Surface Contamination and Cleaning

Surface Contamination and Cleaning, Volume 1

K.L. Mittal, Editor

VSP

Surface Contamination and Cleaning, Volume 1

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SURFACE CONTAMINATION AND CLEANING VOLUME 1

Editor: K.L. Mittal

UTRECHT Ÿ BOSTON 2003

VSP BV P.O. Box 346 3700 AH Zeist The Netherlands

Tel: +31 30 692 5790 Fax: +31 30 693 2081 [email protected] www.vsppub.com

© VSP BV 2003 First published in 2003 ISBN 90-6764-376-9

All rights reserved. No part of this publication may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, electronic, mechanical, photocopying, recording or otherwise, without the prior permission of the copyright owner.

Printed in The Netherlands by Ridderprint bv, Ridderkerk

Contents

Preface Mapping of surface contaminants by tunable infrared-laser imaging D. Ottesen, S. Sickafoose, H. Johnsen, T. Kulp, K. Armstrong, S. Allendorf and T. Hoffard

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Monitoring cleanliness and defining acceptable cleanliness levels M.K. Chawla

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Tracking surface ionic contamination by ion chromatography B. Newton

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A new method using MESERAN technique for measuring surface contamination after solvent extraction M.G. Benkovich and J.L. Anderson

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Methods for pharmaceutical cleaning validations H.J. Kaiser

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Influence of cleaning on the surface of model glasses and their sensitivity to organic contamination W. Birch, S. Mechken and A. Carré

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Decontamination of sensitive equipment R. Kaiser and K. Haraldsen

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The fundamentals of no-chemistry process cleaning J.B. Durkee II

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Development of a technology for generation of ice particles D.V. Shishkin, E.S. Geskin and B. Goldenberg

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Cleaning with solid carbon dioxide pellet blasting F.C. Young

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Development of a generic procedure for modeling of waterjet cleaning K. Babets and E.S. Geskin

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Experimental and numerical investigation of waterjet derusting technology K. Babets, E.S. Geskin and B. Goldenberg

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Practical applications of icejet technology in surface processing D.V. Shishkin, E.S. Geskin and B. Goldenberg

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Correlating cleanliness to electrical performance T. Munson

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Qualifying a cleaning system for space flight printed wiring assemblies J.K. “Kirk” Bonner and A. Mehta

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Investigation of modified SC-1 solutions for silicon wafer cleaning C. Beaudry and S. Verhaverbeke

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Performance qualification of post-CMP cleaning equipment in a semiconductor fabrication environment M.T. Andreas

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Spatial and temporal scales in wet processing of deep submicrometer features M. Olim

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Microdenier fabrics for cleanroom wipers J. Skoufis and D.W. Cooper

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Fine particle detachment studied by reflectometry and atomic force microscopy A. Feiler and J. Ralston

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Dust removal from solar panels and spacecraft on Mars S. Trigwell, M.K. Mazumder, A.S. Biris, S. Anderson and C.U. Yurteri

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Laser cleaning of silicon wafers: Prospects and problems M. Mosbacher, V. Dobler, M. Bertsch, H.-J. Münzer, J. Boneberg and P. Leiderer

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Particle removal using resonant laser detachment K. Kearney and P. Hammond

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The future of industrial cleaning and related public policy-making C. LeBlanc

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Surface Contamination and Cleaning, Vol. 1, pp. vii–viii Ed. K.L. Mittal © VSP 2003

Preface This volume chronicles the proceedings of the International Symposium on Surfase Contamination and Cleaning held under the aegis of MST Conferences in Newark, New Jersey, May 23–25, 2001. Even a cursory look at the literature will evince that there has been tremendous interest and R&D activity in the arena of surface contamination and cleaning, so we decided to organize this symposium. Because of the importance of this topic in many technological areas, tremendous efforts have been devoted to devise novel and more efficient ways to monitor, analyse and characterize contamination on surfaces as well as ways to remove such contamination from a wide variety of surfaces. The ubiquitous nature of surface contamination causes concern to everyone dealing with surfaces, and the world of surfaces is wide and open-ended. A contaminant is defined as “unwanted matter or energy” or “material or energy in the wrong place”. Also contaminants can by broadly classified as: film-type, particulates; ionic, and biological or microbial. The technological areas where surface contamination has always been a bete noire and thus surface cleaning is of cardinal importance are too many and range from aerospace to microelectronics to biomedical. Here a few eclectic examples will suffice to underscore the importance of surface contamination and cleaning. In the world of ever-shrinking device dimensions in the microelectronics, the need to remove ever smaller particles (of nanosize dimension) is quite patent. On the other hand, film-type (organic) contamination is of crucial importance in the area of adhesive bonding, as even a very thin layer of contamination can be very detrimental in attaining good bond strength. In operation theaters, the concern about microbial contamination is all too obvious. So in light of the great concern about surface contamination, people dealing with surfaces are rightfully afflicted with molysmophobia.* The technical program for this symposium comprised 45 papers dealing with all kinds of contaminations on a host of surfaces, and many ramifications of surface contamination and cleaning were addressed. There were brisk and illuminating (not exothermic) discussions, both formally and informally, throughout the symposium. Also if comments from the participants are a barometer for the success of a symposium then this event was quite successful. Now coming to this volume, it contains a total of 24 papers (others are not included for a variety of reasons). It must be recorded that all manuscripts were rigorously peer reviewed and suitably revised (some twice or thrice) before inclusion in this volume. So this volume is not a mere collection of unreviewed papers −

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which is generally the case with many symposia proceedings − rather it reflects information which has passed peer scrutiny. The topics covered include: mapping of surface contaminants; various techniques for cleaning surfaces; various techniques for monitoring level of cleanliness; acceptable cleanliness levels, ionic contamination; pharmaceutical cleaning validations; cleaning of glass surfaces; decontamination of sensitive equipment; no-chemistry process cleaning; waterjet cleaning; cleaning with solid carbon dioxide pellet blasting; cleanroom wipers; dust removal from solar panels and spacecraft on Mars; laser cleaning of silicon surfaces; particle removal; implications of surface contamination and cleaning; and future of industrial cleaning and related public policy-making. I sincerely hope that this volume addressing many aspects and recent developments in the domain of surface contamination and cleaning will be of interest to a wide range of people working in many different industries. Acknowledgements It is always a pleasure to write this particular segment of a book as it offers the opportunity to thank those who helped in many ways. First, my sincere thanks are extended to my colleague and friend, Dr. Robert H. Lacombe, for taking care of the organizational aspects of this symposium. The comments from the peers are a sine qua non to maintain the highest standard of a publication, so I am most appreciative of the time and efforts of the unsung heroes (reviewers) in providing many valuable comments. I am profusely thankful to the authors for their interest, enthusiasm and contribution without which this book would not have seen the light of day. In closing, my thanks go to the staff of VSP (publisher) for giving this book a body form. K.L. Mittal P.O. Box 1280 Hopewell Jct., NY 12533

*Molysmophobia means fear of dirt or contamination, from Mrs. Byrne’s Dictionary of Unusual, Obscure, and Preposterous Words, University Books, Secaucus, NJ (1974).

Surface Contamination and Cleaning, Vol. 1, pp. 1–22 Ed. K.L. Mittal © VSP 2003

Mapping of surface contaminants by tunable infrared-laser imaging DAVID OTTESEN, SHANE SICKAFOOSE,∗ HOWARD JOHNSEN, TOM KULP, KARLA ARMSTRONG, SARAH ALLENDORF and THERESA HOFFARD1 Sandia National Laboratories, P.O. Box 969, MS 9403, Livermore, CA 94551-0969 1 Naval Facilities Engineering Service Center, 1100 23rd Avenue, Port Hueneme, CA 93043-4370

Abstract—We report the development of a new, real-time non-contacting monitor for cleanliness verification based on tunable infrared-laser methods. New analytical capabilities are required to maximize the efficiency of cleaning operations at a variety of federal (Department of Defense [DoD] and Department of Energy [DOE]) and industrial facilities. These methods will lead to a reduction in the generation of waste streams while improving the quality of subsequent processes and the long-term reliability of manufactured, repaired or refurbished parts. We have demonstrated the feasibility of tunable infrared-laser imaging for the detection of contaminant residues common to DoD and DOE components. The approach relies on the technique of infrared reflection spectroscopy for the detection of residues. An optical interface for the laser-imaging method was constructed, and a series of test surfaces was prepared with known amounts of contaminants. Independent calibration of the laser reflectance images was performed with Fourier transform infrared (FTIR) spectroscopy. The performance of both optical techniques was evaluated as a function of several variables, including the amount of contaminant, surface roughness of the panel, and the presence of possible interfering species (such as water). FTIR spectra demonstrated that a water film up to 7 µm thick would not interfere with the effectiveness of the laser-imaging instrument. The instrumental detection limit for the laser reflectance imager was determined to be on the order of a 10-20 nm thick film of a general hydrocarbon contaminant. Keywords: Infrared; tunable-laser; imaging; cleaning; surface contamination.

1. INTRODUCTION

Real-time techniques to provide both qualitative and quantitative assessments of surface cleanliness are needed for a wide variety of governmental and industrial applications. The range of potential applications include aircraft, shipboard, vehicle, and weapon component surfaces to be coated, plated, or bonded. The avail∗

To whom all correspondence should be addressed. Phone: (925) 294-3526, Fax: (925) 294-3410, E-mail: [email protected]

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ability of a convenient analysis technology for on-site, post-cleaning determination of surface contamination will allow more rapid and accurate assessments of the efficiency of chosen cleaning techniques. By developing an on-line technique, processed parts or extracted samples will not have to be sent to a separate laboratory for analysis, thereby eliminating processing delays. The information provided by the optical method will assist the process operator in distinguishing between specific contaminants and determining subsequent actions to be taken. In this paper we report the development of an infrared laser-based imaging approach that will reduce the use, emission, and handling of waste-stream materials in cleaning operations. This work is supported by the separate development of a hardened, portable Fourier transform infrared (FTIR) reflectance instrument at the Naval Facilities Engineering Service Center (NFESC), Port Hueneme, CA in cooperation with the Surface Optics Corporation. Simultaneous development of an FTIR instrument is complementary in nature to the laser-imaging technique and is described in detail elsewhere [1]. Both instruments will be used primarily for the real-time on-line or nearly on-line detection of contaminant residues on reflective surfaces. In each case, surface contamination is detected by its absorption of a grazing-incidence infrared beam reflected from the surface. The instruments differ in the nature of the information they provide. The laserbased instrument produces images that directly indicate the spatial extent and location of infrared-absorbing surface hydrocarbon contaminants. In contrast, FTIR instrumentation provides a wide-band spectral measurement of the surface reflectance averaged over a small area for nearly all organic materials, and many inorganic components. Thus, the laser-imaging system allows the rapid determination of surface cleanliness for organic residues over a large area, while the spectrallyresolved FTIR method is useful in identifying the specific molecular composition of a surface contaminant at a particular location. The imaging system under development employs a widely tunable infraredlaser illumination source in conjunction with an infrared camera. This approach provides an on-line technique for surveying contamination levels over large surface areas in a real-time imaging mode. The laser is broadly-tunable over the 1.34.5 µm wavelength range, thus allowing the detection of many hydrocarbon contaminants via absorption bands associated with CH-, OH-, and NH-stretching vibrations. Currently, the detection and identification of surface contaminants on reflective surfaces is conveniently and rapidly done by FTIR reflectance methods. These non-destructive, non-contacting optical techniques identify the chemical constituents of the contaminants, and can yield quantitative measurements with appropriate calibration. Infrared optical methods are particularly useful for cleanliness verification since the surface is probed under ambient conditions. More sensitive high-vacuum electron and ion spectroscopic techniques (X-ray photoelectron spectroscopy, Auger electron spectroscopy, and secondary-ion mass spectrometry) are not suited for on-line application.

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Commercial instruments that employ infrared reflectance spectroscopy are available for surface analysis and provide both quantitative and qualitative information on surface coatings. These instruments are limited in their ultimate sensitivity to surface contaminants by the nature of their optical design. Infrared radiation is focused onto the surface to be analyzed at a near-normal angle of incidence, resulting in a compact hand-held apparatus. The infrared light is collected as either specularly or diffusely reflected radiation depending on the roughness and scattering properties of the surface [2, 3]. The resulting sensitivity to very thin layers of surface species is limited by poor coupling of the incident electromagnetic field with the vibrating dipoles of the surface molecular species [4-6] in layers less than 0.1 µm thick. In order to maximize the sensitivity of infrared reflectance measurements for absorption bands of thin layers of contaminants on metallic surfaces, theoretical and experimental studies [7-9] have shown that the angle of incidence of infrared radiation on the surface should be increased to at least 60° from the surface normal. This is also true for many thin-film residues on the surface of non-metals, such as dielectrics and semiconductors (although the detectability of contaminant absorption bands under these circumstances depends strongly on the optical constants of both surface and substrate, and any absorption features intrinsic to the non-metallic substrate). Additional sensitivity in the reflectance measurement is obtained by measuring only the component of the reflected infrared radiation polarized parallel to the plane of incidence. This experimental method is variously referred to as, “grazing-angle” reflectance spectroscopy or infrared reflectionabsorption spectroscopy (IRRAS). We have adapted the technique of “grazingangle” reflectance spectroscopy to utilize the newly developed tunable-laser source. 2. EXPERIMENTAL

The laser-based instrument described in this report offers the capability to rapidly survey large surface areas and to determine the location and extent of residual hydrocarbon contaminants following cleaning operations. In contrast, a spectroscopic analysis by an FTIR-based infrared reflectance instrument is able to characterize a very broad range of organic constituents and many inorganic species. However, a surface-probing FTIR instrument measures a spectrum at only a single small area on a sample, thus requiring broad area surveys to be done by sequentially probing many points. Even at a rate of ~ 10 seconds per measurement point, this can be a time-consuming process. The rate of measurement by FTIR spectroscopy is constrained by the relatively low spectral brightness (compared to a laser) of the incandescent illumination sources. This makes it necessary to use relatively long integration times to achieve an acceptable signal-to-noise ratio. The tunable-laser-based instrument overcomes these limitations by illuminating a broad surface area with a high-brightness infrared laser. This approach allows a

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single-wavelength reflectance measurement over an area of several square centimeters to be made on a timescale of less than a second. In order to acquire measurements at multiple wavelengths, the laser is tuned and an image is collected at each of the desired wavelengths. While a detailed spectral map of a surface can be generated over the laser tuning range, the primary use of the system is to provide rapid areal surveys at a few key wavelengths that are indicative of hydrocarbon contaminants. The detection sensitivity for several hydrocarbon species at various illumination wavelengths was evaluated in this work, as well as a method to suppress image noise due to laser speckle while maintaining high illumination intensity. 2.1. Quasi-phasematching tunable infrared laser The broadly-tunable infrared laser illuminator is based on a technology called quasi-phasematching (QPM) [10]. This approach has been exploited to increase the tuning range and power of the infrared light source while reducing its size. For example, continuous-wave (cw) optical parametric oscillators (OPOs) that employ the QPM material, periodically-poled lithium niobate (PPLN), are capable of tuning over the 1.3-4.5 µm spectral region while emitting more than 0.5 W of power. This technique has been used to generate tunable infrared laser light for imaging natural gas emissions, and developing laser-based spectroscopic gas sensors [10]. In this work we are extending it to the analysis of hydrocarbon residues on material surfaces. The limit of the current tuning range of the PPLN-based laser at long wavelengths is about 4.5 µm (2222 cm-1) due to the transmission characteristics of lithium niobate. This property restricts the sensitivity of the chemical imaging system to functional groups containing hydrogen atoms (C-H, N-H, O-H). Extension of the laser tuning wavelength range beyond 5 µm (2000 cm-1) is desirable to provide specific identification of hydrocarbon and some inorganic molecular species. The light source assembled for the IR imaging sensor is an OPO pumped by a continuous-wave (cw) Nd:YAG laser, as shown in Figure 1 [10]. An electric field is induced in the OPO’s PPLN crystal by the electric field of the pump laser; these fields interact to form two new laser beams whose frequencies sum to the frequency of the pump laser. The reflectivities of the mirrors in the optical cavity are selected to resonate one of the generated waves, while the other wave is simply generated and released from the cavity. The resonated wave is called the signal; the non-resonated wave is called the idler. The exact frequencies of the signal and the idler are determined by the phasematching properties of the crystal (described below), the reflectivity of the cavity, and by any spectrally-selective optics that may be added to the laser cavity (e.g. an étalon). While either the signal or the idler beam can be used for measurements, only the idler is used in the experiments reported here. As shown in Figure 1, the OPO used in the imaging sensor is of the “bowtiering” design. A diode-pumped, cw, multimode Nd:YAG laser (Lightwave Elec-

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Figure 1. Diagram of the PPLN OPO and projection optics.

tronics, Mountain View, CA) that is capable of generating at least 6 W of output power at a wavelength of 1064 nm is used as the OPO pump source. Two flat mirrors (M3 and M4) and two curved mirrors (M1 and M2, 50-mm radius of curvature), all coated to be highly reflective at the signal and highly transmissive at the pump and idler wavelengths, form the bow-tie-shaped, single-wavelength resonant ring oscillator cavity designed to resonate the signal wave. An antireflection-coated lens, positioned between the pump laser and the OPO cavity, serves to image the Gaussian pump beam into the PPLN crystal. In this way, a beam waist (E-field radius) of 70 µm is created in the center of the crystal, which itself is centered between the two curved cavity mirrors. During normal operation, the OPO resonates on a single signal mode for minutes at a time, whereupon it hops to another cavity mode. The idler bandwidth is, however, determined by that of the pump beam, which is 10-15 GHz. The use of the QPM material, PPLN, makes cw OPO operation more tunable and efficient than it would be for a conventional birefringently phasematched crystal. Simply stated, phasematching is a condition in which all of the interacting waves (i.e., signal, pump, and idler) maintain a specified relative phase relationship as they propagate through a nonlinear medium, and is a necessary condition for efficient nonlinear generation. In birefringent materials, phasematching is

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achieved by careful selection and/or control of the crystal birefringence, temperature, and beam propagation angles. In a QPM medium, phasematching is designed into the medium during the crystal growth process. Phasematching is achieved by causing the crystal to have a periodically inverting optical axis. The engineering process used to create these crystals increases conversion efficiency by allowing the use of much stronger nonlinear coefficients of the crystal, and frees the system from reliance on birefringence thereby increasing tunability. As the light beams cross the crystal-axisinverting boundaries, any relative dephasing of the waves is corrected. For a crystal of a given periodicity, the rephasing is effective for a particular set of pump, signal, and idler frequencies. Some degree of tuning of these waves can be achieved within the crystal phasematching bandwidth (typically 10-20 cm-1). Broader tuning is achieved by accessing a portion of the same crystal having a different periodicity, or by changing the temperature of the crystal. In the present system, two 50-mm-long PPLN crystals (Crystal Technology, Palo Alto, CA) with an aperture of 11.5 mm × 0.5 mm are used as the active medium. Each crystal contains eight poled regions with different periodicities. One crystal’s periodicities range from 28.5 to 29.9 µm, and of the other crystal from 30.0 to 31.2 µm. When operating at a crystal temperature of 148°C, these periods collectively allow tuning of the idler from 2720 to 3702 cm-1. The crystals are mounted in a stacked fashion within a temperature-stabilized copper oven that is attached to a vertical translation stage. Each crystal is tuned by selecting a period using the vertical motion of the stage; horizontal motion of the oven is used to select between the two crystals. The raw output of the OPO contains the idler beam as well as portions of the signal and pump beams and some higher-order (red, green) beams created spuriously in the PPLN crystal. Spectral filtering is used to dump all but the idler beam. Prior to illumination of the sample, the idler is passed through a set of projection optics, also shown in Figure 1. The first of these is a ZnSe diffuser (mean roughness of ~ 3-4 µm) that is mounted on a motor-driven spindle. The diffuser serves to reduce the phase coherence of the idler in order to minimize laser speckle noise in the transmitted beam and viewed by the IR camera in the light reflected from the sample surface. The cone of radiation leaving the diffuser is collected by a ZnSe faceted lens (Laser Power Optics, Murrieta, CA). The faceted lens is formed to contain the equivalent of 16 6.4 mm facets and 16 partial facets around the edge of the lens on a 3.8 cm diameter with an effective f-number of 1.7. It operates as a prism array – the expanded beam is segmented into 32 different square beamlets that are subsequently overlapped at a distance of 5 cm from the surface of the lens. A ZnSe wire grid polarizer (not shown in Figure 1) is located at the overlap point, and serves to produce a p-polarized beam for the infrared reflectance measurement. The square-shaped overlap region is then imaged onto the target using an f/1.7, 8.4 cm focal-length ZnSe lens. As a unit, the system converts the Gaussian profile of the idler beam into a uniform square illumination on the sample surface.

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The infrared laser light is incident on the sample surface at an angle of 60° from the surface normal, and the specularly reflected component is detected by an InSb focal-plane array (FPA) camera with an infrared macro-lens assembly and an array size of 256 x 256 pixels. The FPA camera is located approximately 0.3 m from the sample surface, and the resulting field of view is 20 x 35 mm. FTIR instruments at both Sandia and NFESC were used to characterize the mid-infrared spectra of contaminated surfaces via optical interfaces for grazingangle reflectance spectroscopy. The system at NFESC uses a commercially available sampling accessory that permits a variable angle of incidence from 30 to 80°, which is convenient for evaluating detection limits for contaminants on a variety of surfaces. The optical interface used by the Sandia National Laboratories FTIR instrument was constructed with a fixed 60° angle of incidence with optics external to the spectrometer. It also differs from the NFESC system in the large solidangle used both to illuminate the surface and collect reflected light. This feature is particularly useful in the examination of rougher surfaces that cause significant scattering of the infrared beam, with a consequent degradation in both signal/noise ratio and detection limits. Both systems use infrared polarizers to enhance the sensitivity of the measurements by restricting the surface illumination to p-polarization [4]. Unless otherwise noted, all reflectance spectra presented in this paper are for p-polarized measurements. 2.2. Test sample preparation for calibration In order to evaluate the usefulness of the laser-imaging technique as a cleaning verification method, we prepared a number of test surfaces with wellcharacterized levels of contamination. These were used to determine detection limits as a function of contaminant species, level of contamination, degree of surface roughness, effect of spectral interference, and instrumental parameters such as angle-of-incidence. Seven candidate materials were chosen as contaminant species for evaluation as shown in Table 1. These materials have proven to be particularly difficult to remove during cleaning operations, and are representative of many other organic contaminants encountered in government and industrial cleaning processes. Detailed measurements on the first four materials have been made in the course of this work and preliminary measurements have been made on the remaining three. A number of metals were chosen as substrates for the target contaminants, based on usage information obtained from military and contractor facilities. These were Aluminum-7075-T6, Titanium 6Al-4V, Steel Alloy 4340, Stainless Steel 304, and Magnesium AZ31B. The metals were fabricated into 3.8 x 12.7 cm flat coupons for laboratory testing and method demonstration. Six surface roughness finishes of the Aluminum 7075-T6 test coupons were obtained, ranging from 80 to 600 grit (600 grit being the smoothest). A profilometer instrument was used to examine the surface roughness profiles and provide average Ra values. A Ra value is an arithmetic average of the absolute deviations

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Table 1. Contaminant materials used for preparation of test coupon for calibration Material

Description

Usage

Drawing Agent

White soft solid – ester grease

Lubricant

Brown liquid – paraffin hydrocarbons

Silicone Mold Release 1 Mold Release 2

Silicone Green liquid – ethanol homopolymer Clear liquid – proprietary polymeric resins Yellow liquid – abietic acid or anhydride Blue liquid – castor oil base

Metal drawing, cutting, and lubricating agent Rust preventative, cleaner, lubricant, protectant for metals Lubricant Mold release agent Mold release agent

Solder Flux Hydraulic Oil MIL-H-5606A AM2

Soldering flux for electrical and electronic applications Hydraulic systems, shock and strut lubricant

from the mean surface level, in millionths of an inch; therefore, a Ra value of 1.5 = 0.00000015 inches (3.8 µm). Due to the nature of metal-shop finishing processes, surface roughness values vary considerably across a given surface area. Finishing operations also result in a directional “grain” parallel to the sample coupons’ longitudinal direction. Surface roughness measurements, therefore, exhibit large variations between measurements taken along an orientation longitudinal or transverse to the polishing axis. Two surface roughness levels, 600 and 220 grit, were obtained for the other metal alloys. Prior to contaminant application, the aluminum alloy coupons were cleaned with acetone and underwent sonication with a clean-rinsing aqueous cleaner. They were then thoroughly rinsed in distilled water and dried in an oven at 50°C. Once cooled, they were weighed on a microbalance with a precision of 0.01 mg. Two or three weighings were averaged. Both drawing agent and lubricant contaminated Al-7075 coupons were produced by two primary deposition methods – airbrushing and manual brushing. Several other techniques were attempted, including “wire-cator” drawing, coupon spinning, and “manual drop and spread.” These techniques were not used to produce test samples for calibration for these particular contaminants due to the superior results obtained from airbrushing and manual brushing. Three levels of drawing agent were applied by airbrushing to three Al test coupons for each of the six surface finishes, creating a suite of 18 panels. Varying concentrations of drawing agent in water were prepared for the airbrush solutions. Similarly, four levels of lubricant were applied to four Al test coupons for each of six surface finishes, creating a suite of 24 panels. Manual brushing was used for all but the least contaminated samples, which were airbrushed. Lubricant solutions for both tech-

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niques were prepared using pentane as the solvent. Similar methods were used in preparing calibration samples with the mold release, solder flux, and hydraulic oil samples. All contaminated coupons were gently heated in an oven at 50°C for several days to remove both semi-volatile and volatile components. This served to stabilize the contaminants, allowing for quantification by weighing. Once the weights became stable, the coupons were cooled and weighed to determine the amount of contaminant present on the surface. When not being weighed or examined, the coupons were kept in a desiccator. 3. RESULTS AND DISCUSSION

Grazing-angle incidence reflectance spectroscopy acts to enhance the detection sensitivity for thin layers of residue predominantly through improved coupling of the electric field intensity of the incident beam with the vibrating dipoles of the surface contaminant layer perpendicular to the metallic surface. Some additional enhancement of the infrared absorption spectrum will also occur due to a lengthening of the effective path length through the absorbing thin film layer [4-6]. If the optical properties of both thin film and substrate are known (or can be determined), the reflection-absorption spectrum can be calculated as a function of film thickness and angle of incidence. This capability is particularly useful for interpreting experimental data and designing optical instrumentation. Computer codes written at Sandia [7] performed these calculations for a variety of materials. 3.1. FTIR measurements FTIR reflectance data for the full drawing-agent sample set were obtained at NFESC and Sandia using angles of incidence of 75 and 60° for average film thickness ranging from 0.1 to 1 µm, and aluminum substrates with surface finish ranging from 600 to 80 grit. Since the surface finishing operation produced a highly directional roughness, measurements were made both longitudinally and transversely with respect to the polishing grooves. Ra values were determined at NFESC using profilometer measurements, and resulted in surface roughness values of 0.3 to 1.5 µm for the longitudinal direction, and 0.5 to 6 µm for the transverse direction. The FTIR reflectance spectra were normalized using the uncoated back of a panel as a clean reference standard, and the intensity data are presented as either reflectance or –log reflectance in the following discussion. The C-H stretching vibrations near 2900 cm-1 proved to be generally useful in quantifying instrument response since these frequencies are well isolated from atmospheric interference due to water vapor and carbon dioxide. However, the baseline for these reflectance data was often non-linear. A simple single-point measurement of intensity was therefore not sufficient to determine the instrument response function.

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Optical constants (n and k) were derived for the contaminant C-H stretching vibrations using the Sandia reflectance code and a dispersion model to calculate a fit to the experimental data for one of the test coupons [7]. Reflectance-absorption spectra for the 2800-3000 cm-1 range were calculated for 1-µm thick films of a specific hydrocarbon contaminant on an aluminum surface at either 60 or 75° angle of incidence. This function was then used as a linear variable in conjunction with a second-order polynomial to produce a least-squares fit of the experimental reflectance data for the test coupons. An example is shown in Figure 2 for the longitudinal measurements of three thicknesses of drawing-agent contaminant at

Figure 2. Linear least-squares fit of experimental reflectance data for drawing-agent contaminant on 600 grit polished aluminum surfaces. Average film thickness: (Top) 0.9 µm, (Middle) 0.4 µm, (Bottom) 0.1 µm.

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Figure 3. Integrated reflection-absorption intensity at 60° angle-of-incidence for C-H stretching bands of drawing-agent films deposited on aluminum test coupons with varying degree of surface roughness (longitudinal, top; transverse, bottom).

75° angle-of-incidence. This procedure produces extremely rapid, robust analyses of the FTIR reflectance data, even for very thin films in the presence of noise, and accounts for baseline shifts and curvature due to interference fringes. Fitting coefficients for the linear spectral function (which are proportional to the integrated intensity) are plotted against the average calculated film thickness, and these results are shown in Figures 3 and 4 for longitudinal and transverse reflectance measurements at 75° and 60° angle-of-incidence, respectively. Results for

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Figure 4. Integrated reflection-absorption intensity at 75° angle-of-incidence for C-H stretching bands of drawing-agent films deposited on aluminum test coupons with varying degree of surface roughness (longitudinal, top; transverse, bottom).

the longitudinal, 60° angle-of-incidence follow a linear relationship with film thickness except for the roughest surface finish (80 grit, Ra = 1.5 µm). The instrument response functions for transverse measurements at 60° angle-of-incidence are also reasonably linear, with the same average slope as seen in Figure 3. In contrast, analysis of the FTIR reflectance data at 75° angle-of-incidence for both longitudinal and transverse sample orientations shows a marked departure from linearity at the highest values of film thickness (Figure 4). The initial slopes

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of the spectral response, the integrated reflection-absorption intensity, of these samples are slightly greater than the intensity of the spectral response of the same samples measured via a 60° angle of incidence data (Figure 3). This behavior is expected due to the increase in reflection-absorption sensitivity with increasing angle of incidence. Here, too, the average initial slope (and hence instrument sensitivity) is the same for both transverse and longitudinal orientations. The pronounced non-linearity in slope for the thickest films at 75° angle-ofincidence was unexpected. An increasingly non-linear response may be observed for thicker absorbing films, and this effect will become more pronounced as the angle of incidence is also increased. The interpretation of the data implying that measurement of a thicker film, sampled at a steeper angle, generated the observed non-linearity in the data is not substantiated by the calculated spectra for the present measurement conditions due to the small change from 60 to 75° in the angle of incidence. Furthermore, such a non-linear effect would be most pronounced for measurements on the smoothest substrate (Figure 4, filled circles) where the effective local orientation of the surface is most constant with respect to the illumination beam. Instead of observing such non-linear behavior the measurements made on the smoothest surface are by far the most linear sample series for the 75° data. We attribute the pronounced non-linearity of the 75° data for the thickest drawing-agent films to the morphological characteristics of the material as deposited on the aluminum test panel surface. As described above, the drawing-agent material is highly viscous and forms a visibly heterogeneous white film at 1-µm thickness. Variations in the deposition process produce relatively thick local areas of drawing-agent film and result in accretion of solid residue along the polishing grooves and ridges of the aluminum substrate. Under these circumstances, illumination of the surface with the FTIR beam at an angle of 75° may result in shadowing by contaminant material on ridge structures for all except the smoothest (600 grit polish) surface. The 12-mm diameter focal area of the infrared beam is elongated by a factor of four for this angle of incidence. In contrast, reflectance measurements at 60° result in only a factor of 2 elongation, and minimize the shadowing effect of thick films except for ridges on the roughest (80 grit polish) surfaces. This interpretation is substantiated by reflectance data for the second test set (lubricant material) as shown in Figure 5. FTIR reflectance measurements have been made at 75° angle-of-incidence for a test series similar to that of the drawing-agent set. An analysis of the C-H stretching frequencies shows a strikingly more linear dependence of instrument response with film thickness (with the exception of a single point for one of the panels with a 220 grit surface finish). We believe that this is due to the more fluid characteristic of the lubricant material, which allows the deposited film to conform much more closely to the surface topography of the test coupons. This behavior may also account for the stronger dependence of the integrated intensity slope with surface roughness, when compared to the nearly constant results for the drawing-agent contaminant examined above.

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Figure 5. Integrated reflection-absorption intensities of C-H stretching bands for lubricant films deposited on aluminum test coupons with varying degree of surface roughness for longitudinal illumination.

Even though excellent sensitivity was demonstrated for common hydrocarbon contaminants using grazing-angle infrared reflectance spectroscopy, concerns remain due to potential interference from other molecular species that may be present in the measurement environment. Chief among these is water, resulting either from cleaning operations or the local environment. Water is a very strong infrared absorber, and its presence on the surface to be measured may cause distortion or obscuration of the characteristic contaminant reflection spectrum. We performed an evaluation of this interference using lubricant-contaminated test panels with an average hydrocarbon thickness of 0.7 µm on aluminum. A water film was created on the surface of the test coupon using an airbrush, and reflection-absorption measurements were acquired at a 75° angle of incidence for several conditions. The thickness of the water film was difficult to determine due to continuous evaporation during the reflectance measurements. We estimated the thickness by measuring coupon weight gain immediately prior to and following the infrared measurements. Film thickness was calculated based on the average weight gain. Reflection-absorption spectra are presented in Figure 6 for three water films on the lubricant-contaminated test panel. These water films range in thickness from 1 µm (not visible to the eye) to 7 µm (clearly visible to the eye). Substantial interference is present in the 1700 cm-1 spectral range (not shown) due to the strong HO-H bending mode. This strong absorption obscures carbonyl absorption features that may be present in some, but not all, hydrocarbon contaminant species. The

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Figure 6. Potential interference effects of water on C-H stretching bands of hydrocarbon lubricant film (0.7 µm) on aluminum. Three thicknesses of water film were examined (1 µm, top; 3 µm, middle; and 7 µm, bottom).

broad H-OH stretching bands centered near 3400 cm-1, however, do not obscure the C-H stretching bands near 2900 cm-1. This is particularly important for the effective and accurate use of the tunable infrared-laser imaging instrument, since images are acquired for only a small number of frequencies near 3000 cm-1, in contrast to the broad-band spectral data collected by the FTIR instrument. 3.2. Tunable infrared-laser imaging Initial images of test panel surfaces were acquired at two frequencies (2915 and 3000 cm-1) that correspond to highly absorbing and non-absorbing portions, respectively, of the hydrocarbon infrared spectrum (see above, Figures 2 and 6). We used an acquisition time of 0.5 ms per frame, and averaged a minimum of 20 frames for each frequency in order to reduce noise (shot noise and laser speckle noise). Although the InSb FPA camera is square (256 x 256 pixels), the aspect ratio of the surface area scanned by the spectrometer and the resulting images in this work are elongated by a factor of two due to the trigonometric effects of the 60° angle of incidence and reflectance. Images were acquired for illumination transverse to the polishing direction. They have been corrected for thermal background emission and normalized for system spectral response at the measurement frequencies. The normalization fac-

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Figure 7. Gray-scale on-resonance (2915 cm-1, top) and off-resonance (3000 cm-1, bottom) images for an aluminum test panel contaminated with hydrocarbon drawing agent of 0.9-µm thickness.

tor was determined by the average intensity ratio of a clean surface (the uncontaminated back surface of the test panel) for the two measurement frequencies. The ratios of successive images using the PPLN-based laser system showed a noise level of 0.44% for the entire 65,536-pixel image under our current operating conditions. This noise level corresponds to a hydrocarbon film thickness of approximately 10-20 nm for the species examined in this report, and is the primary factor in determining the present instrumental detection limit. Gray-scale images at these two frequencies for the hydrocarbon drawing-agent (thickness of 0.9 µm on aluminum) are shown in Figure 7. Structure in the images is primarily in the form of vertical lines that represent ridges in the aluminum substrate formed during surface polishing operations. A darker vertical band near the center of the image manifests the presence of an absorbing hydrocarbon in the

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Figure 8. Reflectance images and line-intensity profiles for an aluminum test panel contaminated with a hydrocarbon drawing-agent of 0.9-µm thickness. Laser coherence noise (A) and results of Gaussian smoothing (C) are illustrated with corresponding intensity profiles (B and D, respectively) sampled along the horizontal lines superimposed on the images.

2915 cm–1 image. However, it is difficult to differentiate the absorbing organic film from the high contrast presented by the surface polishing marks in images at a single wavelength. The image created from the ratio of the two images, corrected for thermal background and normalized for the average image intensity, is a relative reflectance image, as shown in Figure 8 (A), assuming that the reflectance of the substrate remains constant at these two frequencies. Unprocessed image ratios such as these show a periodic grid pattern due to coherent interference effects that tend to obscure the hydrocarbon image, and we have investigated several image enhancement procedures to reduce noise while maintaining spatial resolution and contrast in the reflectance ratio images. Weighted Gaussian smoothing in a 7 x 7 pixel neighborhood and Fourier filtering have both been successful in suppressing this noise without significant degradation in spatial resolution, as shown in Figure 8 (B). The image ratios presented in this report have all been Gaussian smoothed. Reflectance intensity profiles along the horizontal line in each image ratio are also shown in Figure 8 (C) and (D) to demonstrate the magnitude of laser coherence noise and the effects of the smoothing procedure.

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Examples of reflectance ratio images for several test surfaces are shown in Figures 9 and 11 in false color. A calibrated color-table (“Rainbow”) for these falsecolor images is shown in Figure 10. Images for a series of 600-grit polished aluminum substrates contaminated with drawing agent are presented in Figure 9. These are the same specimens whose FTIR spectra are shown in Figure 2. Average film thicknesses for the three samples are 0.9 µm (top, left), 0.4 µm (middle, left), and 0.1 µm (bottom, left). The images are presented in false color format with identical dynamic range to help visualize the location of contaminants. Hydrocarbon material was manually deposited along the orientation of the surface polishing grooves, which is oriented vertically in these images. Heavy deposits of the hydrocarbon residue are easily

Figure 9. False-color reflectance images and thickness profiles for three aluminum test panels contaminated with a hydrocarbon drawing agent (thicknesses are: 0.9 µm, top-left; 0.4 µm, middle-left; 0.1 µm, bottom-left). Corresponding line thickness profiles are shown to the right of each false-color image.

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Figure 10. Color bar for false-color images presented in Figures 9 and 11. Film thickness was calibrated by weight-gain measurements during sample preparation and by comparison with FTIR reflectance data.

visible in the top reflectance image (red and yellow indicating the lowest reflectance, hence the thickest deposit, locations), with a particularly thick vertical band near the center. Very few areas in this image possess high reflectance values (dark blue) characteristic of low contamination. A horizontal line across the center of the image indicates the thickness profile, shown in Figure 9 (top, right) for this sample. Reflectance values have been converted to thickness of the drawing-agent hydrocarbon contaminant using the FTIR data analysis discussed above. The data shown here indicate the thickness averaging about 0.7 µm along the profile line, with heavier deposits up to 2 µm. False color images of the test surfaces contaminated with lower amounts of hydrocarbon (Fig. 9, middle and bottom) show much less spatial variation in the distribution of hydrocarbon residue. Hydrocarbon residues are thinner and appear as predominantly green and light blue in the false-color images while the line profiles show quantitatively the thickness of lubricant in these images. The average thickness values of the three profiles presented here are consistent with the weight change and thickness values determined by FTIR. The potential value of the infrared-laser imaging method for cleanliness verification is clearly demonstrated for these test panels. For these samples distribution of the residual hydrocarbon contaminant is quite variable. In the case of the heaviest contaminated sample, a localized cleaning to effect substantial removal can be profitably applied to the most heavily contaminated areas.

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Figure 11. False-color reflectance images and thickness profiles for three aluminum test panels with a hydrocarbon drawing-agent contaminant (surface polishes are: 600-grit, top-left; 220-grit, middleleft; 80-grit, bottom-left). Corresponding line thickness profiles are shown to the right of each falsecolor image.

We also acquired reflectance ratio images for test surfaces with rougher finishes for average hydrocarbon thicknesses of 0.9 µm, again using transverse illumination. False-color images and corresponding thickness profiles for these two samples are compared to the 0.9-µm thick hydrocarbon residue deposited on the smoothest, 600-grit polished surface in Figure 11. Average thickness values from the three profiles are in reasonable agreement for all three test panels, demonstrating that large changes in surface roughness (0.5, 2.1, and 6.1 µm) do not substantially affect the measured thickness of hydrocarbon residue. We observe a qualitative change in the false-color images in Figure 11. Increasingly rough test surfaces (middle and bottom) exhibit a grainier image qual-

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ity due to the large diversity of surface orientations relative to the infrared laser illumination beam. Distribution of the hydrocarbon residue on the 220-grit surface, however, is much more even (Fig. 11, middle, left) than for the smoothest surface (Fig. 11, top, left). The drawing-agent material shows a strong thickness gradient toward the right-hand side of the image for the roughest, 80-grit, surface (Fig. 11, bottom, left) that is clearly visible despite the grainy image appearance. 4. CONCLUSIONS

The work presented in this report has shown tunable infrared-laser imaging to be an extremely attractive method for on-line detection of hydrocarbon contaminants and determination of their spatial distribution for efficient cleaning operations. Calibrated test panels of hydrocarbon contaminants on metallic substrates were prepared and characterized with FTIR grazing-angle reflectance spectroscopy. Measurements were made over a range of film thicknesses and surface roughness, and the derived instrument sensitivity was quite robust with respect to the degree of surface roughness and the orientation of the reflectance unit to the direction of polishing grooves. Tunable infrared-laser images were acquired at both absorbing and nonabsorbing frequencies for hydrocarbon contaminants on aluminum test panels. The thickness of the contaminant layers calculated from the laser images showed good agreement with the measured film thickness determined by spatially averaged FTIR spectroscopic results. The laser images clearly reveal the heterogeneous distribution of the contaminant species on the component surfaces for a variety of film thicknesses and degree of surface roughness. Primarily, the effects of laser-coherence noise determine the current detection limits of the laser-imaging method. The noise is introduced when an image ratio is formed from images taken at absorbing and non-absorbing wavelengths. For typical hydrocarbon species, the detection limit appears to be on the order of 1020 nm for film thickness. Improvements in the system despeckling and projection optics may substantially decrease this noise level with an attendant increase in sensitivity. The configuration of a future prototype imaging system instrument will be strongly determined by system formats that employ either a pulsed or continuouswave laser, and staring focal-plane array (FPA) cameras or raster-scanned imagers. The design of an imaging system will include a consideration of the ultimate instrument cost. At the present time, it appears that a continuous-wave system with a scanned imager offers the system with the lowest cost. However, the performance of some newly developed inexpensive infrared microbolometer arrays will also be evaluated as a possible component of a low-cost pulsed imager. Future work will enlarge both the laser illumination area and image field of view in order to develop a prototype instrument capable of rapid large-area surveys during cleaning verification.

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Acknowledgments We gratefully acknowledge the financial support for these investigations by the Department of Defense through the Strategic Environmental Research and Development Program. REFERENCES 1. T.A. Hoffard, C.A. Kodres and D.R. Polly, Technical Memorandum, NFESC-TM-2335-SHR (2000).∗ 2. C.A. Kodres, D.R. Polly and T.A. Hoffard, Technical Report, NFESC-TR-2067-ENV (1997).* 3. C.A. Kodres, D.R. Polly and T.A. Hoffard, Metal Finishing 95, 48-53 (1997). 4. R.G. Greenler, J. Chem. Phys. 44, 310-315 (1966). 5. D.L. Allara, in: Characterization of Metal and Polymer Surfaces, L.H. Lee (Ed.), Vol. 2, pp. 193-206, Academic Press, New York (1977). 6. W.G. Golden, in: Fourier Transform Infrared Spectroscopy-Applications to Chemical Systems, J.R. Ferraro and L.J. Basile (Eds.), Vol. 4, pp. 315-344, Academic Press, New York (1985). 7. D.K. Ottesen, J. Electrochem. Soc. 132, 2250-2257 (1985). 8. D.K. Ottesen, L.R. Thorne and R.W. Bradshaw, Sandia Report, SAND86-8789 (1986).* 9. R.W. Bradshaw, D.K. Ottesen, L.R. Thorne, A.L. Newman and L.N. Tallerico, Sandia Report, SAND87-8241 (1987).* 10. P.E. Powers, T.J. Kulp and S.E. Bisson, Optics Letters 23, 159-169 (1998).

∗

NFESC technical reports may be ordered from the web at www.dtic.mil. Reports from Sandia National Laboratories may be ordered by contacting Sandia National Laboratories’ Technical Libraries at (505) 845-8287 or the National Technical Information Service (NTIS) at www.ntis.gov.

Surface Contamination and Cleaning, Vol. 1, pp. 23–41 Ed. K.L. Mittal © VSP 2003

Monitoring cleanliness and defining acceptable cleanliness levels MANTOSH K. CHAWLA∗ Photo Emission Tech., Inc., 3255 Grande Vista Drive, Newbury Park, CA 91320

Abstract—Defining and maintaining a “proper” level of surface cleanliness is, at best, subjective. Often the failure of surface preparation processes is not discovered until problems, such as poor adhesion, occur down stream. Surface cleanliness is critical for good surface finish or success of subsequent operations that depend on surface cleanliness. To assure consistent quality of surface cleanliness, it is important to: understand the types of contaminants that need to be monitored, most common cleanliness monitoring methods and their strengths and limitations, factors to be considered in choosing appropriate cleanliness monitoring method(s), and cost impact of various cleanliness levels. The selection of a cleanliness monitoring method should take into account several factors, such as the type of substrate and the types of contaminants to be monitored, etc. In order to define “Acceptable” level of cleanliness, a total cost approach is needed. Total cost is defined as the cost of cleaning added to the cost of non-conformance related to a particular level of surface cleanliness. An acceptable level of cleanliness is the one that minimizes or optimizes this “total cost”. Keywords: Acceptable cleanliness levels; optimum cleanliness level; total cost of cleaning; cleanliness monitoring methods.

1. INTRODUCTION

Defining and maintaining the surface preparation at “proper” levels is the key to good surface finish. However defining a “proper” level of surface cleanliness is, at best, subjective. For consistent results, it is important to define “how clean is clean”. Often the inadequacy of surface preparation processes is not discovered until problems, such as poor adhesion, occur downstream resulting in nonconformance due to poor surface cleanliness. To assure consistent quality of surface cleanliness, it is important to: understand the types of contaminants to be monitored; most common cleanliness monitoring techniques and their strengths and limitations; factors that affect the choice of cleanliness monitoring technique(s); select an appropriate cleanliness monitoring method; specify a desirable ∗

Phone: (805) 499-7667, Fax: (805) 499-6854, E-mail: [email protected]

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level of surface cleanliness; and monitor the surface cleanliness to an established level on an on-going basis. The selection of a cleanliness verification technique, as a minimum, should take into account the type of substrate and the types of contaminants to be monitored, desired level of cleanliness, speed of measurement, operator skill level required, and acquisition and operating costs. In addition, it is very important that the cleanliness monitoring technique be quantitative, non-destructive and readily usable. For every level of cleanliness, there is a corresponding level of product performance (i.e. failure / non-conformance rate). Each level of cleanliness has a cost associated with achieving that level, just as there is a cost associated with the failure / non-conformance rate corresponding to each level of cleanliness. These two cost components can be combined to assess “total cost” of cleaning. A minimum “total cost” can only be achieved by balancing the cost of incremental cleaning with the reduced cost of corresponding failure / non-conformance rate. The “optimum” level of cleanliness is the one that minimizes the “total cost”. Since all processes have some variation, there is bound to be some variation in the level of cleanliness achieved. An acceptable variation around the “optimum” level of cleanliness, where the total cost is minimum, would define the “Acceptable cleanliness level”. Some suggested approaches to defining acceptable levels of surface cleanliness are also discussed. 2. TYPES OF CONTAMINATION

A contamination is defined as any undesirable foreign matter that is present on a surface. Contaminations can be classified into three different categories: 1) Particulate, 2) Thin Film (Both Organic and Inorganic), and 3) Microbial or biological contamination. (1) Particulate contamination can be defined as any foreign matter present on the surface as a physical object. Some examples of particulate contaminants are dust, hair, micro-fragments and fibers. (2) Thin film contamination, also called Molecular contamination, is present on the surface in the form of a thin film covering the whole surface or some areas of the surface. This type of contamination can be organic or inorganic. Some examples of thin film contaminants are skin oil, grease, surfactant/chemical residues, oxides and other unwanted films. (3) Microbial contamination can be present on the surface in the form of particles or thin films or a combination of both and refers to generally unwanted living organisms present on the surface. Some examples of microbial contaminants are spore, bacilli and organic cultures. This type of contamination generally occurs from the environment or residues from processes.

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3. TYPES OF CLEANLINESS MONITORING METHODS

Cleanliness monitoring methods can also be generally classified into three different categories: 1) Indirect Methods, 2) Direct Methods, and 3) Analytical Methods. All of these methods have certain strengths and limitations, which will be discussed later; hence, it is important to select the method that will be most appropriate for a particular application. Most of these methods are appropriate for thin film or molecular contamination. (1) Indirect methods – Any technique that does not take a measurement on the actual surface or area of interest would be classified as an indirect method. See Table 1 for some of the most common indirect methods along with their features. (2) Direct methods – Any technique that takes a measurement directly from the actual surface or area of interest but does not directly identify the species of contamination present would be classified as a direct method. Some of the most common direct methods along with their features are listed in Table 1. (3) Analytical methods – Any technique that identifies the species of, and measures the amount of contamination would be classified as an analytical technique. Analytical techniques can be direct or indirect; however all of them usually determine the amount of and the species of contamination. Some of the most common analytical methods along with their features are listed in Table 2. 4. MOST COMMON VERIFICATION / MEASUREMENT METHODS

Some of the most common indirect, direct and analytical methods, with a brief discussion of their principles of operation, are presented below. 4.1. Indirect methods 4.1.1. Determination of non-volatile residue (NVR) [1] Also known as gravimetric measurement. This method requires a highly sensitive scale that can weigh parts to an accuracy of plus or minus one milligram, or better. A container is weighed before collecting fluid that flushes the part of interest. After the collected fluid has evaporated, the container is weighed again. The difference in the weight of the container before and after flushing and evaporation is the weight of the contamination removed by flushing. 4.1.2. Ultraviolet (UV) spectroscopy It involves the use of a spectrometer to analyze solvent extract from the parts of interest. Only contaminants that have an absorption wavelength in the UV region can be detected and analyzed. Calibration curves, utilizing samples with known concentration of contamination, can be developed and used to determine actual amount of contamination.

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4.1.3. Use of an optical particle counter (OPC) As the name implies, this method is used for detecting particulate contamination. Typically the part or surface of interest is flushed with some fluid. The fluid is then analyzed using a particle counter. OPC gives both the count and size of particles in the suspension measured. 4.2. Direct methods 4.2.1. Magnified visual inspection It is a step above visual inspection with the naked eye. Using some means of magnification, gross contamination that may not be visible to the naked eye can be observed. Due to its nature it is only effective with smaller parts that can be handled by an operator. The method also limits the surface area that can be checked. 4.2.2. Black light Using a black-light, i.e., UV light it is possible to visually detect gross level of contamination. For this technique to work, however, the contaminant of interest must fluoresce under black light. This method is somewhat similar to magnified visual inspection, except that since the contaminants fluoresce, if present, they are easier to see. Typically the level of contamination that can be detected with this method is too high for most precision cleaning applications. Experiments have shown that a skilled operator can, at best, detect 1 mg/cm2 [2]. 4.2.3. Water break test This technique utilizes the difference in surface tension of water and organic contaminants to detect contamination. This test will detect the presence of hydrophobic films on surfaces. When water is applied to the surface to be checked for contamination, water covers the areas of the surface that are clean. The presence of organic contamination on the surface prevents water from forming a film over it. This test can be used for checking small parts as well as large surfaces. It is very cost effective and will enable detection of molecular layers of hydrophobic organic contaminants. The sensitivity of the test may be questionable for rough or porous surfaces. 4.2.4. Contact angle A drop of water resting on a solid surface forms a shape that is influenced by the solid surface tension. The shape is influenced by presence of organic contaminants on the surface. If a tangent is drawn from the droplet to the solid surface, the angle formed is called “Contact Angle”. Contact angle measurements can be used to detect organic films, coatings or contaminants on the surface. “A contaminated metal part would have a high contact angle, such as 90° or more. Some parts, such as plastics, have positive contact angles even when “clean” so the method is not typically used for cleanliness analysis for these materials. While a number is obtained from this test, the test is still non-quantitative in terms of the contaminants on the part [3]”. Because of its simplicity, contact angle measure-

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ments have been broadly accepted for material surface analysis related to wetting, adhesion, and absorption. 4.2.5. Optically stimulated electron emission (OSEE) [4] A probe illuminates the surface to be tested with ultraviolet light of a particular wavelength. This illumination stimulates the emission of electrons from the metal surface. The emitted electrons are collected and measured as current by the instrument. Contamination reduces the electron emission and, therefore, the current measured. The equipment may be connected to a computerized scanning system that can scan a flat or cylindrical surface for cleanliness. The results can be presented as a color map or 3-D map. The user can define the level of cleanliness each color represents in the graphic presentation of the results. This feature makes it easy to compare “before” and “after” effect of a cleaning process or side-byside comparison of two pieces cleaned in alternative cleaners. OSEE is simple to operate, fast, and relatively inexpensive. In addition, it is quantitative, nondestructive, and non-contact. This technique detects both organic and inorganic contamination, such as oxides, and can be used on any shape of parts as long as the geometry of the part is presented to the sensor in a consistent manner. This system lends itself to scan small parts or large surface areas very quickly. This test can be used in the production line as well as for on-line real time measurement of surface cleanliness. The surface of interest must emit electrons for the technique to work. Nearly all materials of engineering importance emit electrons when exposed to UV light. 4.2.6. MESERAN surface analyzer – (measurement and evaluation of surfaces by evaporative rate analysis) [5] A measurement begins by depositing onto the test surface a small volume of test solution. A thin- end-window Geiger Müller detector is positioned above the droplet and a metered flow of gaseous nitrogen is passed between the detector and the test surface. To sense the volatile compound, organic compounds are used in which one or more of the carbon atoms are Carbon-14. The β-particles given off by the C-14 molecules at the surface are counted. Specifically measurements are made of how many molecules there are, how many are evaporating away, how fast they are evaporating away and, how many remain retained on the surface. Measuring molecules provides a high degree of sensitivity and the opportunity to analyze surfaces on a molecular scale with observations and results available in only a few minutes. The choice of volatile chemical compounds determines whether they react with the surface material, evaporate, or are retained by the various physical/chemical molecular forces acting at the surface. Chemical compounds can be found which tend to both volatilize (evaporate) and yet tend to be retained by the surface upon which they are placed. The balance of these tendencies determines just how long the volatile compound remains on the surface, or just how much remains. In fact, it is possible to choose a compound that reacts with specific properties of the surface, or a compound where the evaporation and/or retention are affected by certain characteristics of the surface

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material. By using only a monolayer equivalent of the radiochemical, the observed rate of evaporation becomes a function of the residual concentration of the non-evaporated molecules of radiochemical compound. 4.2.7. Total organic carbon (TOC) analysis [6] This method uses oxygen gas in a combustion chamber at a set temperature to combust carbon-based contaminants into carbon dioxide which is then detected by CO2 coulometer. Coulometer detection uses electricity to electrochemically measure the weight of carbon combusted in the combustion chamber. The method is very sensitive and can detect as little as one microgram of carbon. The TOC method works on a variety of materials and is surface-geometry independent. The method works only on small parts or pieces of larger parts. Due to the high temperature in the combustion chamber (more than 400°C) the method is not suitable to parts sensitive to high temperature. In addition, the TOC method detects only carbon-based contaminants, although this is generally not an issue since the majority of contaminants encountered in a manufacturing environment are carbon based. The TOC method can be used in a laboratory but is adaptable to production environment. It is a technique that works by oxidizing the sample to convert the carbon into carbon dioxide, and detecting and measuring carbon dioxide. The detection of carbon implies that there was some contamination that had carbon as its constituent. The level of TOC detected determines the level of cleanliness of a part. Since a TOC Analyzer detects only carbon, the compound of interest must contain some carbon in a detectable quantity, in order for the analysis to be carried out. 4.3. Analytical methods Any technique that identifies the species of, and measures the amount of contamination would be classified as an analytical technique. Analytical techniques can be direct or indirect; however all of them usually determine the amount of and the species of contamination. All of the analytical techniques involve “Probing the surface, near-surface region, or interior of a material with electrons, ions, or photons produced radiation that has been altered depending on the number, energy, or type of particles emitted. Changes can also occur in the frequency or absorbance of the radiation transmitted through or reflected from the material. Each type of analytical instrument looks at these emissions in a different way to provide information about certain aspects of the sample, such as structure, composition, or chemistry, and electronic or optical properties” [9]. Most of the analytical techniques test the specimen in vacuum, are expensive and require high skill level to operate and interpret the results. Testing takes time and rarely provides real-time information. Because of the cost of analytical testing, it is recommended that its use be limited to applications where identification of the species of contamination is required to enhance or improve the process. Analytical techniques can be divided into two groups; 1) Chemical/elemental surface analysis, and 2) bulk analysis techniques. There are many techniques that

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are capable of performing these analyses, some of the most common analytical techniques are summarized below. For a more complete list of most common analytical techniques, visit www.cea.com/table/htm, website of Charles Evans & Associates. For a more comprehensive list of analytical techniques visit the website of ESCA users group in England – www.ukesca.org/tech/list/html. 4.3.1. Chemical/elemental surface analysis techniques 4.3.1.1. Auger electron spectroscopy (AES) / scanning Auger microscopy (SAM) [7–9] They are used to obtain elemental composition information (and some chemical information) from the top two to five atomic layers of a material; identify the composition of very small features and particulates on surfaces; and provide depth composition profiles of thin films, metals, and alloys. Micro-beam AES is also used to study grain boundaries in high temperature alloys, and to examine fracture surfaces to determine composition and extent of damage. The Auger electrons, named after the discoverer of the process, are produced (among other emissions) with discrete energies, which are specific to each element, when the surface is irradiated by a finely focused electron beam. Auger electrons are collected and measured. Auger electrons have discrete kinetic energies that are characteristic of the emitting atoms, making this technique particularly useful for identifying elemental composition. The energy level of Auger electrons is specific to a species of contamination. The escape depth of Auger electrons (1–5 nm) makes this technique very surface sensitive. 4.3.1.2. Electron spectroscopy for chemical analysis (ESCA) [7–9] Also known as X-ray Photoelectron Spectroscopy, or XPS, is a surface analysis technique that provides information on both elemental identity and chemical bonding. This information can be used to identify functional groups and molecular types. This method uses special equipment to bombard the surface of interest with X-rays under vacuum conditions, causing electrons to be ejected from the surface. The actual elemental composition can be quantified by measuring the energy level of ejected electrons, since each element ejects electrons at a unique energy. Its application is limited to mostly research and development, but it can be used to calibrate and evaluate other, less sophisticated measurement methods. 4.3.1.3. Secondary ion mass spectrometry (SIMS – static) [7–9] A surface analysis technique used for identifying molecules on a surface, as well as for depth profiling for tracking very low concentrations of contaminants or ionimplanted species. SIMS technique includes static SIMS (SSIMS), dynamic SIMS, and time-of flight SIMS (TOF SIMS). SSIMS can identify organic and inorganic species. TOF SIMS is an ultra-precise and accurate technique for measuring the mass of molecules in the near-surface layers of material. A pulsed primary ion beam is used to sputter material from the surface of the sample. Secondary ions are collected and focused into a reflection time-of-flight (TOF) mass spectrometer, where they are mass analyzed. Analysis involves measuring the length

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of time it takes the secondary ions to reach the detector. The lighter the ion, the less time it takes to reach the detector. From the arrival time the masses of the species can be identified. High sensitivity depth profiling is a key feature. 4.3.1.4. Secondary ion mass spectrometry (SIMS – dynamic) [7–9] It uses a much higher intensity bombarding beam than Static SIMS, and is a particularly sensitive (less than part-per-billion level) method for depth profiling of dopants and trace elements in semiconductors. It can also map the X-Y distribution of atomic species with sub-micrometer spatial resolution. An energetic primary ion beam is used to sputter atoms from the sample surface. Secondary ions emitted are mass analyzed. It is inherently a profiling technique. It uses O2 or Cs ions to bombard a surface in high vacuum. High sensitivity depth profiling is a key feature. 4.3.1.5. Variable-angle spectroscopic ellipsometry (VASE) [7, 8] It is a noninvasive technique that offers information about surface composition, layer thickness, and optical properties. Its applications include examining optical surfaces and crystals, and measuring and analyzing band gaps in semiconductors, optical devices, thin films, and carbon coatings on computer hard disks. 4.3.1.6. Energy dispersive X-ray (EDX) and wavelength dispersive X-ray (WDX) analyses [7, 8] They are often combined with a scanning electron microscope or electron microprobe. EDX provides simultaneous multi-element analysis and elemental mapping capabilities for a region up to a few micrometers deep. WDX analyzes trace amounts of one element at a time and is more quantitative than EDX. An example of EDX application is identifying silicon nitride and titanium carbide inclusions in stainless steel. 4.3.2. Bulk analysis techniques The following are several analytical techniques that typically are used for chemical or elemental analysis of bulk materials, but these can also be adapted for the characterization of surfaces and thin films. Many times these techniques are used in industry for characterizing surfaces, sometimes without full knowledge of the strengths and limitations of these techniques. It is hoped that information about how these techniques work, their strengths and limitations would help the reader in determining their usefulness and limitations for their applications. 4.3.2.1. Fourier transform infrared (FTIR) spectroscopy [7, 8] It provides information about the chemical bonding and molecular structure of organics and some inorganic solids, liquids, gases and films. This technique is especially good for identifying unknowns when reference IR spectra are available. When an infrared beam impinges on a surface, the molecular constituents vibrate in the infrared regime. The identities, surrounding environments, and concentrations of these oscillating chemical bonds can be determined. FTIR is a powerful analytical tool for characterizing and identifying organic molecules. The IR spectrum of an organic compound serves as its fingerprint and provides information

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about chemical bonding and molecular structure. This information can be used to detect the types of organic materials present on the surface. 4.3.2.2. Raman spectroscopy (RS) [7, 8] It is used to examine the energy levels of molecules that cannot be well characterized via infrared spectroscopy. The two techniques, however, are complimentary. In the RS, a sample is irradiated with a strong monochromatic light source (usually a laser). Most of the radiation will scatter or “reflect off” the sample at the same energy as the incoming laser radiation. However, a small amount will scatter from the sample at a wavelength slightly shifted from the original wavelength. It is possible to study the molecular structure or determine the chemical identity of the sample. It is quite straightforward to identify compounds by spectral library search. Due to extensive library spectral information, the unique spectral fingerprint of every compound, and the ease with which such analyses can be performed, the RS is a very useful technique for various applications. An important application of the RS is the rapid, nondestructive characterization of diamond, diamond-like, and amorphous-carbon films. 4.3.2.3. Scanning electron microscopy (SEM) / energy dispersive X-ray analysis (EDX) [7, 8] The SEM produces detailed photographs that provide important information about the surface structure and morphology of almost any kind of sample. Image analysis is often the first and most important step in problem solving and failure analysis. With SEM, a focused beam of high-energy electrons is scanned over the surface of a material, causing a variety of signals, secondary electrons, X-rays, photons, etc. – each of which may be used to characterize the material with respect to specific properties. The signals are used to modulate the brightness on a CRT display, thereby providing a high-resolution map of the selected material property. It is a surface imaging technique, but with Energy Dispersive X-ray (EDX) it can identify elements in the near-surface region. This technique is most useful for imaging particles. 4.3.2.4. X-ray fluorescence (XRF) [7, 8] Incident X-rays are used to excite surface atoms. The atoms relax through the emission of an X-ray with energy characteristic of the parent atoms and the intensity proportional to the amount of the element present. It is a bulk or “total materials” characterization technique for rapid, simultaneous, and nondestructive analysis of elements having an atomic number higher than that of boron. Traditional bulk analysis applications include identifying metals and alloys, detecting trace elements in liquids, and identifying residues and deposits. 4.3.2.5. Total-reflection X-ray fluorescence (TXRF) [7, 8] It is a special XRF technique that provides extremely sensitive measures of the elements present in a material’s outer surface. Applications include searching for metal contamination in thin films on silicon wafers and detecting picogram-levels of arsenic, lead, mercury and cadmium on hazardous, chemical fume hoods.

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5. CONSIDERATIONS FOR SELECTING A CLEANLINESS MONITORING METHOD [10]

There are several factors that should be considered in selecting a method for monitoring surface cleanliness. The factors discussed here are the ones that are most important but by no means represent a complete list of factors that should be considered. There may be other factors that are pertinent to a particular application that should be considered. (1) Type of contaminant – One of the first factors that should be considered in selecting a cleanliness monitoring method is the type of contaminant that need to be monitored. Is the contaminant particulate or thin film type? If thin film contamination, is it organic or inorganic or both? Does the technique under consideration monitor the type of contaminants that need to be monitored? (2) Types of substrates – What type of substrate is going to be monitored? Are the techniques under consideration capable of monitoring this type of substrates? Are the techniques likely to damage the substrate to be monitored? (3) Level of cleanliness to be monitored – It is important that the level of contamination that is expected or tolerable can be monitored by the technique under consideration. It is recommended that samples with different levels of contamination be monitored with the technique(s) under consideration. In evaluating the technique for suitability, prepared samples should have levels of contamination spanning a range from 0% (i.e. clean surfaces) to maybe 200% of the expected level of contamination on the surface. The technique(s) should not have any problem in distinguishing between different levels of contamination. (4) Features of monitoring method – It is important to consider various features of the method under consideration. For example, is the technique non-contact and/or non-destructive? Does the technique require deposit of some medium on the surface? For example, the contact angle measurement requires that a droplet of water be placed on the surface of interest. How large an area can the technique measure? Is it sensitive to surface roughness? Can the technique check parts of different geometries? Can the technique be used on-line? Is the technique suitable for the environment it is going to be used in? Does the technique cause any permanent changes to the surface? All of these questions should be considered to determine the most appropriate monitoring technique for a particular application. (5) Measurement speed – Is the measurement speed critical for the application under consideration? If so, how fast can the technique make a measurement? Is the speed sufficient to keep up with the production flow? (6) Acquisition and operating cost – How does the acquisition cost compare among the techniques that meet other requirements for the application? Are there any expendable items that would have to be purchased for continued use of the equipment? How much does that add to the operating cost? What are the maintenance and calibration requirements and how much these require-

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ments will add to the operating cost? All these questions should be answered to truly compare the total cost of any cleanliness monitoring system. (7) Skill level required – The operator skill level can be a key factor in the use of some techniques, particularly the analytical techniques. Some techniques may involve interpretation of the data to determine the quality of surface cleanliness. These factors should also be considered in the selection of a cleanliness measuring technique. A high operator skill level will result in higher operating cost. In the event of personnel turnover, higher training costs may also be incurred. 6. COST OF CLEANLING [10]

For every level of cleanliness, there is a cost to achieve that level of cleanliness. There is corresponding level of failure/non-conformance for each cleanliness level, hence cost of failures/non-conformance. “Total Cost” of achieving a certain level of cleanliness is the sum of these two costs. As the achieved level of surface cleanliness increases, the cost of cleaning also increases. Eventually the incremental cost of cleaning rises exponentially. Hence the cost of surface cleaning is directly proportional to the surface cleanliness level. Intuitively, we know that the higher the cleanliness level the lower the failure/non-conformance rate, hence cost, due to surface cleanliness. The incremental drop in costs due to lower failure/non-conformance also exhibits exponential relationship. Hence the cost of failures/non-conformance is inversely proportional to the surface cleanliness level. If both of these costs were plotted on a graph, the typical result would be like the one shown in Figure 1.

Figure 1. Total cost vs. cleanliness level.

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An optimum level of cleanliness is the one that minimizes the total cost. Eventually one can arrive at a cleanliness level where the savings in the failure/nonconformance costs will not be offset by incremental cost of achieving cleanliness beyond the optimum level. A small range around the optimum level of cleanliness can be established as the “Acceptable Level” of cleanliness. 7. DEFINING ACCEPTABLE (“OPTIMUM”) LEVEL OF CLEANLINESS

It is expected that the non-conformance levels will increase as the level of cleanliness decreases or vice versa. It is important to understand the relationship between the level of cleanliness and non-conformance rate in order to establish the “acceptable level of cleanliness”. For example, if the failure/non-conformance rate is too high due to the surface cleanliness level, then the surface cleanliness level will have to be improved in order to reduce the failure rate. On the other hand, no failures or a very low failure rate due to the surface cleanliness level implies that the surface may be “over-cleaned.” It may be desirable to optimize the cleaning process by comparing the cost of failures/non-conformance with the cost of cleaning the surface. Generally, increasing the level of surface cleanliness will result in increased cleaning cost. An increased level of cleanliness should lower the rate of non-conformance, which, in turn, reduces the non-conformance cost. As long as the reduction in nonconformance cost more than offsets the increased cost of cleaning, it would be cost effective to increase the achieved level of surface cleanliness. When the decrease in non-conformance cost fails to offset the increase in the cleaning cost, then an optimum or “acceptable” level of cleanliness has been achieved. To establish the optimum level of surface cleanliness, two approaches are outlined here. One approach utilizes the success of the subsequent operation that depends on surface cleanliness level. The other approach is to start monitoring the cleanliness levels achieved and corresponding level of failure/non-conformance rate. Once an acceptable level of cleanliness is established using one of the two approaches, cleaning process can be monitored in production to assure ongoing product quality. 7.1. Controlled experiment This approach requires that the measure of success be defined for the subsequent operation that depends on surface cleanliness. For example, if the parts are to be bonded, then the adhesion strength of the bond will be the measure of success. If the parts are to be coated after cleaning, then the adhesion strength of the coating should be correlated to surface cleanliness. The acceptable level of surface cleanliness is the one that results in the desired level of bond/adhesion strength. One simple approach is to start monitoring and recording the cleanliness level of each part. A statistically significant sample must be monitored to assure valid

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Figure 2. Peel strength vs. surface cleanliness.

results. These parts then must be followed through the process to measure the level of success for each part at the subsequent operation. The level of cleanliness that results in the desired minimum level of success is the minimum level of cleanliness that must be achieved in production. This approach has its limitations. For example, the results depend on what level of cleanliness is being achieved in production. If the surface is “too clean” there may not be enough variation in the cleanliness level to identify the point where minimum success is achieved. On the other hand, if the surface is not clean enough the desirable success may not occur. A more proactive approach is to prepare parts with different levels of surface cleanliness, measure and record the cleanliness level and follow through with the subsequent operation to correlate the success level with cleanliness level. It is recommended that the range of cleanliness should be as wide as possible to help identify the minimum level of cleanliness. Once again it is important that a statistically significant sample be used. It is also recommended that, if possible, several cleanliness measurements should be taken from each part and the mean cleanliness level be correlated to the mean success level. Figure 2 [10] graphically depicts the typical result of correlating the success level to surface cleanliness level. A minimum level of cleanliness is the one that corresponds to the target minimum level of success. 7.2. “Benchmark” testing Once a cleanliness monitoring method has been selected, it can be used to establish the cleanliness level achieved by current cleaning process (“Benchmark”). The production can then be monitored to assure that benchmark cleanliness level is being achieved. In addition, the product can be followed through the manufacturing process to assure that no problems occur downstream as a result of inadequate surface cleanliness. The level of non-conformance related to the level of

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cleanliness achieved should be monitored. The cost associated with a given level of cleanliness and the cost of non-conformance associated with that given level of surface cleanliness should be combined to determine the “total cost”. Changes should be made to the cleaning process to minimize the “total cost”, i.e. optimize the “total cost”. The level of cleanliness associated with the “optimum total cost” should be considered the optimum or “acceptable” level of cleanliness. 8. ON-GOING, IN-PROCESS SURFACE CLEANLINESS MONITORING

Surface cleanliness monitoring system must be used to monitor the process and assure that the desired cleanliness level is being achieved on an on-going basis. Surface cleanliness monitoring system can be very useful in assessing how the surface cleanliness level is affected by making changes to the cleaning process or for evaluating alternative cleaning processes for their ability to achieve the desired cleanliness level. The required level of cleaning agent concentration in the cleaning solution can also be objectively determined and maintained by using a surface cleanliness monitoring system. Measuring the effect of varying the concentration level of the cleaning agent on surface cleanliness can help determine the “optimum” concentration level. In most industries, the chemical or cleaning agent replenishment schedule is usually time-dependent. The success of this approach relies on the level of contamination on each part and the number of parts processed in a given time interval being relatively constant. In real life, the amount of contamination can vary considerably from part to part. In addition, the number of parts cleaned during a given time frame can also vary considerably. A time-dependent replenishment schedule is not the ideal way of assuring product quality. On-going, in-process monitoring of surface cleanliness helps in replenishment of chemicals or cleaning agents only when needed, and not based on a pre-determined, somewhat arbitrary schedule. 9. SUMMARY

In order to define an acceptable level of cleanliness, it is important to minimize the total cost of cleaning. The total cost of cleaning is the sum of the cost of achieving a certain level of surface cleanliness and the cost of failure/nonconformance associated with that level of surface cleanliness. Selecting a method for monitoring cleanliness is the first step in establishing an acceptable level of cleanliness or defining “how clean is clean”. Several factors need to be considered in selecting an appropriate surface cleanliness method, which include, but are not limited to, type of contaminant to be detected, level of cleanliness to be monitored, acquisition and operating cost of the monitoring method, and the skill level

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required to operate the system. Surface cleanliness monitoring method may be direct, indirect or analytical. A monitoring method can be used to optimize the cleaning process by varying different parameters of the cleaning process while monitoring surface cleanliness to see how it is affected by the change. It can also help in ongoing monitoring of the cleaning process to assure that the desirable level of cleanliness is being achieved. REFERENCES 1. B. Kanegsberg and M. Chawla, “Non Volatile Residue”, A2C2 Magazine, 5, No. 3, 41 (2002) and 5, No. 4, 45 (2002). 2. R.L. Gause, “A Noncontacting Scanning Non Contact Photoelectron Emission Technique for Bonding Surface Cleanliness Inspection”, Marshall Space Flight Center, Huntsville, AL, presented at Fifth Annual NASA Workshop, Cocoa Beach, Florida (1987). 3. B. Kanegsberg and M. Chawla, “Contact Angle”, A2C2 Magazine, 4, No. 8, 41 (2001). 4. Surface Quality Monitors Brochure, Photo Emission Tech., Inc. 5. B. Kanegsberg and M. Chawla, “MESERAN”, A2C2 Magazine, 4, No. 9, 49 (2001). 6. B. Kanegsberg and M. Chawla, “Total Organic Carbon”, A2C2 Magazine, 4, No. 10, 37 (2001). 7. Charles Evans Associates Website – www.cea.com 8. R.D. Cormia, “Problem-Solving SURFACE ANALYSIS Techniques”, Surface Sciences Laboratories, Mountain View, CA: Advanced Materials & Processes, 16-23 (Dec. 1992). 9. Measurement and Characterization Website – www.nrel.gov/measurements/surface/html 10. M. Chawla, “How Clean is Clean? Measuring Surface Cleanliness and Defining Acceptable Level of Cleanliness”, in Handbook for Critical Cleaning, B. Kanegsberg and E. Kanegsberg (Eds.), pp. 415-430, CRC Press, Boca Raton, FL (2001).

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Surface Contamination and Cleaning, Vol. 1, pp. 43–48 Ed. K.L. Mittal © VSP 2003

Tracking surface ionic contamination by ion chromatography BEVERLY NEWTON∗ Dionex Corporation, 500 Mercury Drive, Sunnyvale, CA 95032, USA

Abstract—Surface ionic contamination can cause device failures. In order to find the source of the contamination many questions must be answered first. Are the failures due to incoming materials that are not clean? Has there been a change in the process that is introducing contamination? What is the exact nature of the contaminant, ionic, particulate, metallic, etc? Is there a training issue that needs to be addressed. Can the failure be tested for or is it a long term reliability problem? These are just a few of the questions that must be answered as part of the troubleshooting process. This paper addresses how ion chromatography can be used to troubleshoot a manufacturing or cleaning process and to assure the quality and reliability of electronic devices. Topics covered include: 1. What is ion chromatography. 2. How does it differ from other cleanliness testing methods. 3. How can ion chromatography be used to troubleshoot a cleaning process. 4. Real life examples showing how the use of ion chromatography has improved cleaning processes. Keywords: Ionic contamination; ion chromatography; electronic devices.

1. INTRODUCTION

As electronic devices and assemblies become smaller and more complex, the requirements for improved quality control of product cleanliness have begun to escalate. Surface contamination from ions such as chloride, bromide, sodium, and organic acids has been shown to cause failures in electronic devices [1]. Ionic residues can cause corrosion, metal migration and electrical leakage. The failures cased by these residues may be hard or soft failures and may occur several months after the product has been manufactured and shipped to customers. Upon re-testing the returned product, the failures can be intermittent or “no trouble found” making troubleshooting the device for a root cause of the failure difficult. These residues may be on the exposed surface of an electronic device, they may be encapsulated in flux or resin deposits, they may be trapped under surface mounted devices or they may be encapsulated in polymer finishes (Figure 1). ∗

Phone: (408)4814272, Fax: (408)7372470, E-mail: [email protected]

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Figure 1. Chromatogram of a board extract using IPA/water mixture.

Figure 2. Analysis of a cassette used to transport disk drive components during manufacture.

The manufacture of electronic devices typically involves a series of chemical and mechanical operations such as plating, masking, soldering, rinsing, etching, cleaning, etc. Each of these operations along with the environment in which they occur leaves some effect on the device or assembly. The processes and manufacturing environment leave chemical “fingerprints” on the device that are unique to the manufacturing process. In the same way that a forensic scientist would use fingerprints to trace a criminal, analytical techniques can be used to troubleshoot a manufacturing process or field failure to understand and correct the root cause.

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Ionic contamination can also be found on materials that come in contact with electrical devices during manufacture, e.g. gloves, cassettes, etc. (Figure 2). These manufacturing consumables can transfer contamination to the manufactured products and need to be examined for contamination in the same way that the final product is evaluated. 2. TYPES OF IONIC CONTAMINATION

Potentially corrosive ions found on printed circuit boards and electronic devices include: ● Bromide - commonly found in solder masks ● Sulfate - comes from a variety of materials such as oils and release agents ● Chloride - commonly found in fluxes ● Organic acids such as adipic or succinic acid - found in fluxes Typically, the higher the concentration of corrosive ions on a particular assembly, the higher the risk of electrochemical failure. 3. TEST METHODS FOR IONIC CONTAMINATION

In the past, electronic component manufacturers, board manufacturers and electronic assemblers have relied on resistivity of solvent extract (ROSE) type test methods to assure ionic cleanliness. Several studies reported by Contamination Studies Laboratory (CSL, Kokomo, IN) have shown that the ROSE method is inadequate for true quantification of ionic contamination. Recently, a modified ROSE method has been proposed as an IPC (Association Connecting Electronics Industries) Standard Method IPC-TM-650 2.3.25.1. Although this new technique is an improvement for reporting overall ionic contamination, it too provides insufficient information to troubleshoot the root cause of electronic failures caused by ionic contamination. The technique of ion chromatography is uniquely qualified for troubleshooting the root cause of failures due to ionic contamination on electronic devices and printed circuit boards. Ion chromatography can provide information on the chemical nature of the residue causing the failure. The output of the ion chromatograph is called a “chromatogram” and gives the identity and quantity of each ion found in a sample of a rinse extract of the device of interest. Ion chromatography is a form of liquid chromatography. The technique is based on the use of specialized column packings for analytical separation of ions found in a chemical mixture. The main advantages of ion chromatography for residue analysis are: ● Multi-component ion analysis ● Most sensitive detection technique available for many ionic compounds ● Method versatility

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Figure 3. Ion chromatography system configuration.

Ion chromatography is an analytical technique used to separate, identify and quantify ions in a sample matrix such as a water extract of a printed circuit board. The simplest ion chromatography system is composed of a sampling device, a pump, an analytical column, a suppressor and a detector (Figure 3). The analysis begins with a sample, typically a water extract containing ions of interest such as chloride, sulfate, or nitrate. A portion of the sample is injected into the ion chromatography system and combined with an eluent stream composed of sodium hydroxide or bicarbonate solution. The eluent stream carries the sample through the ion chromatography system to the analytical column. The analytical column separates the ions of interest in the sample into narrow bands within the stream of the eluent. Thus, by the time the sample leaves the analytical column, all of the chloride ions are grouped together, then all of the nitrate ions and then all of the sulfate ions. The eluent then sweeps these groups of ions into the suppressor device. This device electrolytically transforms the eluent into pure water leaving just the ions of interest in pure water to be swept along to the conductivity detector. The detector detects the ions based on their conductivity relative to the water eluent. At this point all interfering ions have been removed and the detector’s sensitivity has been maximized allowing for detection of very low (part per billion) levels of ions [2]. This is a very simplified explanation of ion chromatography but it is important to note that more complex samples and analytes can also be analyzed using this technique (for instance, cations such as sodium and magnesium, transition metals such as iron and copper and even certain biological analytes such as amines and nucleic acids).

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4. TRACKING SURFACE IONIC CONTAMINATION IN MANUFACTURING AND ASSEMBLY OPERATIONS

There has been a growing interest in the analysis of ionic contamination on electronic components. Absolute contamination level requirements and guidelines have not been determined; however, Contamination Studies Laboratory (CSL, Kokomo, IN) recommends maximum levels of chloride ion in the range 1.0 µg/sq. in for assembled boards with sensitive components such as microBGAs. The level recommended for bare boards is less than 2.0 µg/sq. in [3]. Ion chromatography provides the unique capability of identifying the individual ions for a given contamination issue. Since the source for chloride contamination can be much different than the source for organic acid contamination it is important to know which ions the manufacturer is dealing with in order to understand and correct the root cause of the problem. This is not possible with resistivity of solvent extract (ROSE) measurements. The capability to identify and quantify individual ions makes ion chromatography a valuable troubleshooting tool for process contamination issues and process monitoring programs. In addition to being the most economical analytical technique for monitoring multiple ions, ion chromatography also provides the ability to distinguish between noncorrosive and corrosive ions, something that ROSE testing is unable to do. A number of studies have been published to show the use of ion chromatography to troubleshoot reliability issues with electronic products. One of the best sources of case study information can be found on the web site for Contamination Studies Laboratory (CSL) at www.residues.com. CSL regularly publishes case studies showing the hazards of ionic contamination to electronic device reliability on their web site and in each issue of Circuits Assembly magazine. A good explanation of how ion chromatography has been used to identify sources of CAF (conductive anodic filament) failures can be found in a study completed by Ready et al. [4]. Several studies [5-7] have been completed on the analysis of ionic contamination on failed disk drive components. As mentioned earlier, manufacturing materials and packaging can be an important source of ionic contamination. Two recent studies by Lin and Graves [8] and Bahten and McMullen [9] provide information on the use of ion chromatography for the analysis of ionic contamination on materials such as pink poly film (a common packaging material) and cleaning brushes. 5. STANDARD TEST METHODS FOR TRACKING IONIC CONTAMINATION

IPC (Association Connecting Electronics Industries) has standard test methods documented for the ROSE, Modified ROSE and Ion Chromatography analysis techniques. These are:

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IPC-TM-650, TM 2.3.25 Detection and Measurement of Ionizable Surface Contaminants by Resistivity of Solvent Extract (ROSE). ● IPC-TM-650, TM 2.3.25.1 Ionic Cleanliness Testing of Bare PWBs (modified ROSE Test Method). ● IPC-TM-650, TM 2.3.28 Ionic Analysis of Circuit Boards, Ion Chromatography Method. IDEMA (International Disk Drive Equipment and Materials Association) has developed the following standard test method for ionic cleanliness testing. ● M13-99, Measurement of Extractable/Leachable Anion Contamination on Drive Components by Ion Chromatography. ●

6. CONCLUSION

The ion chromatography, ROSE and modified ROSE test methods have been developed to allow electronics manufacturers to identify and control ionic contamination before it evolves into a failed component or board. Tracking ionic contamination requires systematic troubleshooting and improved cleanliness of the product as it is manufactured. This means cleaner raw materials and processes which are controlled by systematic analysis using standard methods such as those documented by IPC, IDEMA, IEST (Institute of Environmental Sciences and Technology), and SEMI (Semiconductor Equipment and Materials International). REFERENCES 1. D. Yang, C. Lee, Y. Yang, E. Kaiser, S. Heberling and B. Newton, Precision Cleaning, 17-23 (May 1998). 2. B. Newton, Precision Cleaning, 38-39 (March 2000). 3. D. Pauls and T. Munson, Circuits Assembly, 110-112 (September 1998). 4. W.J. Ready, B.A. Smith, L.J. Turbini and S.R. Stock, Mater. Res. Soc. Symp. Proc. 515, 45-54 (1998). 5. A. Toxen, A2C2, 13-16 (September 1998). 6. P. Mee, M. Smallen and D. Vickers, IDEMA Insight, 1 (March/April 1997). 7. J. Thompson, T. Prommanuwat, A. Siriraks and S. Heberling, IDEMA Insight, 24-29 (May/June 1999). 8. S. Lin and S. Graves, Micro, 95-106 (October 1998). 9. K. Bahten and D. McMullen, Proc. Semiconductor Pure Water and Chemicals Conference, 355364 (March 1999).

Surface Contamination and Cleaning, Vol. 1, pp. 49–73 Ed. K.L. Mittal © VSP 2003

A new method using MESERAN technique for measuring surface contamination after solvent extraction MARK G. BENKOVICH∗,1 and JOHN L. ANDERSON2 1

Honeywell Federal Manufacturing & Technologies,† PO Box 419159, D/833 MS-2C43, Kansas City, MO 64141-6159 2 ERA Systems, Inc., The MESERAN Company, PO Box 3609, Chattanooga, TN 37404-0609

Abstract—The precision analytical technique known as MESERAN Analysis permits, in 2 minutes, quantitative measurement of the level of pre-existing nonvolatile organic residue (NVOR) on a substrate from 100 µg/cm2. MESERAN Analysis is also applicable for determining NVOR deposited from solvents and solvent extracts. The MESERAN method is able to quantify organic contamination levels down to and below 1 ng by depositing as little as 10 µL of solvent containing a known amount of contamination on a clean substrate, allowing it to evaporate, and measuring the resultant residue. The method is described in detail. In addition, NVOR measurements determined from MESERAN data are presented for a specific project conducted at Honeywell Federal Manufacturing & Technologies (FM&T), Kansas City Plant (KCP). Keywords: MESERAN; surface contamination; solvent extraction; non-volatile organic residue.

DEFINITIONS

In this paper a number of abbreviations, special terms, and trademarks are employed: (1) µCi means microCurie, a unit of radiation which corresponds to 3.7 E 4 (37,000) disintegrations per second. (2) Carbon-14 (C-14) refers to the radioactive isotope of the element Carbon, an isotope which emits only soft or low energy beta particles; most C-14 beta particles are stopped by a sheet of paper. (3) USNRC EXEMPT means the very low level of Carbon-14 that is not regulated by the U S Nuclear Regulatory Commission. No license is required for ∗ To whom all correspondence should be addressed. Phone: (816) 997-3529, Fax: (816) 997-2049, E-mail: [email protected] † Operated for the United States Department of Energy under prime contract DE-AC04-01AL66850. ãCopyright Honeywell LLC, 2002.

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possession or use. Only USNRC licensed companies are permitted to distribute EXEMPT quantities not to exceed ten 100 µCi of C-14 (or combinations of smaller quantities that added up to 100 µCi of C-14) at one time. Multiple quantities may be stored by the user. Shipments may be made to anyone in the US. Foreign shipments under IATA regulations must conform to the regulations of the country of final destination. (4) ng means nanogram (1 E - 9 grams or 0.000000001 grams); µg means microgram (1 E - 6 grams or 0.000001 grams), mg means milligram (1 E - 3 grams or 0.001 grams). (5) µL means microliter (1 E - 6 liter or 0.000001 liter). (6) GM detector refers to a thin end-window Geiger Müller detector tube which detects the C-14 beta emissions which penetrate through the 1.4–2.0 mg/cm2 mica window. (7) One nanomole (nmole) is 1 E - 9 moles which equals approximately. 6 E 14 molecules (from Avogadro’s ~ 6 E 23 molecules per gram mole). (8) 1 square centimeter (sq cm) with a roughness factor of 3 is equal to 3 E 16 square Angstroms. (9) Monolayer refers to the number of molecules of a material which covers 1 sq cm in a conventional non-close-packed configuration. For example, on a smooth, flat surface with a roughness factor of 3, each molecule of ntridecane occupies about 50 sq. Angstroms – which equates to ~ 6 E 14 molecules per sq. cm – i.e. one nanomole. (10) NVR means non-volatile residue; NVOR means non-volatile organic residue. (11) MESERAN is an acronym for Measurement and Evaluation of Surfaces by Evaporative Rate ANalysis. (12) MESERAN, MicroSolventEvaporator (MSE), MicroOrganicResidue, and MOR are trademarks licensed to ERA Systems, Inc. (13) Ln or ln is the natural logarithm. (14) 1 mg/ft2 is equivalent to 1.0764 µg/cm2 or 1 µg/cm2 is equivalent to 0.929 mg/ft2. 1. INTRODUCTION

The principle of the MESERAN technique was discovered by one of us (JLA) in 1960. This analytical technique is used in a number of industrial and governmental facilities (within the United States and abroad) for research and development purposes as well as for quality and production control. The characterization of the surface being analyzed is carried out by depositing a chemical detector onto the test surface and observing the rate at which the chemical detector disappears from the surface. The MESERAN technique is routinely used for quantifying organic

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contamination on surfaces and the crosslink density (or degree of cure) in polymers. In addition, the MESERAN technique can be used for quantifying chemically active sites on surfaces [1-3]. Honeywell FM&T, KCP (henceforth KCP) has been using MESERAN Analyzers for approximately 30 years to detect and quantify organic contamination on parts and evaluate various cleaning processes for removing organic contamination. KCP has used MESERAN Analyzers extensively to evaluate the ability of alternate solvents and processes for removing specific organic contaminants to eliminate the use of chlorinated and fluorinated solvents [4-10]. In recent years, KCP has been working on several projects with The MESERAN Company to improve data analysis and develop new methods for using the MESERAN technology [11-14]. 2. PRINCIPLE OF MESERAN TECHNIQUE [15]

The standard microcomputer-based MESERAN technology involves deposition, using a “clean” precision microsyringe, onto a flat or concave surface of 18 µL of a test solution consisting of a low boiling solvent or solvent combination (for these evaluations – cyclopentane) and a high-boiling-but-volatile Carbon-14 labeled compound (in a ratio of approximately 60,000:1). Figure 1 shows the application of test solution. For example, the amount of tridecane-C14 radiochemical per single test (< 0.06 µCi) corresponds to approximately 6 E 14 (6 x 1014) molecules which equates to one nanomole, the equivalent of approximately one molecular layer over one square centimeter. Metered air or nitrogen gas is permitted to flow across the surface and between the surface and a Geiger Müller detector positioned directly above the surface. The evaporation of the low boiling solvent and then the radiochemical is observed as a function of time by recording the detected emissions per second arising from the radiochemical molecules remaining on, or retained by, the surface – the vapor-phase, already-evaporated molecules having been swept out from under the detector by the metered gas (see Figure 2).

Figure 1. Application of test solution.

Figure 2. Measurement of emissions.

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Each test takes less than 3 minutes and the amount of radiochemical employed is EXEMPT from U S Nuclear Regulatory Commission and/or ‘Agreement State’ licensing regulations due to the very low level of C-14 involved. For the measurement of microorganic residues, the MESERAN method may be used: (1) Directly on a flat or concave surface and any microorganic residue thereon which is chemically compatible with the particular radiochemical employed, or (2) Indirectly using an extracting solvent followed by depositing and evaporating an aliquot amount onto a “clean” reference surface. Subsequent deposition and evaporation of the radiochemical solution permits measurement of the amount of deposited residue by comparing the results with previously obtained standards similarly deposited from volumetric dilutions. For non-polar and/or hydrocarbon type residues, tridecane-C14 in cyclopentane (designated BK) is employed. For more polar residues, tetrabromoethane-C14 in cyclopentane (designated AK) is used. In order to provide a high number of detected emissions for the minimal amount of radiochemical deposited, the tridecane-C14 has a specific activity of approximately 57 µCi/µmole (one carbon atom of tridecane is essentially pure C14 isotope) while the tetrabromoethane has both carbon atoms labeled (approximately 114 µCi/µmole). Approximately 200 ng of radiochemical are deposited in each test with similar levels of radioactivity. The MESERAN method assumes that the particular radiochemical employed is chemically compatible with the residue, that the test solution droplet covers all of the residue, and that the test solution solvent substantially dissolves the residue within the time period of the solvent evaporation. Attention to the avoidance of inadvertent contamination and the maintenance of reasonably constant temperature and pressure are required for optimal reproducibility from test to test. 2.1. Mechanism of the MESERAN technique for quantifying organic residues [1-3, 11-14] When a homogeneous chemical is permitted to evaporate, the classical mechanism of the process (normally measured by monitoring the already evaporated portion) follows first order kinetics, i.e., the plot of log concentration vs. time is a straight line. This mechanism applies to pure materials as well as to solutions of chemicals in which the components are chemically compatible and in which the second component is non-volatile under the conditions of the process. In the presence of the second component, the rate of evaporation is slowed. In the MESERAN technology, however, the amount of radiochemical retained by the surface as a function of time is measured by counting the emissions arising from the radiochemical molecules remaining on the surface. In this discussion, the temperature and pressure are assumed constant and the concentration of already evaporated molecules in the adjacent gaseous phase approaches zero due to the flowing air or nitrogen referred to above. The molecular weight of each evaporating molecule and the intermolecular forces among the near-neighbor molecules

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are thus the primary factors in determining the tendency of each molecule to remain in solution or conversely to escape from the liquid portion of the air/liquid (or semisolid) interface. In the MESERAN technology, which employs only a monolayer equivalent of the radiochemical, the observed rate of evaporation is thus a function of the residual concentration of the non-evaporated molecules of the Carbon-14 radiochemical. Figure 3 illustrates the typical evaporation of the radiochemical solution from a clean surface. The A-B line represents the evaporation of the low boiling solvent (e.g., cyclopentane). The rationale for the initial increase in counts/second is that the C-14 soft beta emissions are partially absorbed by the solvent molecules. B represents the point at which substantially all of the low boiling solvent has evaporated and the maximal amount of residual radiation reaches the GM detector. The B-C line represents the evaporation of the radiochemical from the surface under the conditions of the test. C represents a level where the GM detector can no longer adequately differentiate the residual radiation from background. A solution of the high-boiling-but-volatile tridecane-C14 in higher boiling hydrocarbons (i.e., contamination) follows a similar but slower path than does the evaporation of the labeled tridecane itself since the non-volatile “residue” molecules occupy increasing portions of the liquid (or semi-solid) interface. The rate at which the solvent evaporates is slowed somewhat and the rate at which the radiochemical evaporates is slowed considerably with the observed rate of evaporation being a function of the amount of residue on the surface. The observed rate of evaporation of the radiochemical (the slope expressed as a positive integer) thus is an inverse measure of the amount of non-evaporating residue. The lower the slope, the more the residue and vice versa. Figure 4 illustrates typical evaporations of the radiochemical solution with increasing amounts of residue. ABC is repeated from Figure 3 and illustrates a typical evaporation of the radiochemical solution with no interactions from residue (i.e., a clean substrate). A*B*C* illus-

Figure 3. Typical evaporation of radiochemical solution from a clean surface.

Figure 4. Typical evaporations of radiochemical solution with increasing amounts of residue.

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trates a typical evaporation of the radiochemical solution with some contamination present. A**B**C** illustrates a typical evaporation of the radiochemical solution with a larger amount of contamination present. 2.2. Methods of analyzing MESERAN data [1-14] There are two general methods for analyzing the MESERAN data: (1) Total Counts (total area under each curve based on counts minus background) and (2) Slope of the evaporation of radiochemical (the post-peak portion of the curve). Based upon raw data minus background, Figure 5 illustrates three typical experimentally derived curves of natural logarithm (Ln or ln) counts per second minus background vs. time in seconds. Figure 5 is similar to Figure 4 except raw data from actual tests are shown. In Figure 5, the upper curve represents a high level of organic residue, the middle curve represents a medium level of organic residue, and the lower curve represents a low level of organic residue. The scatter, particularly at the lower values, is due to the inherent randomness of radiation (the Poisson distribution in which the square root of each count total is the best estimate of one standard deviation).

Figure 5. Plot of raw data showing low, medium, and high levels of organic contamination.

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In an effort to reduce the effect of the randomness of radiation, the data (ln (counts – background)) are “smoothed” from six seconds through 115 seconds (for 120 second length tests) and replotted. The smoothing is accomplished by summing the ln (counts – background) at the sixth second with the previous five seconds and the subsequent five seconds and dividing this number by 10. A divisor of 10 is used instead of 11 because it is statistically sound to take the number of items being smoothed and subtract one from it because a degree of freedom is lost. This process is carried out through the 115th second and the subsequent data are replotted as smoothed (ln (counts – background)) versus time. Figure 6 represents the same data as in Figure 5 except that the data in Figure 6 are logarithmically smoothed to increase the reliability of the individual points. The plotted smoothed curve is then analyzed via linear regression to determine the slope of the post-peak line (down to near background) which best fits the data representing the evaporative process. The determined slope is multiplied by –10,000 to convert it to a positive integer; this becomes the reported MESERAN slope value with units of smoothed (ln (counts – background))/sec x (–10,0000).

Figure 6. Logarithmic plot of smoothed data showing low, medium, and high levels of organic contamination.

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The slope method is more sensitive, especially at low levels of contamination. For measuring microorganic residues, the total counts method of analysis (i.e., the area under each overall curve based on actual counts) is valid from somewhat less than 200 ng to approximately 100,000 ng (100 µg). Higher total counts are indicative of higher organic residue levels and vice versa. The slope method of data analysis, normally based on statistically smoothed data and based on the log count vs. time relationship, increases the sensitivity of the lower limit markedly (to less than 1 ng) since the total counts method (the total area under each curve) approaches statistical insignificance somewhat below 200 ng. Expressed as a positive integer, the higher (or steeper) the slope, the cleaner the surface and conversely, the lower (or more flat) the slope, the higher the residue. Both the total counts and slope methods of analysis can be used qualitatively or quantitatively. The total counts method has been used for approximately 30 years at KCP by testing a surface with the MESERAN Analyzer and comparing the results to those obtained from known clean standards for that particular surface. The total counts of the clean standard are subtracted from the total counts obtained on the surface being tested to give a net total counts representing the contamination amount. This result can be compared to previously performed calibrations of contamination to obtain a quantitative result for the contamination amount. Similarly, the slope method can be used to compare the slope obtained on the surface being tested to the slope obtained from known clean standards for that particular surface. The slope can also be compared to previously performed calibrations of contamination to obtain a quantitative result for the contamination amount. In many cases, quantitative data are not needed. For instance, if one is performing process control work to determine if the cleaning process is performing as designed, quantitative data on the actual amount of contamination may not be necessary. Often times, as long as the parts being cleaned are less than a certain level of contamination, they are clean enough. Therefore, one only has to establish the MESERAN total counts or MESERAN slopes that correspond to that level of contamination and relate the tests as being in compliance or not. KCP has used this technique for years to control cleanliness and compare the abilities of different cleaners and cleaning processes to remove various contaminants. Net total count values were established that corresponded to electrical failures and catastrophic adhesion failures. As long as the MESERAN net total counts were below these levels, no cleaning related failures occurred [4-10]. In recent years, KCP has been incorporating the use of the slope technique to give more quantifiable data for lower amounts of contamination. Calibrations of various contaminants have been performed by KCP to develop calibration curves for these contaminants on substrates of interest. MESERAN slope results obtained can now be compared to the calibration curves to determine quantitative amounts of contamination detected [11-14]. The volumetric dilution process for making calibration solutions is shown in Figure 7.

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Figure 7. Calibration solutions formulations.

Volumetric dilutions were used to make contamination solutions for depositing known amounts of the organic contaminant on reference substrates (e.g., aluminum panels, stainless steel disks, glass cones, etc.). These calibrations were performed in the following manner. A master calibration solution was prepared in a 10-mL volumetric flask by dissolving 100 mg of the organic contaminant in 10 mL of solvent (e.g., cyclopentane, methylene chloride, or hexane that has been double distilled in an all-glass still with no grease in the joints – NVOR of these solvents are approximately 10 ppb). The master calibration solution was thoroughly mixed and 1 mL of this solution was placed in another 10-mL volumetric flask. The second volumetric flask was then diluted with the double distilled solvent until the solution level was at 10 mL and this solution was thoroughly mixed. Subsequent dilutions were carried out in a similar fashion. Ten microliters (µL) of each calibration solution were deposited on the precleaned substrates and allowed to evaporate. This resulted in the following amounts of contamination on the substrates: 1 ng, 10 ng, 100 ng, 1 µg, 10 µg, and 100 µg. Some intermediate levels were obtained by depositing 3 µL and 5 µL of the calibration solutions. The substrates that were contaminated were then tested using the MESERAN Analyzer to develop a calibration curve for the contaminant. For example, calibration curves for Dioctyl Phthalate (DOP) using radiochemical test solution AK (tetrabromoethane-C14 in cyclopentane) on aluminum panels are shown in Figures 8 and 9. Figure 8 shows the calibration curve for DOP using the total counts method of analysis. Figure 9 shows the calibration curve for DOP using the slope method of analysis. As can be seen from examining Figure 8, the total counts method of analysis loses its ability to differentiate contamination amounts (i.e., loses its statistical significance) below a few hundred nanograms of contamination (approximately a monolayer). However, the slope method of analysis shown in Figure 9 is able to differentiate contamination amounts down to 1 ng. In general practice, total counts can be used to quantify contamination amounts greater than a monolayer (a few hundred nanograms) up towards the 100 µg range. The slope method can be used to quantify contamination levels well below the monolayer (down to a nanogram) as well as up to approximately 100 µg.

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Figure 8. Calibration curve for DOP on aluminum panels using MESERAN total counts.

Figure 9. Calibration curve for DOP on aluminum panels using MESERAN low variance slopes.

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Whenever possible it is advantageous to develop calibration curves for the contaminants of interest. KCP has developed calibration curves for numerous contaminants typically encountered in KCP operations such as oils, greases, mold releases, solder fluxes, resins, etc. However, since one does not always know all of the contaminants that may be present on a part, KCP developed a general calibration curve to use for unknown samples based upon hydrocarbon residues. Thus far, most hydrocarbon residues tested have similar calibration curves for the various amounts of residue. 3. EXPERIMENTAL

3.1. Purpose KCP conducted a cleanliness evaluation to determine the NVOR amounts on aluminum and stainless steel panels which were machined using KCP machining fluids and cleaning processes chosen for production of hardware for a particular customer. This section of the paper describes how KCP used recent advances in MESERAN technology to determine the NVOR amounts on four stainless steel panels and four aluminum panels (31 in2 each, excluding edges) by extracting the panels with methylene chloride and quantifying the extracted residues in mg/ft2. The virgin methylene chloride solvent was also evaluated so that its contribution could be subtracted from the solvent extracts. The customer specifically requested that the results be reported in mg/ft2 as opposed to µg/cm2, therefore that is how the results are reported in this paper. It is common practice in the Aerospace industry (as well as other industries) to report contamination amounts on large surfaces in mg/ft2. The conversion factors for these units are 1 mg/ft2 is equivalent to 1.0764 µg/cm2 or 1 µg/cm2 is equivalent to 0.929 mg/ft2. 3.2. Sample details For the NVOR evaluations, four samples each of the aluminum and stainless steel (10 cm x 10 cm x 0.7 cm) were machined at KCP using particular machining fluids and associated machining methods. The four KCP machining fluids evaluated were a hydrocarbon blend (mixture of 70% Pennex N 47 and 30% Hangsterfer’s Hard Cut # 511) and three aqueous-based coolants (Cimtech 200, Trimsol, and Cimstar 3700). The suppliers for these materials are: Pennex N 47 – Exxon Company, Houston, TX; Hangsterfer’s Hard Cut #511 – Hangsterfer’s Laboratories, Mantua, NJ; Cimtech 200 – Cincinnati Milacron Marketing, Cincinnati, OH; Trim Sol – Master Chemical Corporation, Perrysburg, OH; and Cimstar 3700 – Cincinnati Milacron Marketing, Cincinnati, OH. Two of the stainless steel samples were improperly labeled; therefore, the contaminant for these two panels is not known for sure. They were either contaminated with the hydrocarbon blend or Cimtech 200 and are described as such in subsequent portions of this paper (including several tables). All of the stainless steel samples were passivated by the KCP plating

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group. This evaluation was conducted on these eight small samples to allow cleanliness verifications to be performed at KCP prior to cleaning large samples (25.4 cm x 25.4 cm x 1.3 cm) that would be sent to the customer for cleanliness verification. 3.3. Cleaning process The aluminum and stainless steel panels tested in this evaluation were cleaned using the following steps: (1) ultrasonic cleaned in Dirl-Lum 603 (30 g per liter concentration) for 5 minutes at 140°F (60°C), (2) rinsed in flowing DI water for 15– 30 seconds, (3) DI water rinsed in ultrasonic cascade rinse station with 3 tanks (30 seconds in each tank) at 110–115°F (43.3–46.1°C), (4) blown dry with filtered nitrogen, and (5) baked for 30 minutes minimum at 220°F (104.4°C )in a HEPA filtered convection oven with nitrogen flowing into the oven. The panels were then packaged in nylon bags and heat sealed. Dirl-Lum 603, supplied by Blue Wave Ultrasonics, Davenport IA, is a powdered alkaline cleaner. It contains sodium metasilicate, sodium carbonate, sodium tripolyphosphate, dodecyl benzene sulfonate, polyethoxyolated phenol, and nonyl phenol. 3.4. Customer cleanliness requirements and associated problems The customer has cleanliness level requirements for this hardware which can be extremely difficult to measure. The desired cleanliness of the hardware is