Hostname: page-component-78c5997874-g7gxr Total loading time: 0 Render date: 2024-11-09T07:52:24.114Z Has data issue: false hasContentIssue false
  • English
  • Français

Spectrum Simulation in DTSA-II

Published online by Cambridge University Press:  16 September 2009

Nicholas W.M. Ritchie
Nicholas W.M. Ritchie*
Affiliation:
National Institute of Standards and Technology, Gaithersburg, MD 20889-8371, USA
*
Corresponding author. E-mail: [email protected]
Get access

Abstract

Spectrum simulation is a useful practical and pedagogical tool. Particularly with complex samples or trace constituents, a simulation can help to understand the limits of the technique and the instrument parameters for the optimal measurement. DTSA-II, software for electron probe microanalysis, provides both easy to use and flexible tools for simulating common and less common sample geometries and materials. Analytical models based on ϕ(ρz) curves provide quick simulations of simple samples. Monte Carlo models based on electron and X-ray transport provide more sophisticated models of arbitrarily complex samples. DTSA-II provides a broad range of simulation tools in a framework with many different interchangeable physical models. In addition, DTSA-II provides tools for visualizing, comparing, manipulating, and quantifying simulated and measured spectra.

Keywords

Monte CarlosimulationEPMAEDSX-rayelectron
Type
Instrumentation and Software Development
Information
Microscopy and Microanalysis , Volume 15 , Issue 5 , October 2009 , pp. 454 - 468
Copyright
Copyright © Microscopy Society of America 2009

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.)

References

REFERENCES

Acosta, E., Llovet, X. & Salvat, F. (2002). Monte Carlo simulation of bremsstrahlung emission by electrons. Appl Phys Lett 80, 3228–3230.Google Scholar
Alvisi, M., Blome, M., Griepentrog, M., Hodoroaba, V.D., Karduck, P., Mostert, M., Nacucchi, M., Procop, M., Rohde, M., Scholze, F., Statham, P., Terborg, R. & Thiot, J.F. (2006). The determination of the efficiency of energy dispersive X-ray spectrometers by a new reference material. Microsc Microanal 12, 406–415.Google Scholar
Bastin, G., Dijkstra, J. & Heijligers, H. (1998). PROZA96: An improved matrix correction program for electron probe microanalysis, based on a double Gaussian approach. X-Ray Spectr 27, 3–10.Google Scholar
Bote, D. & Salvat, F. (2008). Calculations of inner-shell ionization by electron impact with the distorted-wave and plane-wave Born approximations. Phys Rev A 77, 042701.Google Scholar
Casnati, E., Tartari, A. & Baraldi, C. J. (1982). An empirical-approach to K-shell ionization cross-section by electrons. Phys B: At Mol Opt Phys 15, 155–167.Google Scholar
Chantler, C.T., Olsen, K., Dragoset, R.A., Chang, J., Kishore, A.R., Kotochigova, S.A. & Zucker, D.S. (2005). X-Ray Form Factor, Attenuation and Scattering Tables (version 2.1). Gaithersburg, MD: National Institute of Standards and Technology. Available at http://physics.nist.gov/ffast (accessed August 15, 2008). Originally published as Chantler, C.T. (2000). J Phys Chem Ref Data 29(4), 597–1048; and Chantler, C.T. (1995). J Phys Chem Ref Data 24, 71–643.Google Scholar
Cullen, D.E. (1992). Program RELAX: A code designed to calculate X-ray and electron emission spectra as singly charged atoms relax back to neutrality. UCRL-ID-110438, Lawrence Livermore National Laboratory.Google Scholar
Czyzewski, Z., MacCallum, D.O., Romig, A. & Joy, D.C. (1990). Calculations of Mott scattering cross-section. J Appl Phys 68, 3066–3072.CrossRefGoogle Scholar
Edgerton, R., Fiori, C., Hunt, J., Isaacson, M., Kirkland, E. & Zaluzec, N. (1991). EMMFF V. 1.0. Available at http://www.amc.anl.gov/ANLSoftwareLibrary/EMMPDL(old)/Info/emmff.doc.Google Scholar
Fiori, C., Swyt-Thomas, C. & Myklebust, R. (2005). Desktop spectrum analyzer. Available at http://www.cstl.nist.gov/div837/Division/outputs/DTSA/DTSA.htm.Google Scholar
Goldstein, J., Newbury, D., Joy, D., Lyman, C., Echlin, P., Lifshin, E., Sawyer, L. & Michael, J. (2003). Scanning Electron Microscopy and X-Ray Microanalysis—Third Edition. New York: Kluwer Academic/Plenum Publishers.Google Scholar
Heinrich, K.J.F. (1986). Mass absorption coefficients for electron probe microanalysis. Proceedings of the 11th International Congress on X-ray Optics and Microanalysis, University of Western Ontario, pp. 67–119.Google Scholar
Henke, B., Gullikson, E. & Davis, J. (1993). X-ray interactions: Photoabsorption, scattering, transmission, and reflection at E = 50–30000 eV, Z = 1–92. At Nucl Data 54, 181–342.Google Scholar
Jablonski, A., Salvat, F. & Powell, C.J. (2003). NIST Electron Elastic-Scattering Cross-Section Database—Version 3.1. Gaithersburg, MD: National Institute of Standards and Technology.Google Scholar
Joy, D.C. & Luo, S. (1989). An empirical stopping power relationship for low-energy electons. Scanning 11, 176–180.CrossRefGoogle Scholar
Kissel, L., Quarles, C. & Pratt, R. (1983). Shape functions for atomic-field bremsstrahlung from electrons of kinetic energy 1–500 keV on selected neutral atoms 1 ≤ Z ≤ 92. Atom Data Nucl Data 28, 381–460.Google Scholar
Knoll, G. (2000). Radiation Detection and Measurement—Third Edition. New York: John Wiley and Sons.Google Scholar
Myklebust, R., Newbury, D. & Yakowitz, H. (1976). In NBS Special Publication 460, Heinrich, K., Yakowitz, H. & Newbury, D. (Eds.), p. 105. Washington, DC: National Bureau of Standards.Google Scholar
Niculae, A., Soltau, H., Lutz, G., Lechner, P., Bechteler, A., Eckhardt, R., Hermenau, K., Jaritschin, O., Liebel, A., Simsek, A., Jaritschin, O., Liebel, A., Simsek, A., Schaller, G., Schopper, F., Strüder, L., Schaller, G., Schopper, F. & Strüder, L. (2008). Expanding the detector efficiency of silicon drift detectors with optimized radiation entrance window. Proceedings of 57th Annual Conference on Applications of X-Ray Analysis, Denver, Colorado, August 4–8, 2008.Google Scholar
Pouchou, J.-L. & Pichoir, F. (1991). Quantitative analysis of homogeneous or stratified microvolumes applying the model ‘PAP.’ In Electron Probe Quantification, Heinrich, K. & Newbury, D. (Eds.), pp. 31–75. New York: Plenum.Google Scholar
Press, W., Teukolsky, S., Vetterling, W. & Flannery, B. (1992). Numerical Recipes in C—The Art of Scientific Computing—Second Edition. New York: Cambridge University Press.Google Scholar
Ritchie, N.W.M. (2005). A new Monte Carlo application for complex sample geometries. Surf Interface Anal 37, 1006–1011.Google Scholar
Salvat, F., Fernández-Varea, J.M. & Sempau, J. (2006). PENELOPE-2006 computer code. OECD/NEA Data Bank, Issy-les-Moulineaux, France. Available at http://www.nea.fr/lists/penelope.html.Google Scholar
Scott, V., Love, G. & Reed, S. (1995). Quantitative Electron-Probe Microanalysis—Second Edition. New York: Ellis Horwood.Google Scholar
Seltzer, S. & Berger, M. (1986). Bremsstrahlung energy-spectra from electrons with kinetic-energy 1 keV–10 GeV incident on screened nuclei and orbital electrons of neutral atoms with Z = 1–100. Atom Data Nucl Data 35, 345–418.CrossRefGoogle Scholar
Small, J., Newbury, D. & Myklebust, R. (1987). Modeling of the bremsstrahlung radiation produced in pure-element targets by 10–40 keV electrons. J Appl Phys 61(2), 459–469.Google Scholar
Villarrubia, J.S., Ritchie, N.W.M. & Lowney, J.R. (2007). Monte Carlo modeling of secondary electron imaging in three dimensions. Proc SPIE 6518, 65180K1–14.Google Scholar