Publications

Abstract not provided.

Abstract not provided.

Abstract not provided.

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.

Abstract not provided.