Publications

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Spectrum imaging combined with multivariate statistics is an approach to microanalysis that makes the maximum use of the large amount of data potentially collected in forensics analysis. Here, this study examines the efficacy of using spectrum imaging-enabled microscopies to identify chemical signatures in simulated bioagent materials. This approach allowed for the ready discrimination between all samples in the test. In particular, the spectrum imaging approach allowed for the identification of particles with trace elements that would have been missed with a more traditional approach to forensic microanalysis. Finally, the importance of combining signals from multiple length scales and analytical sensitivities is discussed.

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As the size of mechanical systems shrinks from macro- to nanoscales, surface phenomena such as adhesion, friction, and wear become increasingly significant. This paper demonstrates the use of alcohol adsorption as a means of continuously replenishing the lubricating layer on the working device surfaces and elucidates the tribochemical reaction products formed in the sliding contact region. Friction and wear of native silicon oxide were studied over a wide range of length scales from macro- to nanoscales using a ball-on-flat tribometer (millimeter scale), sidewall microelectromechanical system (MEMS) tribometer (micrometer scale), and atomic force microscopy (nanometer scale). In all cases, the alcohol vapor adsorption successfully lubricated and prevented wear. Imaging time-of-flight secondary ion mass spectrometry analysis of the sliding contact region revealed that high molecular weight oligomeric species were formed via tribochemical reactions of the adsorbed linear alcohol molecules. These tribochemical products seemed to enhance the lubrication and wear prevention. In the case of sidewall MEMS tests, the lifetime of the MEMS device was radically increased via vapor-phase lubrication with alcohol.

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Qualification of microsystems for weapon applications is critically dependent on our ability to build confidence in their performance, by predicting the evolution of their behavior over time in the stockpile. The objective of this work was to accelerate aging mechanisms operative in surface micromachined silicon microelectromechanical systems (MEMS) with contacting surfaces that are stored for many years prior to use, to determine the effects of aging on reliability, and relate those effects to changes in the behavior of interfaces. Hence the main focus was on 'dormant' storage effects on the reliability of devices having mechanical contacts, the first time they must move. A large number ({approx}1000) of modules containing prototype devices and diagnostic structures were packaged using the best available processes for simple electromechanical devices. The packaging processes evolved during the project to better protect surfaces from exposure to contaminants and water vapor. Packages were subjected to accelerated aging and stress tests to explore dormancy and operational environment effects on reliability and performance. Functional tests and quantitative measurements of adhesion and friction demonstrated that the main failure mechanism during dormant storage is change in adhesion and friction, precipitated by loss of the fluorinated monolayer applied after fabrication. The data indicate that damage to the monolayer can occur at water vapor concentrations as low as 500 ppm inside the package. The most common type of failure was attributed to surfaces that were in direct contact during aging. The application of quantitative methods for monolayer lubricant analysis showed that even though the coverage of vapor-deposited monolayers is generally very uniform, even on hidden surfaces, locations of intimate contact can be significantly depleted in initial concentration of lubricating molecules. These areas represent defects in the film prone to adsorption of water or contaminants that can cause movable structures to adhere. These analysis methods also indicated significant variability in the coverage of lubricating molecules from one coating process to another, even for identical processing conditions. The variability was due to residual molecules left in the deposition chamber after incomplete cleaning. The coating process was modified to result in improved uniformity and total coverage. Still, a direct correlation was found between the resulting static friction behavior of MEMS interfaces, and the absolute monolayer coverage. While experimental results indicated that many devices would fail to start after aging, the modeling approach used here predicted that all the devices should start. Adhesion modeling based upon values of adhesion energy from cantilever beams is therefore inadequate. Material deposition that bridged gaps was observed in some devices, and potentially inhibits start-up more than the adhesion model indicates. Advances were made in our ability to model MEMS devices, but additional combined experimental-modeling studies will be needed to advance the work to a point of providing predictive capability. The methodology developed here should prove useful in future assessments of device aging, however. Namely, it consisted of measuring interface properties, determining how they change with time, developing a model of device behavior incorporating interface behavior, and then using the age-aware interface behavior model to predict device function.

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Time-of-flight secondary ion mass spectrometry (TOF-SIMS) instruments are capable of saving an entire mass spectrum at each pixel of an image, allowing for retrospective analysis of masses that were not selected for analysis during data collection. These TOF-SIMS spectral images contain a wealth of information, but few tools are available to assist the analyst in visualizing the entire raw data set and as a result, most of the data are not analyzed. Automated, nonbiased, multivariate statistical analysis (MVSA) techniques are useful for converting the massive amount of data into a smaller number of chemical components (spectra and images) that are needed to fully describe the TOF-SIMS measurement. Many samples require two back-to-back TOF-SIMS measurements in order to fully characterize the sample, one measurement of the fraction of positively charged secondary ions (positive ion fraction) and one measurement of the fraction of negatively charged secondary ions (negative ion fraction). Each measurement then needs to be individually evaluated. In this paper, we report the first MVSA analysis of a concatenated TOF-SIMS date set comprising positive ion and negative ion spectral images collected on the same region of a sample. MVSA of concatenated data sets provides results that are intuitive and fully describe the sample. The analytical insight provided by MVSA of the concatenated data set was not obtained when either polarity data set was analyzed separately. © 2005 American Chemical Society.

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The performance and reliability of microelectromechanical (MEMS) devices can be highly dependent on the control of the surface energetics in these structures. Examples of this sensitivity include the use of surface modifying chemistries to control stiction, to minimize friction and wear, and to preserve favorable electrical characteristics in surface micromachined structures. Silane modification of surfaces is one classic approach to controlling stiction in Si-based devices. The time-dependent efficacy of this modifying treatment has traditionally been evaluated by studying the impact of accelerated aging on device performance and conducting subsequent failure analysis. Our interest has been in identifying aging related chemical signatures that represent the early stages of processes like silane displacement or chemical modification that eventually lead to device performance changes. We employ a series of classic surface characterization techniques along with multivariate statistical methods to study subtle changes in the silanized silicon surface and relate these to degradation mechanisms. Examples include the use of spatially resolved time-of-flight secondary ion mass spectrometric, photoelectron spectroscopic, photoluminescence imaging, and scanning probe microscopic techniques to explore the penetration of water through a silane monolayer, the incorporation of contaminant species into a silane monolayer, and local displacement of silane molecules from the Si surface. We have applied this analytical methodology at the Si coupon level up to MEMS devices. This approach can be generalized to other chemical systems to address issues of new materials integration into micro- and nano-scale systems.

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Time-of-flight secondary ion mass spectrometry (TOF-SIMS) by its parallel nature, generates complex and very large datasets quickly and easily. An example of such a large dataset is a spectral image where a complete spectrum is collected for each pixel. Unfortunately, the large size of the data matrix involved makes it difficult to extract the chemical information from the data using traditional techniques. Because time constraints prevent an analysis of every peak, prior knowledge is used to select the most probable and significant peaks for evaluation. However, this approach may lead to a misinterpretation of the system under analysis. Ideally, the complete spectral image would be used to provide a comprehensive, unbiased materials characterization based on full spectral signatures. Automated eXpert spectral image analysis (AXSIA) software developed at Sandia National Laboratories implements a multivariate curve resolution technique that was originally developed for energy dispersive X-ray spectroscopy (EDS) [Microsci. Microanal. 9 (2003) 1]. This paper will demonstrate the application of the method to TOF-SIMS. AXSIA distills complex and very large spectral image datasets into a limited number of physically realizable and easily interpretable chemical components, including both spectra and concentrations. The number of components derived during the analysis represents the minimum number of components needed to completely describe the chemical information in the original dataset. Since full spectral signatures are used to determine each component, an enhanced signal-to-noise is realized. The efficient statistical aggregation of chemical information enables small and unexpected features to be automatically found without user intervention.

Analytical instrumentation such as time-of-flight secondary ion mass spectrometry (ToF-SIMS) provides a tremendous quantity of data since an entire mass spectrum is saved at each pixel in an ion image. The analyst often selects only a few species for detailed analysis; the majority of the data are not utilized. Researchers at Sandia National Laboratory (SNL) have developed a powerful multivariate statistical analysis (MVSA) toolkit named AXSIA (Automated eXpert Spectrum Image Analysis) that looks for trends in complete datasets (e.g., analyzes the entire mass spectrum at each pixel). A unique feature of the AXSIA toolkit is the generation of intuitive results (e.g., negative peaks are not allowed in the spectral response). The robust statistical process is able to unambiguously identify all of the spectral features uniquely associated with each distinct component throughout the dataset. General Electric and Sandia used AXSIA to analyze raw data files generated on an Ion Tof IV ToF-SIMS instrument. Here, we will show that the MVSA toolkit identified metallic contaminants within a defect in a polymer sample. These metallic contaminants were not identifiable using standard data analysis protocol.

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The effect of the density and in-plane distribution of interfacial interactions on crack initiation in an epoxy-silicon joint was studied in nominally pure shear loading. Well-defined combinations of strong (specific) and weak (nonspecific) interactions were created using self-assembling monolayers. The in-plane distribution of strong and weak interactions was varied by employing two deposition methods: depositing mixtures of molecules with different terminal groups resulting in a nominally random distribution, and depositing methyl-terminated molecules in domains defined lithographically with the remaining area interacting through strong acid-base interactions. The two distributions lead to very different fracture behavior. For the case of the methyl-terminated domains (50 {micro}m on a side) fabricated lithographically, the joint shear strength varies almost linearly with the area fraction of strongly interacting sites. From this we infer that cracks nucleate on or near the interface over nearly the entire range of bonded area fraction and do so at nearly the same value of local stress (load/bonded area). We postulate that the imposed heterogeneity in interfacial interactions results in heterogeneous stress and strain fields within the epoxy in close proximity to the interface. Simply, the bonded areas carry load while the methyl terminated domains carry negligible load. Stress is amplified adjacent to the well-bonded regions (and reduced adjacent to the poorly bonded regions), and this leads to crack initiation by plastic deformation and chain scission within the epoxy near the interface. For the case of mixed monolayers, the dependence is entirely different. At low areal density of strongly interacting sites, the joint shear strength is below the detection limit of our transducer for a significant range of mixed monolayer composition. With increasing density of strongly interacting sites, a sharp increase in joint shear strength occurs at a methyl terminated area fraction of roughly 0.90. We postulate that this coincides with the onset of yielding in the epoxy. For methyl-terminated area fractions less than 0.85, the joint shear strength becomes independent of the interfacial interactions. This indicates that fracture no longer initiates on the interface but away from the interface by a competing mechanism, likely plastic deformation and chain scission within the bulk epoxy. The data demonstrate that the in-plane distribution of interaction sites alone can affect the location of crack nucleation and the far-field stress required.