Hostname: page-component-586b7cd67f-2plfb Total loading time: 0 Render date: 2024-11-22T11:38:52.762Z Has data issue: false hasContentIssue false
  • English
  • Français

Efficient Simulation of Secondary Fluorescence Via NIST DTSA-II Monte Carlo

Published online by Cambridge University Press:  13 March 2017

Nicholas W. M. Ritchie
Nicholas W. M. Ritchie*
Affiliation:
National Institute of Standards and Technology, 100 Bureau Drive, Gaithersburg, MD 20899-8372, USA
*
* Corresponding author. [email protected]
Get access

Abstract

Secondary fluorescence, the final term in the familiar matrix correction triumvirate Z·A·F, is the most challenging for Monte Carlo models to simulate. In fact, only two implementations of Monte Carlo models commonly used to simulate electron probe X-ray spectra can calculate secondary fluorescence—PENEPMA and NIST DTSA-IIa (DTSA-II is discussed herein). These two models share many physical models but there are some important differences in the way each implements X-ray emission including secondary fluorescence. PENEPMA is based on PENELOPE, a general purpose software package for simulation of both relativistic and subrelativistic electron/positron interactions with matter. On the other hand, NIST DTSA-II was designed exclusively for simulation of X-ray spectra generated by subrelativistic electrons. NIST DTSA-II uses variance reduction techniques unsuited to general purpose code. These optimizations help NIST DTSA-II to be orders of magnitude more computationally efficient while retaining detector position sensitivity. Simulations execute in minutes rather than hours and can model differences that result from detector position. Both PENEPMA and NIST DTSA-II are capable of handling complex sample geometries and we will demonstrate that both are of similar accuracy when modeling experimental secondary fluorescence data from the literature.

Keywords

Monte CarloX-raysecondary fluorescenceEPMASEM-EDS
Type
Instrumentation and Software
Information
Microscopy and Microanalysis , Volume 23 , Issue 3 , June 2017 , pp. 618 - 633
Copyright
© Microscopy Society of America 2017 

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

Acosta, E., Llovet, X., Coleoni, E., Salvat, F. & Riveros, J.A. (1998). Monte Carlo simulation of X-ray emission by kilovolt electron bombardment. J Appl Phys 83, 6038–6049.Google Scholar
Acosta, E., Llovet, X. & Salvat, F. (2002). Monte Carlo simulation of bremsstrahlung emission by electrons Applied Physics Letters. Amer Inst Phys 80, 3228–3230.Google Scholar
Bastin, G.F., Van Loo, F.J.J., Vosters, P.J.C. & Vrolljk, J.W.G.A. (1983). A correction procedure for characteristic fluorescence encountered in microprobe analysis near phase boundaries. Scanning 5(4), 172–183.CrossRefGoogle 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(4), 042701.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. Technical Report. National Institute of Standards and Technology, Gaithersburg, MD. Available at http://physics.nist.gov/ffast (retrieved May 1, 2007).Google Scholar
Dalton, J.A. & Lane, S.J. (1996). Electron microprobe analysis of Ca in olivine close to grain boundaries: The problem of secondary X-ray fluorescence. Am Mineral 81(1–2), 194–201.Google Scholar
Ericson, C. (2004). Real-Time Collision Detection . San Fransisco: CRC Press.CrossRefGoogle Scholar
Gamma, E., Helm, R., Johnson, R. & Vlissides, J. (1994). Design Patterns: Elements of Reusable Object-Oriented Software. Boston MA: Addison-Wesley Professional.Google Scholar
Heinrich, K.F.J. (1981). Electron Beam X-Ray Microanalysis. New York: Von Nostrand Reinhold Company.Google Scholar
Kissel, L., Quarles, C.A. & Pratt, R.H. (1983). Shape functions for atomic-field bremsstrahlung from electrons of kinetic energy 1–500 keV on selected neutral atoms 1≤Z≤9. Atomic Data Nucl Data Tables 28(3), 381–460.Google Scholar
Knop, R.E. (1970). Algorithm 381: Random vectors uniform in solid angle. Commun ACM 13(5), 326.Google Scholar
Llovet, X., Pinard, P.T., Donovan, J.J. & Salvat, F. (2012). Secondary fluorescence in electron probe microanalysis of material couples. J Phys D Appl Phys 45(22), 225301.Google Scholar
Llovet, X. & Salvat, F. (2016). PENEPMA: A Monte Carlo programme for the simulation of X-ray emission in EPMA. IOP Conf Ser Mater Sci Eng 109(1), 012009.Google Scholar
McMaster, W.H., Kerr Del Grande, N., Mallett, J.H. & Hubbell, J.H. (1969). Compilation of X-ray cross sections. Technical Report. Berkley, CA: Lawrence Radiation Laboratory, University of California.Google Scholar
Perkins, S.T., Cullen, D.E., Chen, M.H., Rathkopf, J., Scofield, J. & Hubbell, J.H. (1991). Tables and graphs of atomic subshell and relaxation data derived from the LLNL Evaluated Atomic Data Library (EADL), Z=1–100. Technical Report. Berkley, CA: Lawrence Livermore National Laboratory.Google Scholar
Pinard, P.T., Demers, H., Salvat, F. & Gauvin, R. (2010). An API/GUI for Monte Carlo simulation of EPMA spectra using PENELOPE. Microsc Microanal 16(S2), 280–281.Google Scholar
Press, W.H., Teukolsky, S.A., Vetterling, W.T. & Flannery, B.P. (1992). Numerical Recipes in C, vol. 2. Cambridge: Cambridge University Press.Google Scholar
Procop, M. & Hodoroaba, V.-D. (2009). A test material and a quick procedure for the performance check of X-ray spectrometers attached to the SEM. Microsc Microanal 15(Suppl 2), 1120–1121.Google Scholar
Ritchie, N.W.M. (2005). A new Monte Carlo application for complex sample geometries. Surf Interface Anal 37(11), 1006–1011.CrossRefGoogle Scholar
Ritchie, N.W.M., Newbury, D.E. & Lindstrom, A.P. (2011). Compton scattering artifacts in electron excited X-ray spectra measured with a silicon drift detector. Microsc Microanal 17(6), 903–910.CrossRefGoogle ScholarPubMed
Salvat, F., Fernandez-Varea, J.M. & Sempau, J. (2015). PENELOPE-2014: A code system for Monte Carlo simulation of electron and photon transport. Technical Report. OECD/NEA Data Bank, Issy-les-Moulineaux, France.Google Scholar
Seltzer, S.M. & Berger, M.J. (1985). Bremsstrahlung spectra from electron interactions with screened atomic nuclei and orbital electrons. Nucl Instrum Methods Phys Res B 12(1), 95–134.CrossRefGoogle Scholar
Seltzer, S.M. & Berger, M.J. (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. Atomic Data Nucl Data 35(3), 345–418.Google Scholar
Valovirta, E., Erlach, S., Llovet, X. & Heikinheimo, E. (2001). EPMA of metal-metal diffusion couples at high temperature. In EMAS 2001–7th European Workshop on Modern Developments and Applications in Microbeam Analysis, Tampere, Finland.Google Scholar
Wark, D.A. & Watson, E B. (2006). TitaniQ: A titanium-in-quartz geothermometer. Contrib Mineral Petrol 152(6), 743–754.Google Scholar