becomes important above about 1 MeV. Several models for nuclear elastic cross sections have been developed. The NRL group used experimentally determined values in the early calculations of the NIEL, but more recently an optical model approach has been employed. For energies >60 MeV, nuclear inelastic interactions become important. These have to be treated using one of several approximate models. At the moment, the nuclear inelastic interactions are probably the least satisfactory part of the overall calculation. Fortunately, for most cell types shielded with coverglass in typical space proton environments, only transmitted protons with energies up to ~10 MeV make significant contributions to the overall damage to the cell [16,17,18]. This is fortuitous since Rutherford scattering is generally the most accurate part of the overall calculation.

V - 17

NRL Displacement Damage Dose Method Determine Incident Particle Spectrum (e.g. AP8)

Calculate Nonionizing Energy Loss (NIEL) (Energy Dependence of Damage Coefficients)

Calculate Slowed-Down Spectrum (SDS) (Shielding) Measure Characteristic Degradation Curve vs. Dd (Dd=NIELxFluence) (2 e- and 1 p+ energy)

Calculate Dd for Mission (Integrate SDS with NIEL)

Read Off EOL Value

Figure 16: Flowchart describing the NRL displacement damage dose model for space solar cell damage prediction. Only ground-based data at three particle energies are needed.

Over the range of energies important for electron interactions in space, only Coulombic interactions are important and these can be calculated very accurately using the differential scattering cross section developed originally by Mott [28]. Nuclear contributions to the electron NIEL only become important for much higher energies than are typically important in space spectra. In order to calculate the energy dependence of the NIEL, the differential scattering cross sections are combined with the total recoil energy of the target atoms, which is partitioned into ionizing and nonionizing components by the so called Lindhard factor. The result is then integrated over solid angle. As an example, Figure 17 shows the calculated energy dependence of the electron and proton NIEL for GaAs over the energy range from zero up to 200 MeV. In these calculations, the threshold energies for atomic displacements for both Ga and As were taken as 10 eV [5]. It can be seen in Figure 17 that the electron NIEL increases with energy, whereas the proton NIEL generally decreases with increasing energy except close to the threshold. This is energy dependence can be seen qualitatively in the data shown in Figure 13.

V - 18

RDCs

10

3

10

2

10

1

10

0

10

-1

10

-2

10

-3

10

-4

10

-5

10

-6

10

-7

Proton

Electron

10

-5

10

-4

10

-3

10

-2

10

-1

10

0

10

1

10

2

10

3

10

4

Energy (MeV)

Figure 17: Proton and electron NIEL in GaAs.

It is a notable feature of Figure 13 that all the curves, except those for protons with energy 5x109 MeV/g (Figure 41). Measurements showed the Voc degradation to be due to a more rapid radiationinduced increase in dark current in the 22% In cells. This may be a direct result of the higher In content in those cells and, hence, larger lattice mismatch. However, the Isc response suggests that differences in cell structure also significantly impact the radiation response. The 17% In cells, which were of a p-i-n and n-i-p structure, displayed a much better blue response before irradiation, which was nearly insensitive to the irradiation, and after irradiation, those cells showed a better spectral response at nearly all wavelengths (Figure 42 [53]). This can be

V - 43

explained by the enhanced collection efficiency afforded by the intrinsic layer of these cells and to a better front and rear interface passivation scheme. From these results, it can be concluded that, within the range of In concentrations studied, the response of these cells are more strongly controlled by the cell structure than the In concentration. 1.0

Normalized Pmp

0.9

0.8

n/p In0.22Ga0.78As (a) p-i-n In0.17Ga0.83As (b) n-i-p In0.17Ga0.83As (c)

0.7

(d)

p/n GaAs (d)

(c) (b) (a)

0.6

0.5 8

9

10

10

10

10

Dd (MeV/g)

Figure 41:. Radiation-response of single-junction InyGa1-yAs solar cells with different stoichiometries. The p/n GaAs data are from [5] and the InGaAs data are from [53]). 1.0


0.8

0.6

0.4

n/p In0.22Ga0.88As n-i-p In0.17Ga0.83As open symbols are after 10 irradiation Dd ~ 4x10 MeV/g

0.2

0.0 400

600

800

1000

1200

Wavelength (nm)

Figure 42: Radiation-response of the QE of two single-junction InyGa1-yAs solar cells [53].

The radiation response of several dual junction InxGa1-xP/InyGa1-yAs devices are shown in Figure 43 [54]. The n/p InGaP2/GaAs device is an EOL optimized cell developed under the ManTech program [51]. Except for the In0.51Ga0.49P/In0.03Ga0.97As cell in the n/p structure, the cells show generally similar radiation characteristics, independent of the In concentration. The n/p V - 44

In0.51Ga0.49P/In0.03Ga0.97As cell was optimized for terrestrial use under AM1.5 illumination, so the middle cell base thickness and dopant level were larger than optimal for good radiation resistance. These results are similar to those of the single junction GayIn1-yAs cells, and again suggest that the cell structure may have considerably more affect on the radiation response than the In concentration 1.0

1.0

(a)

(a) 0.9

0.9

Normalized Isc

p-i-n In0.17Ga0.83As (b)

0.8

(d)

n-i-p In0.17Ga0.83As (c)

(c)

p/n GaAs (d) 0.7

Normalized Voc

(b)

n/p In0.22Ga0.78As (a)

(b) (c) 0.8

(d)

n/p In0.22Ga0.78As (a) p-i-n In0.17Ga0.83As (b) n-i-p In0.17Ga0.83As (c)

0.7

p/n GaAs (d)

0.6

0.6

0.5

0.5 10

8

10

9

10

8

10

10

10

9

10

10

Dd (MeV/g)

Dd (MeV/g)

(a)

(b)

1.0

(d) Normalized Pmp

(b) (a) 0.8

(c) n-i-p In0.65Ga0.35P/In0.17Ga0.83As (a) n-i-p In0.51Ga0.49P/In0.03Ga0.97As (b) n/p In0.51Ga0.49P/In0.03Ga0.97As (c)

0.6

n/p InGaP2/GaAs (d) 7

10

8

10

10

9

10

10

11

10

Dd (MeV/g)

(c) Figure 43: Comparison of the radiation-response of several dual junction InxGa1-xP/InyGa1-yAs solar cells [54] (The n/p InGaP2/GaAs data are from [51].)

The advantages of the EOL optimized InxGa1-xP/InyGa1-yAs 2J cell can be seen through a study of the Isc response of the cells (Figure 44). At low Dd levels, the Isc of the EOL optimized n/p InGaP2/GaAs cell is limited by the top cell, and as such, degrades little since the top cell is quite resistant to irradiation. As the bottom cell degrades at higher Dd levels, the dual-junction device transitions to being bottom cell limited. At this point, the dual-junction Isc degradation curve turns over and rapidly degrades down to the level of the other cells. These results clearly demonstrate that, like the InGaP2/GaAs technology, the GayIn1-yAs sub-cell cell primarily controls the radiation response of the MJ GaxIn1-xP/GayIn1-yAs devices. In Figure 45, the normalized degradation of the Isc of the GaxIn1-xP top and GayIn1-yAs bottom cells are

V - 45

shown independently [54]. These data were calculated by integrating the spectral response of each sub-cell over the energy dependence of the AM0 spectrum. The degradation of all the GaxIn1-xP cells can be seen to be small up to high damage levels, independent of the stoichiometry. The GayIn1-yAs cells, on the other hand, degrade significantly. Also, a significant difference is observed in the degradation of the different GayIn1-yAs bottom cells. Since two cell structures, each with y = 0.03, show significantly different behavior, this difference cannot be attributed only to the cell stoichiometry. Again, the cell response appears to be more strongly controlled by the cell structure. The n-i-p Ga0.97In0.03As sub-cell benefits from the increased carrier collection efficiency afforded by the extended electric field of the intrinsic region. Also, in contrast to the n-i-p Ga0.97In0.03As, which was designed for AM0 operation, the n/p Ga0.97In0.03As cell was designed from AM1.5 operation, so the base dopant level was relatively high in that cell (~ 2x1017 cm-3), which results in a lower radiation resistance.

1.0

Normalized Isc

(d)

(a)

0.8

(b) (c) n-i-p In0.65Ga0.35P/In0.17Ga0.83As (a) n-i-p In0.51Ga0.49P/In0.03Ga0.97As (b) n/p In0.51Ga0.49P/In0.03Ga0.97As (c)

0.6

n/p InGaP2/GaAs (d) 7

10

10

8

10

9

10

10

10

11

Dd (MeV/g)

Figure 44: Comparison of Isc radiation-response in InxGa1-xP/InyGa1-yAs solar cells [54] (The n/p InGaP2/GaAs data are from [51].)

V - 46

Calculated Photocurrent (norm.)

1.0 0.9 0.8 0.7 0.6

(Fraunhofer ISE) In0.65Ga0.35P top cell In0.17Ga0.83As bottom cell In0.49Ga0.51P top cell In0.03Ga0.97As bottom cell

0.5 (J-Energy Corp.) 0.4 0.3 108

In0.49Ga0.51P top cell In0.03Ga0.97As bottom cell

109

1010

1011

1012

Displacement Damage Dose (MeV/g)

Figure 45: Comparison of the radiation-response of photocurrent of the top and bottom cells of the InxGa1-xP/InyGa1-yAs solar cells of Figure 44 [54]. The legend indicates the group that produced the solar cells. The data were calculated from the QE data of these DJ solar cells.

This analysis of the radiation response of MJ GaxIn1-xP/GayIn1-yAs devices suggests that, contrary to initial speculation, the radiation-response of the GayIn1-yAs-based devices is quite good and essentially independent of In content. Instead, it is the cell structure that more significantly controls the radiation-response, and it has been shown how the cell structure may be optimized for maximum BOL and EOL performance that is equal to or better than conventional Ga0.49In0.51P/GaAs cells. 5.2.2 Modeling Multijunction Solar Cell Radiation Response In Section 3, methods for modeling the radiation response of a SJ GaAs/Ge solar cell were presented. This model will now be extended to include the MJ GaxIn1-xP/GayIn1-yAs/Ge devices. It has already been demonstrated that proton irradiation data at various energies measured in the GaxIn1-xP/GayIn1-yAs devices discussed in the preceding section can be correlated in terms of Dd [22,52,53,54]. Indeed, most of the data presented in the preceding section were expressed in terms of Dd. Therefore, both the JPL and the NRL modeling techniques will apply to those devices, by definition. Nevertheless, it is instructive to demonstrate the data correlation explicitly. This can be done in a simple fashion by adding RDC data for the MJ cells to Figure 23. This is done in Figure 46 where the RDC data generated by Marvin for the MJ cells developed in ManTech program [51] have been used. The SJ data are those of Figure 23, which are taken from [5].

V - 47

Norm NIEL and RDCs

10

2

10

1

10

0

10

Proton NIEL SJ RDC 2J RDC 3J RDC Electron NIEL (1.7 power) SJ RDC 2J RDC 3J RDC

-1

10

-2

10

-1

10

0

10

1

10

2

10

3

Energy (MeV)

Figure 46: RDCs for SJ GaAs/Ge [5], 2J InGaP2/GaAs, and 3J InGaP2/GaAs/Ge [51] solar cells plotted along with the GaAs NIEL. To be consistent with the RDCs, the proton NIEL has been normalized to the value at 10 MeV and the electron data to 1 MeV. The electron NIEL was raised to the 1.7 power according to the n value determined for the SJ cells. The correlation of the RDCs with NIEL indicates that the Dd analysis methodology is applicable to the MJ cell technologies.

The fact that the RDCs correlate with the NIEL in Figure 46 indicates that the Dd methodology is applicable to these MJ devices. It should be noted that in Figure 46, the electron NIEL has been raised to the 1.7 power in accordance with the n value determined for the SJ GaAs/Ge dataset (Table 1). For the 2J and 3J data, values of 1.09 and 1 were determined for n, respectively [55]. The correlation of the electron RDCs with the normalized NIEL would be closer if these n values had been applied for each individual dataset. The behavior of the RDCs at very low proton energies can be explained in terms of the finite range of these protons in the solar cell devices under study. This will be investigated in detail in Section 7.2. To facilitate on-orbit performance predictions for these MJ cell technologies, values of C and Dx have been determined. These were found by fitting the radiation data presented in [51] to Equation 8. Table 3: These are the fitting parameters found from fitting the3J solar cell data [51] to Equation 8.

N C Dxe (MeV/g) Dxp (MeV/g) Rep BOL Eff (%)

3J InGaP/GaAs/Ge 1.6 0.295 9.83x109 3.08 x109 3.19 24.7

V - 48

5.3 Thin Film Photovoltaics Thin-film photovoltaics (TFPV) on flexible substrates are considered to be an enabling technology for several next generation space power systems. These technologies offer the advantages of low cost, light-weight, and radiation resistance. TFPV devices have been shown to be much more radiation resistant to proton and electron irradiation than their crystalline material counterparts, because TFPV devices have shown extremely low annealing temperatures suggesting that in actual operation, the net radiation degradation will be minimal. However, the technologies remain at a low level of maturity, and significant research and development is required to optimize the device performance and take full advantage of the advanced material properties. The two primary TFPV materials currently under development are amorphous Si (aSi) and CuIn(Ga)Se2 (CIGS). In this section, an understanding of the basic radiation degradation mechanisms and the processes by which the devices recover from the radiation damage will be developed. 5.3.1 Amorphous Si 5.3.1.1 Amorphous Si Radiation Response Amorphous Si solar cell technology was patented [56] and described by Carlson [57] in 1977. A good review of this technology was given by Guha and Yang [58]. Amorphous Si solar cells are typically grown by decomposition of silane at a temperature between 100 and 300oC using a variety of substrates with stainless steel and polyimide being the primary substrates of interest for space applications to enable lightweight, flexible arrays. An a-Si solar cell is typically a p-i-n structure, where most of the photocurrent generation occurs in the intrinsic layer. The a-Si material, as expected, is characterized by a high degree of disorder which creates wide bandtails in the forbidden gap and structural defects like dangling, strained, and weak bonds. These bandtails and defects act as recombination centers. In addition, it has been observed that illumination of a-Si solar cells leads to the creation of metastable defects that degrade the photovoltaic output (the so-called Staebler-Wronski Effect [ 59 ]). Thus, the challenge for attaining good-quality a-Si solar cell devices lies in the minimization of the concentration of recombination centers in the as-grown material and devising methods for stabilizing the material. The key factor that has been found to reduce the effect of as-grown defects is the use of hydrogen dilution in the gas mixture during growth [60]. The current understanding is that hydrogenated material is more ordered because the excess hydrogen passivates dangling bonds at the growing surface allowing the impinging species to bond in more energetically favorable sites. Stability issues have been addressed by making the absorber layers thinner thereby reducing the distance over which the charge carrier must migrate to be collected. The absorber layers needed to be so thin, however, that the cells suffered from incomplete photon absorption, so a multijunction approach has been adopted. The highest efficiencies have been achieved with a triple junction device (Figure 47) where the cell active layers are less than 1 µm thick in total. QE measurements made on a 3J a-Si cell are shown in Figure 48 with the AM0 spectrum shown for reference. The bandgap of the individual junctions are controlled by varying the growth parameters of the a-Si alloy and by forming a-Si – Ge alloys with varying Ge concentrations. The top junction is typically a-Si with a bandgap ~1.8 eV. The middle junction is typically a 1020% Ge a-Si(Ge) alloy with a bandgap of ~1.6eV. The bottom junction is typically a 40-50% Ge

V - 49

a-Si(Ge) alloy with a bandgap ~1.4eV. Stable, AM0 efficiencies around 10% can be routinely achieved with these devices, and a representative IV curve is shown in Figure 49. ITO - TCO p-i-n a-Si p-i-n a-Si(Ge), 20% p-i-n a-Si(Ge), 40% stainless steel – 5 mils

Figure 47: This is a schematic diagram of a triple-junction a-Si solar cell grown on a stainless steel substrate. The entire 3J stack above the stainless steel substrate is less than 1 um thick. 1.0 Irradiation was 0.5 MeV electrons up to 6x1015cm-2 Anneal was 24 hrs at 70oC in the dark Before Irradiation After Irradiation After Annealing AM0 spectrum (normalized)

0.8

External QE

a-Si(Ge), ~1.6eV

0.6 a-Si, ~1.8eV

0.4 a-Si(Ge), ~ 1.4eV

0.2

0.0

300

675 Wavelength (µm)

1050

Figure 48: These are external quantum efficiency (QE) data measured on a TJ a-Si solar cell. The response of each individual sub-cell is evident. The AM0 solar spectrum, normalized to its maximum value is shown for reference. This cell was irradiated with 0.5 MeV electrons up to a fluence of 6x1015 cm-2 and then annealed for 24 hours at 70oC in the dark and unbiased, and data measured at each stage are shown.

V - 50

0 a-Si

Current (mA)

-3

Area - 1.1 cm2 Isc - 10.1 mA/cm2 Voc - 2.30 V

-6

Imp - 8.6 mA/cm2 Vmp - 1.78 V Pmp - 15.3 mW/cm2 FF - 0.66 Eff - 11.21%

-9

-12 0.0

0.5

1.0

1.5 Voltage (V)

2.0

2.5

Figure 49: This is an IV curve measured on a 3J a-Si solar cell that is generally representative of this technology.

The first evaluations of a-Si solar cells for use in space appeared in the early 1980’s [61, 62]. These early studies were performed on single-junction (SJ) cells. Both electron and proton irradiations were performed, and degradation in short circuit current (Isc) was found to be the primary effect with little degradation observed in open circuit voltage (Voc). The Isc degradation was correlated with a degradation of the cell QE which was attributed to degradation in the minority carrier diffusion length (L) and possibly to surface deterioration. Significant annealing capacity of the a-Si cells was reported although the annealing temperatures employed in these early studies were rather high (>150oC). The degradation of L was said to be most likely due to displacements of hydrogen atoms while the annealing was attributed to the mobility of the hydrogen within the a-Si material. It was speculated that the annealing was likely occurring during the irradiation and that the annealing was controlled by a distribution of thermal barriers characterized by a distribution of annealing coefficients. During the latter half of the 1980’s, a-Si solar cells continued to be developed for terrestrial use such that, by around 1990 the a-Si conversion efficiencies were approaching that of the crystalline Si solar cells being used on space solar arrays. Therefore, the US Air Force funded research to again evaluate a-Si solar cells for space use. As part of this effort, Mueller and Anspaugh produced the first comprehensive study of the radiation response of a-Si solar cells [63]. They studied the electron and proton radiation response and annealing characteristics of 3J a-Si solar cells produced by Solarex. In agreement with the earlier work, this study showed electron irradiation to cause degradation in Isc and Voc that was readily removed by annealing. Muller and Anspaugh went further by showing significant annealing to occur at temperatures as low as 20oC. In contrast to the earlier work, however, Muller and Anspaugh showed very little proton irradiation-induced degradation except for the case where the protons stopped within the active region of the solar cell. In all cases, the proton degradation annealed at low temperatures. Muller and Anspaugh did point out the very interesting result that the damage induced by electron irradiation increased with decreasing electron energy, which is in disagreement with displacement damage theory. This suggests that more than just displacement damage is responsible for the a-Si solar cell response.

V - 51

Beyond the work of Muller and Anspaugh [63], progress in the understanding of the radiation response of a-Si solar cells was lead primarily by research performed by TRW [ 64 and references therein] and Wayne State University under TRW and NASA funding [65], as well as the ongoing joint AFRL/NRL research reported in [66,67] and being presented here. From analysis of the evolution of the junction dark current in SJ a-Si solar cells under proton and electron irradiation, Lord [65] and Wang [68] suggested that the degradation was primarily due to a reduction in the electric field in the intrinsic layer. This can explain the radiation-induced decrease in the dark IV characteristic of 3J a-Si cells (Figure 50) because the decrease can be modeled by a reduction in depletion region dark current due to a reduction in the junction electric field. In terms of the solar cell PV parameters, the reduction in the junction field can also explain the reduction in Isc after irradiation. Lord proposed the mechanism for this to be compensation of the material by radiation-induced defects, i.e. displacement damage effects [65]. However, further research by the Wayne State group observed similar degradation after 40 keV electron irradiation, which is too low an energy to produce displacements [68]. Indeed, returning to the data of Muller and Anspaugh [63], the fact that the degradation increases with decreasing electron energy cannot be explained by displacement damage effects alone. 10-1 Pre-rad

Current (A)

10-2 10-3 10-4 10-5

Post-anneal

10-6 10

Post-rad

a-Si solar cell

-7

500 keV electrons, 4x1015 cm-2

10-8 10

24 hrs, 70oC anneal

-9

0.0

0.5

1.0 1.5 2.0 Wavelength (nm)

2.5

Figure 50: These are dark current data measured in a 3J a-Si solar cell before and after electron irradiation and after annealing. The irradiation causes a decrease in the junction dark current. The annealing restores a significant percentage of the dark current.

A resolution of this result has been proposed by Srour et al. based on a comparison of proton, electron, and x-ray irradiation of 2J a-Si solar cells [64]. Their study showed that 10 keV x-ray irradiation produced the same degradation of the a-Si cells as electron and proton irradiation. This suggests that it is ionization effects that mainly control the device response, as they were able to correlate the damage produced by the various irradiations in terms of the equivalent ionizing dose. This is not to say that displacement damage effects could be completely ignored especially in cases where the irradiation causes large amounts of displacement damage, e.g. low energy protons that stop in the device active region [64], but their analysis showed that for essentially all realistic space environments, the a-Si solar cell degradation will be dominated by ionizing energy deposition. This analysis fits with the degradation model of Lord et al. [65] if one assumes that the ionization-induced charge is captured by pre-existing defects in the intrinsic region and that radiation-induced displacement damage defects play a role only at high damage levels.

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While the analysis of Srour et al. [64] puts forth a compelling argument, the understanding of the mechanisms controlling the a-Si radiation response is not complete. This can be seen in the present data of the response of 3J a-Si solar cells to electron irradiation at three different energies plotted as a function of absorbed ionizing dose (Figure 51). If the damage were due to absorbed ionizing dose, then the data would be expected to fall on a single curve when plotted against total dose, but they do not. Because the space environment consists of a spectrum of electrons, this has significant implications on the ability to predict on-orbit a-Si solar cell performance. The lack of correlation may be attributed to varied ionization rates within each sub-cell. The middle and bottom sub-cells have increasing amounts of Ge, and the sub-cells showed varied amounts of radiation-induced degradation as can be seen in the QE data (Figure 48). It is interesting to note that the degradation increased with increasing Ge content. As a final note, the present theory has been developed based primarily on analysis of the Isc degradation, but it is fill factor (FF) degradation that dominates the a-Si solar cell response, especially under proton irradiation [66,67]. To fit with the model as presented, the FF degradation might be attributed to an increase in bulk resistivity due to the decrease in conductivity through the intrinsic region, but more study is required to substantiate this.

Electron Irradiation a-Si Pmp (mW/cm2)

15

10

0.5 MeV 1 MeV 1.5 MeV 0.5 MeV after anneal

5

0

106

107 108 Ionizing Dose (rad(Si))

109

Figure 51: These are Pmp data measured in a-Si solar cells under irradiation by electrons of different energies plotted in terms of absorbed ionizing dose. The closed symbols represent data from cells that were not subjected to annealing. The open symbols represent data from cells that were subjected to a 24 hr, 70oC exposure after each incremental fluence. At the highest fluence, the annealing time was 43 hrs.

5.3.1.2 Amorphous Si Annealing Characteristics In Figure 48 and Figure 50, the effects of a 24 hr anneal in the dark at 70oC on an irradiated a-Si solar cell are shown. The annealing is seen to efficiently remove the radiation-induced degradation in both the QE and dark current. Similar recovery was observed in the FF such that substantial recovery was observed in maximum power (Pmp) (Figure 51). The data shown in Figure 51 were obtained by irradiating a single set of solar cells with incrementally increasing fluences of electrons. After each irradiation, some cells were exposed to a 24 hour anneal which

V - 53

resulted in a reduction in the amount of degradation. Furthermore, at the highest fluence, the anneal time was extended to 43 hrs, and more recovery was observed suggesting that full recovery may be attainable after sufficient long annealing times or higher annealing temperatures. Given these results, it was postulated that for an a-Si array in a real space environment, which is characterized by a low flux and an operating temperature of about 70oC, the a-Si solar cells would show essentially no degradation on orbit. United Solar Systems Corporation and Fokker Space teamed to test this theory [69]. They exposed 3J a-Si solar cells to a very low flux, 1 MeV electron irradiation at 70oC, and indeed, the cells showed very little degradation. Their data are shown in Figure 52 along with the data from Figure 51 and data from crystalline Si [4] and GaAs [5] for comparison. From this comparison, it is clear that when the annealing behavior is considered, the a-Si technology displays extreme radiation hardness. As pointed out by Srour et al. [64], this behavior can be explained in terms of the degradation model described above since significant recovery at relatively low annealing temperatures is consistent with the liberation of trapped charge as opposed to the annealing of displacement damage defects. However, just as the case with the development of the degradation model, more research is needed to fully understand the mechanisms for this annealing behavior. 1 MeV Electron Irradiation a-Si

Pmp (mW/cm2)

20

15

10 High Flux, 28oC High Flux, 28oC annealed Low Flux, 70oC crystalline Si GaAs/Ge

5

0

1014

1015 Fluence (cm-2)

1016

Figure 52: These are data from a-Si solar cells compared to data from crystalline Si [4] and GaAs/Ge [5]. Note that the data are plotted as a function of 1 MeV electron fluence. The two data sets labeled “High Flux” are from Figure 51. The data labeled “Low Flux” were taken under very low flux irradiations while the cells were held at 70oC [69].

5.3.2 CuIn(Ga)Se2 5.3.2.1 CuIn(Ga)Se2 Radiation Response CIGS solar cells are typically heterojunction devices formed when CdS is deposited on a layer of CuIn(Ga)Se2 (Figure 53). The CIGS layer is typically 1 – 2 µm thick while the CdS is typically on the order of 50 nm thick with a ~ 50 µm layer of ZnO serving as an antireflective coating and a passivation layer. The ZnO is usually coated with a layer of Indium Tin Oxide (ITO) which serves as a transparent conducting oxide (TCO). Sunlight enters the cell through the ZnO and is absorbed almost exclusively in the CIGS layer. One of the primary advantages of CIGS solar

V - 54

cells is that they may be grown on a variety of substrates. The highest efficiencies have been achieved with cells grown on soda-lime glass substrates [70]; however, for space applications, the focus has been on the growth of cells on lightweight and flexible substrates like stainless steel and Kapton [71,72, 73]. ITO TCO top contact (0.6 um) ZnO buffer layer (0.04 um) CdS (0.1 um) CuIn(Ga)Se2 (2 um) Molybdenum back contact (0.2 um) Substrate

Figure 53: This is a schematic diagram of a typical CIGS solar cell. The thickness given are approximate and vary amongst growth techniques and manufacturers. Various substrates are used ranging from glass to stainless steel to polyimide.

The radiation response of CIGS solar cells has been studied by several groups over the past two decades. In the mid to late 1980’s, Boeing carried out a program to develop CIGS solar cells for space applications [74], which resulted in a space flight experiment on board the Naval Research Laboratory’s LIPS-III Satellite [75]. In the 1990’s, a project was conducted in Japan, lead by NASDA (now JAXA), to demonstrate commercial-of-the-shelf (COTS) electronics for space applications, which included CIGS solar cells designed for terrestrial applications. These cells were launched on The Mission Demonstration Test Satellite-1 (MDS-1) on February 4, 2002 [76,77,78]. The Europeans have an extensive effort underway to develop CIGS solar cells (e.g. see [72]), and the European Space Agency (ESA), has funded efforts to develop CIGS for space use including a space flight experiment, Equator-S [22,79]. All of these projects and flight experiments have been based on laboratory samples, and no space qualified CIGS solar cells are currently available in any production volumes. Because of this, the overall CIGS radiation database is incomplete and somewhat incoherent. Nevertheless, this series of experiments have served to demonstrate the radiation hardness of CIGS solar cells, especially under electron irradiation, and to produce a basic understanding of the CIGS solar cell radiation response. A representative set of radiation degradation data for CIGS solar cells are shown in Figure 54. This figure presents data measured after 1 MeV proton irradiation of CIGS cells grown on flexible metal substrates and other cells grown on glass substrates. These cells were provided to NRL by the Institute of Physical Electronics (IPE) at the University of Stuttgart [73]. Data measured by IPE on similar cells after 4 MeV proton and 0.5, 1, and 3 MeV electron irradiation are also shown [80]. Also shown are the ground test data generated for the MDS-1 mission [78]. The evolution of the PV parameters with increasing particle fluence and energy appears to be in agreement with displacement damage effects. This is an important result because CIGS is a polycrystalline material, so an analysis based on displacement damage effects may not be applicable to a material with such a high degree of disorder. This will be investigated in detail in the next section. Studies of CIGS cells exposed to 50 keV x-rays showed very little degradation [81], which indicates that CIGS cells are not sensitive to ionization effects. Instead, the study by Jasenek and Rau showed the degradation under electron and proton irradiation to be due to the

V - 55

introduction of an acceptor-like defect approximately 300 meV above the valence band that acts as a recombination and a compensation center [80]. 1.0

Normalized Pmp

0.8

0.6

Metal Subst. 1 MeV prot. Glass Subst. 1 MeV prot. IPE data, 4 MeV prot MDS data, 0.38 MeV prot. MDS data, 1 MeV prot. MDS data, 3 MeV prot. IPE data, 0.5 MeV elec. IPE data, 1 MeV elect. IPE data, 3 MeV elec.

0.4

0.2

0.0 10

10

11

10

12

10

13

10

14

10

15

10

16

10

17

10

18

10

19

10

-2

Particle Fluence (cm )

Figure 54: Radiation data measured in CIGS solar cells under proton and electron irradiation. The particle and energy are indicated in the legend. The data labeled metal subst. and glass subst. Are CIGS cells grown on metal substrates and glass substrates, respectively, and both datasets were measured after 1 MeV proton irradiation. The 4 MeV proton and the electron irradiation data labeled IPE were generated on similar cells and are from [80]. The data labeled MDS-1 are the ground test data generated for the MDS-1 mission and are from [73].

While the study of Jasenek and Rau [80] was quite thorough, the exact nature of the radiationinduced defect structure is not completely understood. The more recent publication by IPE suggests that their model remains incomplete [82]. In particular, there appears to be degradation mechanisms operative under proton irradiation that are not evident under electron irradiation. Under proton irradiation, degradation is observed in all of the PV parameters, but under electron irradiation, only Voc shows significant degradation until very high fluence levels. In particular, no Isc degradation is observed under electron irradiation. This results in electron irradiation being significantly less damaging than proton irradiation [71,80]. While proton irradiation is expected to be more damaging than electron irradiation, the observed behavior results in a separation of electron and proton data that is considerably larger for CIGS cells than for other technologies. This can be seen explicitly in Figure 55 where the CIGS data are compared to InP/Si solar cells data. InP/Si solar cells are chosen for comparison because this technology has been shown to be very radiation resistant [83]. The response of the two technologies under proton irradiation is comparable, but under electron irradiation, the resistance of the CIGS cells appears to be superior to that of the InP/Si cells by over an order of magnitude. While the degradation model remains incomplete, a general understanding of the damage mechanisms operative in these CIGS cells has been developed. It can be concluded that the presently available CIGS database has shown CIGS solar cells to have good radiation resistance,

V - 56

especially under electron irradiation, and that the cell degradation can be modeled in terms of displacement damage effects as has been done for crystalline soar cell technologies like GaAs. 1.0

0.8 1 MeV electron Pmp (norm.)

4 MeV proton 0.6

0.4 CIGS InP/Si

0.2

0.0 1E11

1E12

1E13

1E14 1E15 1E16 Particle Fluence (cm-2)

1E17

1E18

1E19

Figure 55: CIGS solar cell degradation data compared to that of InP/Si. The response of the two technologies to proton irradiation is comparable, but the CIGS cells show enhanced resistance to electron irradiation.

5.3.2.2 CIGS Analysis in Terms of Displacement Damage Dose In this section, an explicit analysis of CIGS solar cell radiation damage in terms of displacement damage dose (Dd). The NIEL has been calculated for CIGS and is shown in Figure 56. The data of Figure 54 are plotted in terms of Dd in Figure 57. In determining the electron Dd values, Eref was set to 1 MeV and a value of n=2 was found to fit the data well. The data are seen to correlate to within a reasonable margin considering that the data were obtained from three different sets of radiation experiments (Figure 54). A factor of 156 was found to work well for the present data (Figure 58). Fitting the data to Equation 8 yielded the C and Dx parameters given in Table 4.

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1.E+02

1.E+01

1.E+00

protons NIEL (MeV cm2/g)

1.E-01

1.E-02

1.E-03

1.E-04

electrons

1.E-05

1.E-06

1.E-07 1.E-04

1.E-03

1.E-02

1.E-01

1.E+00

1.E+01

1.E+02

1.E+03

Energy (MeV)

Figure 56: These are nonionizing energy loss (NIEL) values calculated for protons and electrons incident upon CuInSe2.

1.0

Normalized Pmp

0.8

0.6 Metal Subst. 1 MeV prot. Glass Subst. 1 MeV prot. IPE data, 4 MeV prot MDS data, 0.38 MeV prot. MDS data, 1 MeV prot. MDS data, 3 MeV prot. IPE data, 0.5 MeV elec. IPE data, 1 MeV elect. IPE data, 3 MeV elect.

0.4

0.2

0.0 8

10

9

10

10

10

11

10

12

10

13

10

14

10

15

10


Figure 57: These are the data of Figure 54 plotted as a function of Dd. The data measured at different energies are seen to collapse to a single curve.

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1.0

Normalized Pmp

0.8

0.6 Metal Subst. 1 MeV prot. Glass Subst. 1 MeV prot. IPE data, 4 MeV prot MDS data, 0.38 MeV prot. MDS data, 1 MeV prot. MDS data, 3 MeV prot. IPE data, 0.5 MeV elec. IPE data, 1 MeV elect. IPE data, 3 MeV elect.

0.4

0.2

0.0 7

10

8

10

9

10

10

10

11

10

12

10

13

10

14

10


Figure 58: These are the data of Figure 57 where the electron data has been brought into alignment with proton data by dividing the electron Dd values by the Rep factor of 156.

Table 4: Fit parameters determined by fitting the CIGS data to Equation 8.

CIGS 2 0.224 1.31 x1012 8.37 x109 156 10% and 15%

n C Dxe (MeV/g) Dxp (MeV/g) Rep BOL Eff (%)

5.3.2.3 CIGS Annealing Characteristics CIGS solar cells have displayed the ability to recover from radiation damage at temperatures as low as room temperature [63,84,85]. Moreover, CIGS solar cells have been observed to recover under an applied light bias [86,87,88,89]. This is extremely attractive behavior for space applications since it indicates that the cells will anneal while under normal operating conditions and thus show an effectively higher radiation resistance. A demonstration of this behavior can be seen in Figure 59 where data from [86] are shown that compare the recovery of Voc in a 1 MeV electron irradiated CIGS solar cell after storage in the dark with the recovery of a similarly irradiated cell after that cell had been illuminated at open circuit. The BOL Voc was ~ 525 mV, and the electron fluence was 1x1018 cm-2. The Voc loss decreases from 125 mV to 45 mV after less than 3 hours of illumination at room temperature. The work of Kawakita et al. [88,89] has shown similar annealing behavior after proton irradiation, and their work has demonstrated the annealing to occur while the solar cells were under irradiation. Furthermore, the data from the solar cell experiment onboard the MDS-1 spacecraft which is flying in a geostationary transfer orbit (GTO), as analyzed by Kawakita et al. [89], have demonstrated the annealing effects in the

V - 59

space environment (Figure 60). These results certainly suggest an extreme radiation resistance for CIGS solar cells.

Figure 59: These are data from [86] that demonstrate the enhanced annealing of irradiated CIGS solar cells under illumination. The cells were irradiated with 1 MeV electrons up to a fluence of 1x1018 cm-2. One cell was stored in the dark while the other was illuminated with white light (100 mWcm-2) at open circuit.

Figure 60: These are data from [89] showing CIGS solar cell data from the MDS-1 space solar cell experiment, which is flying in a high radiation environment. Also shown are predictions based on ground test data with and without annealing effects included. These data show no change in the CIGS solar output. The predictions made without including annealing significantly over estimate the cell degradation.

While the literature data clearly demonstrate the annealing capabilities of CIGS solar cells, the basic mechanisms governing the annealing of the radiation damage are not yet fully understood. Kawakita et al. [88,89] have produced a thorough analysis of the kinetics of the CIGS annealing V - 60

under both thermal stimulation and illumination. Their analysis was based on the formalism derived for crystalline materials such as InP [ 90 , 91 , 92 ] and InGaP [ 93 , 94 ] where the performance recovery is due to electron-hole pair recombination at defect sites that enhance defect migration and hence defect annealing [95]. The success of that analysis to accurately model both ground test and the MDS-1 space data would appear to indicate that the CIGS devices behave as their crystalline counterparts. However, Jasenek et al. have produced results that do not agree with this model. In contrast, their results showed that forward biasing an irradiated CIGS solar cell in the dark does not cause permanent recovery [87]. Thus, the formalism developed to describe the injection-annealing behavior of the crystalline solar cells must be adapted to apply to the CIGS cells, and the physical origins of the CIGS cell response must be at least somewhat different from those of the mechanisms operative in crystalline cells. Although the basic annealing mechanisms are not fully understood, the effects of annealing are substantial and must be considered when analyzing the overall CIGS radiation response. In particular, when making on orbit performance predictions, as will be done in a later section, the cell recovery must be included in the calculations. This can be done by modifying Equation 8. Using the formalism of Heinbockel [96], this is done by determining an effective value of Dd (labeled - Ddeff) which is the actual Dd value that has been reduced to account for annealing. Heinbockel has shown that Ddeff is given by the following expression:

Equation 12

Dd′ A( t ) In Equation 12, Dd' is the dose rate. The f parameter represents the fraction of defects that are expected to anneal, which has been assumed to be unity in the present analysis. A(t) is the defect annealing rate given by: Dd eff = f

Equation 13

A( t ) = A o e

−

Ea

kT

In Equation 13, Ao is a constant, Ea is the activation energy for the annealing process, k is Boltzmann’s constant, and T is the temperature. Using the data of Kawakita et al. [88], values of Ao and Ea have been determined to be 3.4x10-5 s-1 and 0.8 eV, respectively. Note that these annealing parameters are based on the illuminated annealing data from [88] as opposed to data of thermal annealing in the dark. Inserting Equation 13 into Equation 12 and then inserting Ddeff into Equation 8 allows the prediction of the degradation level of a CIGS cell in a given radiation environment that includes the effect of simultaneous annealing. A last issue to be addressed in the analysis of the annealing characteristics of the CIGS cells is the question of whether significant annealing of the cell occurs during the ground test measurements. If true, then the C value (Equation 8) might be considered incorrect since it would not represent a “pure” degradation rate. Kawakita et al. investigated this by performing 10 MeV irradiations at 270 K [88]. The data obtained after irradiation at low temperature is compared with the room temperature irradiation data from Figure 54 in Figure 61. The low V - 61

temperature irradiation data may be exhibiting a slightly higher degradation rate, but the differences are rather small in comparison the over scatter in the data. Considering that the 1 MeV proton irradiations required less than 10 minutes total irradiation time, annealing effects are not expected to be significant for those datasets. Indeed, it is the electron irradiation data that would be expected to show significant annealing effects since those irradiations require on the order of days to complete, but low temperature electron irradiation data are not presently available. For the present analysis, the degradation parameters given in Table 3 will be used. 1.0 0.9 0.8

Normalized Voc

0.7 0.6 0.5 0.4

Metal Subst. 1 MeV prot. Glass Subst. 1 MeV prot IPE data, 4 MeV prot. MDS data, 0.38 MeV prot. MDS data, 1 MeV prot. MDS data, 10 MeV prot. MDS data, 10 MeV prot. measured at low temperature

0.3 0.2 0.1 0.0 8

10

9

10

10

10

11

10

12

10

13

10


Figure 61: These are normalized Voc data from the solar cells of Figure 54 plotted with data measured by Kawakita et al. [89] after 10 MeV proton irradiation at 270 K (labeled “MDS data, 10 MeV prot. measured at low temperature” in the legend). The data measured after irradiation at low temperature does appear to display a slightly increased degradation rate, but the difference is only slightly outside the range of the overall dataset.

6 On-Orbit Solar Cell Performance Predictions This section will take a brief look at a trade study used to design a space solar array. In addition to the choice of solar cell technology, there are many aspects important for such a study such as solar cell circuit layout, array substrate design, and choice of coverglass. In keeping with the focus of the present discussion, this trade study will investigate how the solar cell radiation response drives the array design. To accomplish this, the EOL performance of solar arrays based on 3J InGaP/GaAs/Ge, SJ GaAs/Ge, and CIGS solar cells will be calculated for low-Earth orbit (LEO), medium-Earth orbit (MEO), and geostationary Earth orbit (GEO). The shielding of the solar cells will be varied by choosing a range of coverglass thicknesses and two different solar array designs. The analysis will be made using the Solar Array Verification and Analysis Tool (SAVANT) computer code, which implements Dd analysis. It must be stressed, however, that these calculations are for illustrative purposes only. The parameter space for solar array design is quite large and mission specific, so this brief look cannot be expected to cover all aspects.

V - 62

The performance parameters for the 3J InGaP/GaAs/Ge, SJ GaAs/Ge, and CIGS used in this analysis are given in Table 1,Table 3, and Table 4. The BOL efficiency for the SJ GaAs/Ge cells was 18%. Two BOL efficiencies for the CIGS cells were considered, 10% to be representative of currently available cells and 15% to represent what is expected to be available within five years. Annealing has been considered for the CIGS cells according to the analysis presented above. No annealing has been considered for the crystalline devices. The 3J and SJ cell weights were taken from [97] to be 0.72 kg/m2. The CIGS weight was taken from [98] to be 0.4 kg/m2 where a 1.5 mil steel substrate is assumed for the solar cell substrate. As discussed in 4.2, the coverglass used on the solar cell surface, the substrate on which the solar cell active layers are grown, and the solar array substrate material all provide shielding for the solar cell from the incident irradiation. In the SAVANT calculations, this shielding is accounted for by converting the shielding material to an equivalent thickness of coverglass material. The effect of the equivalent thickness of coverglass on the incident particle spectrum is then calculated by applying the continuous slowing down approximation [21]. Here, the 1.5 mil thick steel substrate for the CIGS cells is estimated to be equivalent to 5 mils of coverglass. The Ge substrate for the SJ and 3J cells was assumed to be 5 mils thick and to be equivalent to 2 mils of coverglass. Two specific array designs have been chosen for the present study – a rigid honeycomb panel like the MPTB array (Figure 34), which might be considered a “standard” array, and a lightweight, flexible array consisting of 2 mil thick sheet of Kapton stretched on a frame. The lightweight array is considered in order to investigate the possible advantages of the thin film CIGS technology. For this study, the parameters of interest are the array substrate weight, expressed in Kg/m2 and the shielding the array provides. The rigid array is assumed to be 1.8 kg/m2 based on the analysis of [97] with an equivalent coverglass thickness of 30 mils. The lightweight array is assumed to weigh 0.69 kg/m2, which was determined by replacing the Al honeycomb weight in the rigid array with that of a 2 mil sheet of Kapton. In terms of shielding, a 2 mil sheet of Kapton is equivalent to about 1 mil of coverglass. The orbits chosen here are all circular. The representative LEO orbit was chosen to have a 1000 km radius, a 90o inclination, and 7 year duration. The MEO orbit was defined as an 8000 km orbit, 0o degree inclination, and 7 year duration. The GEO orbit radius is 35,876 km at a 100o W inclination for 15 years. Inputting these orbital parameters into SAVANT produced estimates of the equivalent Dd values for the electron and proton radiation environment. This was done for a range of coverglass thicknesses. These values give the total Dd due to electrons or protons that a solar cell with the given thickness coverglass will experience. By expressing the backside shielding provided by the solar array substrate as an equivalent thickness of coverglass, these data also provide the backside irradiation equivalent Dd, which is then summed with the frontside value to give the total Dd. The electron and proton Dd are then combined using the Rep value to give the total mission Dd. The simultaneous annealing of the CIGS cells was included by altering the total mission Dd values according to Equation 12 and Equation 13. The array temperature was assumed to be 70oC. The Dd' value was determined by dividing the total Dd by the total mission time. The effect of including the annealing in the CIGS calculations is shown in Figure 62 where the data

V - 63

are plotted as a function of the thickness of the frontside coverglass. In these calculations, the 15% BOL efficiency and the flexible solar array were assumed. As expected, including annealing effects has a significant impact in the situations where there is a significant radiation environment, namely when the coverglass is thin and in the MEO orbit. Indeed, in the MEO orbit, a gain of nearly a factor of 2 is observed due to annealing. Note that the mission duration in the MEO orbit was reduced from 7 years to 1 year because the radiation degradation was so severe for all the technologies on the lightweight array. 200 LEO Orbit o 1000 km, 90 incl., 7 years lightweight array CIGS solar cells, 15% BOL

100 EOL Specific Power (W/kg)

EOL Specific Power (W/kg)

150

100

Analysis without annealing Analysis including annealing effects

50

MEO Orbit o 8000 km, 0 incl., 1 year lightweight array CIGS solar cells, 15% BOL

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50

Analysis without annealing Analysis including annealing effects 0 0.0

0.5

1.0

1.5

2.0

2.5

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(c) Figure 62: These are predicted EOL specific power values as a function of front coverglass thickness for a CIGS solar cell mounted on a lightweight array for a LEO (a), MEO (b), and GEO (c) orbit where the calculations have been performed with and without including annealing effects. A significant effect is observed for the thinner coverglasses especially for the high radiation MEO orbit where nearly a factor of 2 gain is observed due to annealing.

For thin film technologies, one of the primary advantages driving the technology development is the potential for flexible solar arrays. However, if the radiation degradation of the solar cell is severe enough, the cell may require a rigid coverglass for shielding. The data in Figure 62 show that in the LEO and GEO orbits, the annealing bolsters the EOL performance enough that flying cells with extremely thin covers may be feasible. For the array to be flexible, the cover would actually need to be a flexible coating. More careful analysis is required to determine if the cells V - 64

would be survivable with only a coating on the surface, but these preliminary data are at least encouraging. Comparisons of the results for the various cell technologies in the LEO orbit for the rigid array are shown in Figure 63. The data are plotted as a function of the thickness of the frontside coverglass. The backside shielding was constant at 30 mils equivalent for the rigid array, and the backside radiation exposure was included in the total Dd calculations. The data are expressed in terms of both power density (W/m2) and specific power (W/kg). The higher BOL efficiency of the 3J solar cells results in higher EOL performance. Also, the data show a clear peak in the specific power at about 2-3 mils of coverglass such that increased shielding results in decreased EOL performance due to increased weight. Similar results are observed for the GEO orbit assuming the rigid honeycomb array (Figure 64) except that the peak in specific power occurs at a slightly lower coverglass thickness. The same results are obtained if the rigid array is replaced by the lightweight array (Figure 65). 130

350

120 300

LEO Orbit o 1000 km, 90 incl., 7 years Annealing incl. in CIGS data


110

2

EOL Pmp (W/m )

250

200

150

CIGS 10% BOL CIGS 15% BOL SJ GaAs/Ge 3J InGaP/GaAs/Ge

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LEO Orbit o 1000 km, 90 incl., 7 years Annealing incl. in CIGS data

100 90 80 70 60 50


40 30 20

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4

6

8

10

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14


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2

4

6

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Figure 63: These are the calculated EOL performance data for the LEO orbit expressed in terms of power density (W/m2) and specific power (W/kg). The coverglass thickness value refers to the glass on the front of the cell. These calculations assumed the solar cells are mounted on a rigid honeycomb panel that provides an equivalent backside shielding of 30 mils, and the backside irradiation exposure was included in the total Dd calculations. In this orbit, the superior BOL efficiency of the 3J InGaP/GaAs/Ge cells results in the best EOL performance except for the thinnest of coverglasses.

V - 65

14

130 GEO Orbit o 35876 km, 100 W incl., 15 years Annealing Incl. in CIGS data

120

CIGS 10% BOL CIGS 15% BOL SJ GaAs/Ge 3J InGaP/GaAs/Ge 0

2

GEO Orbit o 35876 km, 100 W incl., 15 years Annealing included in CIGS data


2

EOL Pmp (W/m )

140

360 340 320 300 280 260 240 220 200 180 160 140 120 100 80 60 40 20 0

100 90 80 70 60 50 40


30 20 10 0

4

0


2


Figure 64: These are the calculated EOL performance data for the GEO orbit expressed in terms of power density (W/m2) and specific power (W/kg). The coverglass thickness value refers to the glass on the front of the cell. These calculations assumed the solar cells are mounted on a rigid honeycomb panel that provides an equivalent backside shielding of 30 mils of coverglass, and the backside irradiation exposure was included in the total Dd calculations. As was the case for the LEO orbit, the superior BOL efficiency of the 3J InGaP/GaAs/Ge cells results in the best EOL performance except for the thinnest of coverglasses.

V - 66

4

350

LEO Orbit o 1000 km, 90 incl., 7 years lightweight array Annelaing Incl. in GIGS data

200 300

2

EOL Pmp (W/m )

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LEO Orbit o 1000 km, 90 incl., 7 years lightweight array Annealing Incl. in CIGS data

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CIGS, 10% BOL CIGS, 15% BOL SJ GaAs/Ge 3J InGaP/GaAs/Ge

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220 GEO Orbit o 35876 km, 100 W incl., 15 years lightweight array Annealing Incl. in CIGS data

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EOL Pmp (W/m )

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300

200

150

100


50

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140 120 GEO Orbit o 35876 km, 100 W incl., 15 years lightweight array Annealing Incl. in CIGS data

100 80 60


40 20

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160

0 0.0

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1.0

1.5

2.0

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(b) Figure 65: These are the calculated EOL performance data for the LEO (a) and GEO (b) orbits assuming a lightweight array expressed in terms of power density (W/m2) and specific power (W/kg). The coverglass thickness value refers to the glass on the front of the cell. The lightweight array was assumed to provides an equivalent backside shielding of 1 mil of coverglass, and the backside irradiation exposure was included in the total Dd calculations. As was the case for the LEO and GEO orbits assuming a rigid array, the superior BOL efficiency of the 3J InGaP/GaAs/Ge cells results in the best EOL performance except for the thinnest of coverglasses.

It is interesting to note that, for the most part, the CIGS cells provide little or no advantage in either the LEO or the GEO orbits when mounted on a rigid array. Even assuming a 15% BOL efficiency and including the weight savings, the SJ cells with 50% higher BOL efficiency and the 3J cells with twice the BOL efficiency give better EOL performance. It should be noted, however, that cell cost has not been considered. Actual cost information, i.e. $/W, to perform these calculations are not available. It may well be that the significantly lower manufacturing cost of the CIGS material may place the technology at the top of an EOL $/W plot. The results shown in Figure 63, Figure 64, and Figure 65 show the CIGS cells to be superior to their crystalline counterparts if an extremely thin coverglass is used. As mentioned in the

V - 67

discussion of the effect of including annealing effects in the EOL calculations (Figure 62), it is the potential of the CIGS cells to function well with only a thin surface coating instead of a relatively thick, rigid coverglass that makes the concept of a flexible solar array feasible. The present results suggest that a CIGS solar cell with only a 0.5 to 1 mil thick surface coating and mounted on a 2 mil thick sheet of kapton will produce an EOL specific power of about 180 W/kg which is only about 20% less than that achievable with the best 3J InGaP/GaAs/Ge technology. Given this potential, AFRL is working to develop coating materials that display the proper thermal and optical properties for the cells. Part of the joint AFRL/NRL study is focused on measuring the effect of radiation on the coating properties and measuring the shielding that the coatings provide to the solar cells. This work is ongoing, and the results of this study will be presented in future publications. The MEO orbit, because of the extreme radiation environment, may be an environment where the advantages of the CIGS solar cells would be expected to dominate. The calculated EOL performance in the MEO orbit for both the rigid and lightweight arrays is shown in Figure 66, and indeed, a dramatic advantage is realized from the CIGS solar cells, especially in the case of the 15% BOL efficiency on a lightweight array. Note that the mission duration was reduced to 1 year from 7 years for the lightweight array case because none of the technologies could survive the 7 year mission. In the calculations for the lightweight array case, the SJ and 3J devices were assumed to have an equivalent backside shielding of 1 mil from the Kapton array layer and 2 mils from the Ge wafer used as the solar cell substrate itself. For the CIGS solar cells, the equivalent backside shielding was assumed to be 1 mil for the Kapton sheet and 5 mil for the 1.5 mil steel solar cell substrate. With this significantly reduced shielding, the better radiation resistance and simultaneous annealing of the CIGS technology allows for much better EOL power densities even with the lower BOL efficiency, and the lower weight of the CIGS cells allows for significantly improved EOL specific power values. It is this capability that makes the CIGS technology attractive. It should be noted, however, that for the rigid array, a 12 mil coverglass is required to achieve maximum EOL specific power and for the lightweight array, a 3 mil cover was required. To achieve a flexible array will require either a significant advance in the coating materials or a sacrifice in EOL performance.

V - 68

250 MEO Orbit 0 8000 km, O incl., 7 years Annealing Incl. in CIGS data

200

70


60


EOL Pmp (W/m )

50 2

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50

10

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30 20 MEO Orbit o 8000 km, 0 incl., 7 years Annealing incl. in CIGS data

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(a) 200

130 120


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EOL Pmp (W/m )

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MEO Orbit 0 8000 km, O incl., 1 year lightweight array Annealing Incl. in CIGS data

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MEO Orbit o 8000 km, 0 incl., 1 year lightweight array Annealing Incl. in CIGS data

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(b) Figure 66: These are the calculated EOL performance data for the MEO orbit example for cases of the rigid (a) and lightweight array (b) expressed in terms of power density (W/m2) and specific power (W/kg). The coverglass thickness value refers to the glass on the front of the cell. The rigid array was assumed to provide an equivalent backside shielding of 30 mils of coverglass, and the lightweight array was assumed to provide an equivalent backside shielding of 1 mil of coverglass. The backside irradiation exposure was included in the total Dd calculations. The duration of the mission for the lightweight array case was reduced to1 year. In this severe radiation environment, the CIGS show an advantage.

7

Special Topics in Solar Cell Radiation Response

7.1 Solar Cell Response at High Degradation Levels This section will investigate the response of a solar cell to irradiation levels higher than were considered in Section 2.3. Although the radiation levels involved are much larger than those encountered in a typical space mission, the investigation is worthwhile because in addition to diffusion length degradation, other degradation mechanisms emerge that lead to interesting effects on the solar cell performance. V - 69

The degradation of an InP/Si solar cell measured over a wide proton fluence range is shown in Figure 67. The data have been divided into three regions along the fluence axis. Region I corresponds to the fluence range where diffusion length degradation primarily controls the cell response as described in Section 2.3. Region II corresponds to higher fluences where there is an increase in Isc while Voc continues to decrease. These competing effects result in Pmp remaining essentially constant with increasing fluence. Region III corresponds to very high fluences where both Isc and Voc decline significantly and Pmp degrades to near zero. The following discussion will investigate the degradation mechanisms operative in Regions II and III. While this discussion uses InP/Si as an example, the same degradation mechanisms have been observed in Si and GaAs solar cells [7,8,9]. 40 Isc (mA/cm2), Pmax (mW/cm2), Eff (%)

Region

Region

35

Region

30 25

n+p InP/Si

20 15 Isc Pmax Voc

10 5 0

10 0 10

1011

1012

1013

1014

1015

3 MeV Proton Fluence (cm-2)

Figure 67: This graph depicts the evolution of the PV parameters of an InP/Si solar cell under irradiation by 3 MeV protons. The degradation can be separated into 3 fluence regions. In region I at lower fluences, the cell response is controlled mainly by reduction in diffusion length while the response in regions II & III at high fluences is due mainly to carrier removal effects.

7.1.1 Region II The onset of region II is marked by the emergence of the second major degradation mechanism, carrier removal. As described in conjunction with Figure 7, carrier removal occurs when radiation-induced defects capture majority carriers and thereby compensate the material. Since solar cells are typically one-sided abrupt junctions, carrier removal almost exclusively affects the base. Using capacitance vs. voltage (CV) measurements, the base carrier concentration can be determined. Carrier concentration data have been measured in this way in the InP/Si solar cell of Figure 67 as a function of 3 MeV proton fluence (Figure 68). As the carrier concentration decreases, the depletion region width increases (Figure 1), which means more light will be absorbed in that region. Because of the junction electric field, carrier collection is more efficient in the depletion region. Therefore, Isc increases, even above the pre-irradiation value (Figure 67).

V - 70

Majority Carrier Concentration (cm-3)

101 9 8 7

Measured Data fit of data

6 5

n+p InP/Si

4 3

2

100

0

50

100 150 3 MeV Proton Fluence (x1011cm-2)

200

Figure 68: This figure shows the decrease in the base majority carrier concentration due to carrier removal in an InP/Si solar cell under 3 MeV proton irradiation. Radiation-induced defects capture majority charge carriers and thereby compensate the material.

While extension of the depletion region results in enhanced carrier collection efficiency and thus improved Isc, it also results in an increased dark current which degrades Voc. Because the recombination/generation current originates with defects in the depleted region, the magnitude of this current increases linearly with depletion region width. Also, L continues to degrade in this fluence regime resulting in a continuing increase in diffusion dark current. The increase in Isc and decrease in Voc are competing effects, which results in Pmp remaining essentially constant in Region II (Figure 67). 7.1.2 Region III The third degradation region occurs at very high fluence levels, and it is marked by a decrease of the photovoltaic output to zero. It should be noted that while the decrease in Pmp appears abrupt in Figure 67, the logarithmic scale on the fluence axis masks an otherwise extended fluence range. In Region III, the effects of carrier removal become so severe that the p-type base is driven n-type, which destroys the original emitter/base junction of the cell. There can no longer be any photogenerated current or voltage, so Isc and Voc decrease bringing Pmp down to near zero. This evolution can be clearly seen in the cell QE response (Figure 69). As the 3 MeV proton fluence increases from 1x1013 to 7x1013 cm-2, the QE increases corresponding to the increase of Isc in region II. However, as the fluence increases to beyond 1x1014 cm-2, the QE degrades as the base transitions from p- to n-type.

V - 71

1.0


0.9

0 1x1013 7x1013 1x1014 2x1014

n+p InP/Si

0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0.0 300

400

500

600 700 800 Wavelength (nm)

900

1000

1100

Figure 69: This graph shows the degradation in QE of the InP/Si cell of Figure 67 under 3 MeV proton irradiation. The particle fluence is given in the legend in units of cm-2. For fluences in Region I, the long wavelength response degrades due to diffusion length degradation. At higher fluences, the QE increases due to an increase in the depletion region width caused by carrier removal. This corresponds to the increase of Isc in Region II. At very high fluences, carrier removal destroys the junction and the QE degrades rapidly (Region III).

There is a residual QE response evident in Fig. 5 after the highest fluence. The origin of this photoresponse has been identified by Taylor et al. [99] as the base/back surface field (BSF) interface (Figure 1). The BSF layer is included in the cell structure to create a drift field that drives minority charge carriers back toward the junction. Since the BSF is much more heavily doped than the base, it remains p-type even after the base has turned n-type. Therefore, an n+p junction forms at the rear of the cell which gives rise to a small QE response at the longest wavelengths. However, since the cell is so heavily damaged, L is very short and the dark current is large, so the cell can produce essentially no power. 7.2 Case of Nonuniform Damage Deposition As seen in Figure 17, the proton NIEL increases with decreasing energy. Because of this, lower energy protons loose energy at an increasing rate as they slow down and eventually come to rest in the cell. Increased energy deposition means greater defect concentration, so low energy proton irradiation induces a nonuniform damage profile, with a higher defect concentration at the end of the proton track. This is shown graphically in Figure 70 where the Monte Carlo particle transport program SRIM [100] has been used to calculate the damage tracks produced by protons with various incident energies in Si. The end of track damage peak is seen to become smaller and deeper into the material at lower energies. Since Si solar cell diffusion lengths are on the order of 100 to 200 µm, it is not until the energy exceeds about 10 MeV that uniform damage can be expected in the cell active region.

V - 72

#Vacancies/µm

101

Si

400 keV 1 MeV 4 MeV 10 MeV

100

10-1

10-2 10-1

100

101

102

103

Depth (µm)

Figure 70: These are proton damage tracks in Si for various incident proton energies calculated using SRIM [100]. The proton transfers energy to the crystal lattice as it traverses the solar cell creating vacancies. As the proton energy decreases, the rate of energy transfer (i.e. NIEL) increases, so the induced defect concentration increases. A peak in defect production occurs at the end of the track.

In the case when the proton energy is changing significantly as it passes through the solar cell active region, damage predictions based on the incident proton energy are not expected to be accurate. Because the NIEL is calculated based on the incident proton energy, the Si solar cell damage coefficients [14] correlate with NIEL over only limited energy range (Figure 71). It is important to understand that this is not indicative of a breakdown of the solar cell degradation model based on displacement damage effects. It simply indicates that the damage energy calculation is performed with a higher energy than the proton actually possesses when causing the damage. It follows, then, that if the correct proton energy were used, then the damage calculations, i.e. NIEL, would correlate with the measured RDCs. Messenger et al. have developed a formalism for performing such calculations [101]. This formalism is based on the Kinchin-Pease model, which states that the number of vacancies produced is directly proportional to the damage energy. Using SRIM [38], the vacancy distribution produced by protons of a given incident energy in the solar cell structure of interest is calculated (e.g. Figure 70), which is directly proportional to NIEL. Integrating this distribution over the device depth gives the total displacement damage deposited. Normalizing this value at all energies to the value determined for incident 10 MeV protons produces a set of RDCs that can be compared to the measured dataset from [14]. This is done in Figure 72 and the agreement is seen to be quite good over a wide energy range.

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Relative Damage Coefficients

102 *All data normalized to 10 MeV protons

Si

Proton NIEL JPL Damage Coefficients 101

100

ESD<EINC 10-1 10-1

100

ESD≈EINC

101

102

Proton Energy (MeV)

Figure 71: A plot of measured Si solar cell damage coefficients relative to 10 MeV (i.e. RDCs) along with the Si NIEL also normalized to 10 MeV. The RDCs correlate with the NIEL over only a very small energy range because for incident energies less than about 6 MeV, the proton looses significant energy as it passes through the cell active region.

Relative Damage Coefficients

102 *All data normalized to 10 MeV protons Proton NIEL JPL Damage Coefficients SRIM Results (80 µm) SRIM Results (100 µm) 101

100 10-1

100

101

Proton Energy (MeV)

Figure 72: This is a comparison of the measured Si solar cell RDCs [14] with those calculated using SRIM [38] according to the formalism of Messenger et al. [101]. Assuming a solar cell active region 80 mm wide produces good agreement between

Two calculated datasets are shown in Figure 72. The difference between these is the length over which the vacancy distribution was integrated. The integration should be performed over the active region of the solar cell, so the integration limits are based on the value of the minority carrier diffusion length. For the data shown in Figure 72, diffusion lengths of 100 and 80 µm were chosen, and the data calculated assuming 80 µm appears to give the better fit. Thus, with knowledge of the minority carrier diffusion length of the material and the structure of the solar cell, the entire set of RDCs can be calculated using SRIM [38]. Furthermore, with the calculated

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Normalized Maximum Power (P/Po)

RDCs and a single degradation curve measured at the reference energy, in this case 10 MeV, a complete calculation of the solar cell performance under irradiation by a spectrum of particles can be made. A comparison of EOL performance predictions made with the measured RDCs to those made using the calculated RDCs is shown in Figure 73 for a 60o circular Earth orbit as a function of orbital altitude. The calculations shown in Figure 73 were made using the JPL methodology. The data demonstrate that application of the JPL method using either the measured of the calculated RDCs produces the same results to within a few percent. 1.0 Si Solar Cells (1982) 0.9

*solid lines: JPL dashed lines: SRIM

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Figure 73: These are end-of-life performance predictions made using the JPL methodology for the specified Earth orbit as a function of orbital altitude. The coverglass thickness assumed for each altitude is indicated on the graph. The calculations were performed using the measured RDCs from [14] and the RDCs calculated by the methodology of Messenger et al. [101], and the agreement is seen to be quite good.

Low energy proton irradiation induced damage in the MJ InGaP/GaAs/Ge solar cells produces very interesting results due to the fact that the active layers of the MJ devices are extremely thin in comparison to the proton range. Damage track calculations from SRIM for protons incident on a typical 3J InGaP/GaAs/Ge solar cell are shown in Figure 74. Protons with incident energies of about 0.5 MeV and below stop within the active cell region, so the RDCs in this lower energy range are expected to deviate from the NIEL calculated assuming the incident energy. The plot of SJ [5] and MJ cell [51] RDCs with the NIEL (Figure 46) is reproduced in Figure 75. The RDCs decrease below the NIEL for incident energy around 0.1 MeV, but the RDCs are seen to increase for an incident energy of 50 keV. This behavior can be explained by investigating the details of the damage induced by protons with incident energies in this range.

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Vacancy Production Rate (#/µm/p+)

103

Proton Energy

InGaP

GaAs

Ge

15.8 keV 25.1 keV 39.8 keV 63.1 keV 126 keV 199 keV 316 keV 501 keV 794 keV 1.26 MeV 1.99 MeV 3.16 MeV 5.01 MeV 7.94 MeV 12.6 MeV

102

101

100

10-1

10-2 10-2

10-1

100

101

Penetration Depth (µm)

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103

Figure 74: These are proton irradiation-induced damage tracks calculated by SRIM assuming an InGaP/GaAs/Ge solar cell structure.

Norm NIEL and RDCs

10

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Proton NIEL SJ RDC 2J RDC 3J RDC Electron NIEL (1.7 power) SJ RDC 2J RDC 3J RDC

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Figure 75: RDCs for SJ GaAs/Ge [5], 2J InGaP2/GaAs, and 3J InGaP2/GaAs/Ge [51] solar cells plotted along with the GaAs NIEL. To be consistent with the RDCs, the proton NIEL has been normalized to the value at 10 MeV and the electron data to 1 MeV. The electron NIEL was raised to the 1.7 power according to the n value determined for the SJ cells. The correlation of the RDCs with NIEL indicates that the Dd analysis methodology is applicable to the MJ cell technologies.

Figure 76 shows QE measurements made on 3J InGaP/GaAs/Ge solar cells after irradiation by protons with incident energies in the range in question. The 0.05 and 0.1 keV protons cause degradation in the top cell only. The 0.4 and 1 MeV protons damage the middle cell almost exclusively. Therefore, in agreement with the measured RDCs, the damage produced by protons with energies of about 0.4 MeV and above is controlled by the GaAs sub-cell, and the RDCs correlate directly with the GaAs NIEL. In the very low energy range, the damage is strongly

V - 76

dependent on the specific cell structure. Depending on the thickness of the top cell and the exact bandgap of the top cell (the top cell bandgap can be varied through control of growth parameters and slight changes in stoichiometry), the 0.1 MeV proton damage can result in relatively more or less damage. The same conclusions hold 0.05 MeV proton damage. However, these RDCs are always less than the NIEL due to the increased radiation hardness of InGaP over GaAs [93]. 1.0

1.0

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InGaP/GaAs/Ge 0.8

Quantum Efficiency

Quantum Efficiency

0.8

0.6

0.4

50 keV Protons Solid lines: Unirradiated Dashed lines: 1x1012 p+/cm2

0.2

0.0 300

500

700

900

1100

1300

1500

0.6

0.4


0.2

1700

0.0 300

1900

500

700

Wavelength (nm)

900

1100

1.0

1700

1900

InGaP/GaAs/Ge

0.8

0.8

Quantum Efficiency

Quantum Efficiency

1500

1.0

InGaP/GaAs/Ge

0.6

0.4


0.2

0.0 300

1300

Wavelength (nm)

500

700

900

1100

1300

1500

0.6

0.4

1 MeV Protons Solid lines: Unirradiated Dashed lines: 1x1012 p+/cm2

0.2

1700

1900

0.0 300

Wavelength (nm)

500

700

900

1100

1300

1500

1700

1900

Wavelength (nm)

Figure 76: These are QE measurements made on 3J InGaP/GaAs/Ge solar cells after irradiation by protons of different energies. 0.05 and 0.1 MeV protons cause damage only in the top cell while 0.4 and 1 MeV protons damage the middle cell only.

Using the SRIM generated vacancy profile data of Figure 74, the RDCs can be calculated for the MJ InGaP2/GaAs/Ge devices according to the methodology of Messenger et al. as was done above for Si solar cells. This has been done and the data are shown in Figure 77. The calculated RDCs agree with the measured RDCs reasonably well. In particular, the “double-hump” structure and the decrease in RDCs for incident energies below 0.1 MeV is accurately modeled in the calculated RDCs. The calculated RDCs do not exactly agree with the measured data RDCs due to the extreme sensitivity of the calculations to the exact 3J solar cell structure and top cell performance parameters, i.e. the diffusion length and absorption coefficient. Nevertheless, these results show that the MJ radiation response can be accurately modeled by this formalism.

V - 77

Pmax Relative Damage Coefficients

102

MJ

101

100

10-1

NIEL JPL RDCs (SPL 2J) SRIM RDCs JPL RDCs (SJ GaAs)

10-1

100

101

102

Proton Energy (MeV)

Figure 77: This is a comparison of the measured solar cell Pmp RDCs [5] with those calculated using SRIM [38] according to the formalism of Messenger et al. [101] for SJ GaAs/Ge [5] and 2J InGaP2/GaAs/Ge [51]. The proton NIEL, normalized to 10 MeV, is also shown.

This analysis appears to highlight the importance of low energy proton effects in space solar cell radiation response analyses. In this context, it is instructive to consider quantitatively the fraction of the solar cell damage resulting from these low energy protons in a space environment. Take the example of a 5093 km circular orbit at an inclination of 60o, which is a particularly harsh environment in the heart of the earth’s trapped proton belts. The proton spectrum has been calculated using the NASA AP8 model. With this spectrum, the equivalent Dd experienced by a GaAs/Ge solar cell after one year in orbit has been calculated using Equation 6. The calculations were performed assuming three different coverglass thicknesses to show the effects of shielding. The results are shown in Figure 78 as the cumulative fraction of Dd as a function of proton energy where the energy refers to that of the protons as they emerge from the backside of the coverglass and are directly incident upon the cell. The results show that after the incident proton spectrum has passed through the coverglass, protons with energies as low as the displacement damage threshold and ranging to greater than 100 MeV are incident upon the solar cell. However, the large majority of the solar cell degradation, i.e. the largest fraction of cumulative Dd, comes from protons with energies between about 0.l and 10 MeV. Therefore, while monoenergetic, normal incidence irradiation by low energy (< ~ 0.1 MeV) protons can be quite damaging, the effect of such protons in a real space environment is much less dramatic because of the effects of shielding and the content of space proton spectrum. This result has significant impact on choice of ground test protocols as will be discussed in next section.

V - 78

Cumulative Fraction of Dd

1.0

GaAs

0.8

3 mil

0.6

12 mil 30 mil

0.4

5000 km, Circular Orbit 60° Inclination 5 year mission

0.2

0.0 10-4

10-3

10-2

10-1

100

101

102

103

Slowed-Down Proton Energy (MeV)

Figure 78: Cumulative fraction of the total Dd as a function of proton energy through coverglasses of 3, 12, and 30 mils. As an example, this plot shows, using 20 mil fused silica coverglasses in the harsh proton environment of this Earth orbit, that ~75% of the total displacement damage arises from protons having energies 0.1 MeV<E

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