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The Role of Monte Carlo Calculations In Quantitative Analysis

Published online by Cambridge University Press:  02 July 2020

E. Lifshin
Affiliation:
General Electric Corporate Research and Development Center, Schenectady, NY, 12301
L. A. Peluso
Affiliation:
General Electric Corporate Research and Development Center, Schenectady, NY, 12301
R. Gauvin
Affiliation:
Université de Sherbrooke, Quebec, Canada
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Extract

In conventional quantitative electron microprobe analysis, methods like ZAF and φ(ρz), are used to convert x-ray intensity data to chemical composition utilizing equations that describe electron solid interactions, the x-ray generation process, absorption of the generated x-rays and secondary fluorescence effects. These equations capture the overall response of a sample or standard rather than consider the fate of individual electrons over time. For example, f(χ) is simply the ratio of x-rays emitted to the number generated. While for the most part these methods are built on solid physical models, the models were never exact enough to completely match measured data. Consequently, they have been modified by various adjustable parameters to ensure a high level of accuracy for a wide variety of systems and experimental conditions. However, since most of the adjustments were based on data taken at normal incidence, there has always been a question of how to obtain accurate results from tilted samples.

Type
Problem Elements and Spectrometry Problems in X-Ray Microanalysis
Copyright
Copyright © Microscopy Society of America

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References

1.Statham, P. J., X-ray Spectrometry, 5, 154, (1976).CrossRefGoogle Scholar
2.Ding, Q. et al., J. Appl. Phys., Vol. 76, No. 11, 7180 (1994).CrossRefGoogle Scholar
3.Joy, D. C., Monte Carlo Modeling fo r Electron Microscopy and Microanalysis, Oxford (1995).Google Scholar
4.Browning, R. et al., J. Appl. Phys., Vol. 76, No. 4, pp. 20162022, (1994).CrossRefGoogle Scholar
5.Joy, D. C. and Luo, S., Scanning, Vol. 11, pp. 176180, (1989).CrossRefGoogle Scholar
6.Casnati, E. et al., J. Phys., B15, pp. 155167, (1982).Google Scholar
7.Heinrich, K.F.J., The Electron Microprobe, (Eds. McKinley, TD, Heinrich, KFJ, Whittry, DB), Wiley, New York, p. 296, (1966).Google Scholar
8.Bambynek, et al., Rev. Mod. Phys., Vol. 44, No. 4, pp. 716813, (1972).CrossRefGoogle Scholar
9.Schreiber, T. P. and Wims, A. M., Microbeam Analysis (Ed. Heinrich, KFJ), San Francisco Press, San Francisco, pp. 161166, (1982).Google Scholar
10.Lifshin, E. et al., Proc 8th IXCOM, Boston, Pendel Publishers, pp. 141148, (1977).Google Scholar