Hostname: page-component-cd9895bd7-gvvz8 Total loading time: 0 Render date: 2024-12-28T05:59:45.109Z Has data issue: false hasContentIssue false
  • English
  • Français

Automated Analysis of SEM X-Ray Spectral Images: A Powerful New Microanalysis Tool

Published online by Cambridge University Press:  31 January 2003

Paul G. Kotula ,
Michael R. Keenan  and
Joseph R. Michael
Paul G. Kotula
Affiliation:
Materials Characterization Department, Sandia National Laboratories, P.O. Box 5800, MS 0886, Albuquerque, NM 87185-0886, USA
Michael R. Keenan
Affiliation:
Materials Characterization Department, Sandia National Laboratories, P.O. Box 5800, MS 0886, Albuquerque, NM 87185-0886, USA
Joseph R. Michael
Affiliation:
Materials Characterization Department, Sandia National Laboratories, P.O. Box 5800, MS 0886, Albuquerque, NM 87185-0886, USA
Get access

Abstract

Spectral imaging in the scanning electron microscope (SEM) equipped with an energy-dispersive X-ray (EDX) analyzer has the potential to be a powerful tool for chemical phase identification, but the large data sets have, in the past, proved too large to efficiently analyze. In the present work, we describe the application of a new automated, unbiased, multivariate statistical analysis technique to very large X-ray spectral image data sets. The method, based in part on principal components analysis, returns physically accurate (all positive) component spectra and images in a few minutes on a standard personal computer. The efficacy of the technique for microanalysis is illustrated by the analysis of complex multi-phase materials, particulates, a diffusion couple, and a single-pixel-detection problem.

Keywords

spectrum imagespectral imagestatistical analysisprincipal components analysisX-ray microanalysisinformation extractionmultivariate statistical analysis
Type
Instrumentation and Technique
Information
Microscopy and Microanalysis , Volume 9 , Issue 1 , February 2003 , pp. 1 - 17
Copyright
© 2003 Microscopy Society of America

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)