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Win X-ray: A New Monte Carlo Program that Computes X-ray Spectra Obtained with a Scanning Electron Microscope

Published online by Cambridge University Press:  09 December 2005

Raynald Gauvin ,
Eric Lifshin ,
Hendrix Demers ,
Paula Horny  and
Helen Campbell
Raynald Gauvin
Affiliation:
Department of Metals and Materials Engineering, McGill University, Montréal, Québec H3A 2B2, Canada
Eric Lifshin
Affiliation:
College of Nanoscale Science and Engineering, University at Albany, CESTM, 251 Fuller Road, Albany, NY 12203, USA
Hendrix Demers
Affiliation:
Department of Metals and Materials Engineering, McGill University, Montréal, Québec H3A 2B2, Canada
Paula Horny
Affiliation:
Department of Metals and Materials Engineering, McGill University, Montréal, Québec H3A 2B2, Canada
Helen Campbell
Affiliation:
Department of Metals and Materials Engineering, McGill University, Montréal, Québec H3A 2B2, Canada
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Abstract

A new Monte Carlo program, Win X-ray, is presented that predicts X-ray spectra measured with an energy dispersive spectrometer (EDS) attached to a scanning electron microscope (SEM) operating between 10 and 40 keV. All the underlying equations of the Monte Carlo simulation model are included. By simulating X-ray spectra, it is possible to establish the optimum conditions to perform a specific analysis as well as establish detection limits or explore possible peak overlaps. Examples of simulations are also presented to demonstrate the utility of this new program. Although this article concentrates on the simulation of spectra obtained from what are considered conventional thick samples routinely explored by conventional microanalysis techniques, its real power will be in future refinements to address the analysis of sample classifications that include rough surfaces, fine structures, thin films, and inclined surfaces because many of these can be best characterized by Monte Carlo methods. The first step, however, is to develop, refine, and validate a viable Monte Carlo program for simulating spectra from conventional samples.

Keywords

X-ray microanalysisMonte Carloscanning electron microscopyenergy dispersive spectrometryelectron scattering
Type
MICROANALYSIS
Information
Microscopy and Microanalysis , Volume 12 , Issue 1 , February 2006 , pp. 49 - 64
Copyright
2006 Microscopy Society of America

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References

REFERENCES

Bastin, G.F., Van Loo, F.J.J., & Heijligers, H.J.M. (1986). Evaluation of the use of Gaussian φ(ρz) curves in quantitative electron probe microanalysis: A new optimization. X-Ray Spectrom 13, 91–97.Google Scholar
Bishop, H.E. (1965). Calculation of electron penetration and X-ray production in a solid target. In Proceedings of the 4th ICXOM Paris, Castaing, R., Deschamps, P. & Philibert, J. (Eds.), pp. 112–117.
Casnati, E., Tartari, A., & Baraldi, C. (1982). An empirical approach to K-shell ionization cross section by electron. J Phys B 15, 155–167.Google Scholar
Czyzewski, Z., MacCallum, D.O., Romig, A.D., & Joy, D.C. (1990). Calculations of Mott scattering cross sections. J Appl Phys 68, 3066–3072.Google Scholar
Ding, Z.-J., Shimizu, R., & Obori, K. (1994). Monte Carlo simulation of X-ray spectra in electron probe microanalysis: Comparison of continuum with experiment. J Appl Phys 76, 7180–7187.Google Scholar
Drouin, D., Hovington, P., & Gauvin, R. (1997). Casino: A new era of Monte Carlo code in C language for electron beam interaction, Part II: Tabulated values of Mott cross sections. Scanning 19, 20–28.Google Scholar
Duncumb, P., Barkshire, I.R., & Statham, P.J. (2001). Improved X-ray spectrum simulation for electron probe microanalysis. Microsc Microanal 7, 341–355.Google Scholar
Fiori, C.E. & Swyt, C.R. (1989). The use of theoretically generated spectra to estimate detectability limits and concentration variance in energy–dispersive X-ray microanalysis. In Microbeam Analysis, Russell, P.E. (Ed.), pp. 236–238. San Fransisco: San Francisco Press.
Gauvin, R. (1993). A parameterization of K-shell ionization cross-section by electron with an empirical modified Bethe's formula. Microbeam Anal 2, 253–258.Google Scholar
Gauvin, R., Hovington, P., & Drouin, D. (1995). Quantification of spherical inclusions in the scanning electron microscope using Monte Carlo simulations. Scanning 17, 202–219.Google Scholar
Gauvin, R. & L'Espérance, G. (1992). A Monte Carlo code to simulate the effect of fast secondary electrons on K factors and spatial resolution in the TEM. J Microsc 168(pt. 2), 153–167.Google Scholar
Goldstein, J.I., Newbury, D.E., Echlin, P., Joy, D.C., Romig, A.D., Lyman, C.E., Fiori, C.E., & Lifshin, E. (1992). Scanning Electron Microscopy and X-ray Microanalysis. New York: Plenum Press.
Gryzinski, M. (1965). Classical theory of atomic collision I: The theory of inelastic collisions. Phys Rev A 138, 336–358.Google Scholar
Heinrich, K.F.J. (1966). X-ray absorption uncertainty. In The Electron Microprobe, McKinley, T.D., Heinrich, K.F.J. & Whittry, D.B. (Eds.), pp. 296–340. New York: Wiley.
Henke, B.L., Gullikwa, E.M., & Davies, J.C. (1993). X-ray interactions: Photoabsorption, scattering, transmission and reflection at E = 50–30,000 eV, Z = 1–92, Atom Data Nucl Tables 54, 181–342.Google Scholar
Henoc, J. & Maurice, F. (1976). Characteristics of a Monte Carlo program for microanalysis study of energy loss. In Use of Monte Carlo Calculations in Electron Probe Microanalysis and Scanning Electron Microscopy, Heinrich, K.J., Newbury, D.E. & Yakowitz, H. (Eds.), pp. 61–95. Washington, DC: National Bureau of Standards.
Hovington, P., Drouin, D., & Gauvin, R. (1997). Casino: A new era of Monte Carlo code in C language for electron beam interaction, Part I: Description of the program. Scanning 19, 1–14.Google Scholar
Joy, D.C. (1995). Monte Carlo Modeling for Electron Microscopy and Microanalysis. New York, London: Oxford University Press.
Joy, D.C. (1998). The efficiency of X-ray production at low energies. J Microsc 191(pt. 1), 74–82.Google Scholar
Joy, D.C. & Luo, S. (1989). An empirical stopping power relationship for low-energy electrons. Scanning 11, 176–180.Google Scholar
Joy, D.C., Luo, S., Gauvin, R., Hovington, P., & Evans, N. (1995). Experimental measurements on electron stopping power. Scan Microsc 16, 209–220.Google Scholar
Karduck, P. & Rehbach, W. (1991). The use of tracer experiments and Monte Carlo calculations in the φ(ρz) determination for electron probe microanalysis. In Electron Probe Quantitation, Heinrich, K.F.J. & Newbury, D.E. (Ed.), pp. 191–217. New York: Plenum Press.
Kirkpatrick, P. & Wiedmann, L. (1945). Theoretical continuous X-ray energy and polarization. Phys Rev 67, 321–339.Google Scholar
Kyser, D. (1979). Monte Carlo simulation in analytical electron microscopy. In Introduction to Analytical Electron Microscopy, Hren, J.J., Goldstein, J.I. & Joy, D.C. (Eds.), pp. 270–284. New York: Plenum Press.
Lifshin, E., Doganaksov, N., Sirois, J., & Gauvin, R. (1998). Statistical consideration in EDS. Microsc Microanal 4, 598–604.Google Scholar
Llovet, X., Salvat, F., & Fernandez-Varea, J.M. (2004). Monte Carlo simulation of electron transport and X-ray generation. II. Radiative processes and examples in electron probe microanalysis. Microchim Acta 145, 111–120.Google Scholar
Newbury, D.E. & Myklebust, R.L. (1981). A Monte Carlo Electron Trajectory Simulation for Analytical Electron Microscopy, Geiss, R.H. (Ed.), pp. 91–98. San Francisco: San Francisco Press Inc.
Press, W.H., Flanery, B.P., Teckolsky, S.A., & Vetterling, W.T. (1992). Numerical Recipes. Cambridge: Cambridge University Press.
Reimer, L. (1985). Scanning Electron Microscopy; Physics of Image Formation and Microanalysis. Berlin: Springer-Verlag.
Scholze, F. & Procop, M. (2002). Measurement of detection efficiency and response functions for a Si (Li) X-ray spectrometer in the range 0.1–5 keV. X-Ray Spectrom 30, 69–76.Google Scholar
Schreiber, T.P. & Wims, A.M. (1982). Relative intensities of K and L shell X-ray lines. In Microbeam Analysis, Heinrich, K.F.J. (Ed.), pp. 161–166. San Francisco: San Francisco Press Inc.
Thinh, T.P. & Leroux, J. (1979). New basic empirical expression for computing tables of X-ray mass attenuation coefficients. X-Ray Spectrom 8, 85–91.Google Scholar
Tsoulfanidis, N. (1995). Measurement and Detection of Radiation. New York: Hemisphere Publishing Corporation.
Vignes, A. & Dez, G. (1968). Distribution in depth of the primary X-ray emission in anticathodes of titanium and lead. Brit J Appl Phys (J Phys D) 2, 1309–1322.Google Scholar