Paper AS-WeM9
The Effects of Pre-Processing of Secondary Ion Mass Spectrometry (SIMS) Image Data on Self-Modeling Image Analysis

Wednesday, October 22, 2008, 10:40 am, Room 207

SIMS imaging is a powerful technique for surface analysis. The data from SIMS results in hundreds or thousands of images corresponding to ions of different masses. In order to facilitate data analysis, data reduction techniques are required. One of the tools to reduce the massive amounts of data is self-modeling mixture analysis, which expresses the SIMS image data in a few images representing pure components and their associated mass spectra. This paper will focus on the pure variable approach. A pure variable has contributions from only one component in the mixture data set (i.e. a value of m/e to which only one chemical component contributes) and thus can be used as a relative concentration estimate to resolve the mixture data into pure component spectra and their contributions (“concentrations’) in the form of images. Similarly, pure pixels can be selected to resolve the mixture data. Image data are often of a noisy nature. Therefore, pre-processing of the data is often used to improve the results. A popular pre-processing for TOF-SIMS data is based on the Poisson nature of the data. This paper will show a modification of Poisson scaling procedure of the data, which makes it less susceptible to noise. Another way to enhance data analysis is using correlation based techniques to minimize the influence of outlying pixels. This paper will show how the data analysis results, as obtained with the pure variable/pixel approach, can be improved using the proper pre-processing tools, using data sets of actual samples of several chemical mixtures and a fused metal sample.

Sandia is a multiprogram laboratory operated by Sandia Corporation, a Lockheed Martin Company, for the United States Department of Energy’s National Nuclear Security Administration under contract DE-AC04-94AL85000.