Hostname: page-component-848d4c4894-m9kch Total loading time: 0 Render date: 2024-05-14T08:18:42.528Z Has data issue: false hasContentIssue false
  • English
  • Français

PENEPMA: A Monte Carlo Program for the Simulation of X-Ray Emission in Electron Probe Microanalysis

Published online by Cambridge University Press:  15 May 2017

Xavier Llovet Open the ORCID record for Xavier Llovet [Opens in a new window]  and
Francesc Salvat
Xavier Llovet*
Affiliation:
Centres Científics i Tecnològics, Universitat de Barcelona, Llus Solé i Sabars 1-3, 08028 Barcelona, Spain
Francesc Salvat
Affiliation:
Facultat de Física (FQA and ICC), Universitat de Barcelona, Diagonal 647, 08028 Barcelona, Spain
*
*Corresponding author. xavier@ccit.ub.edu
Get access

Abstract

The Monte Carlo program PENEPMA performs simulations of X-ray emission from samples bombarded with both electron and photon beams. It is based on the general-purpose Monte Carlo simulation package PENELOPE, an elaborate system for the simulation of coupled electron-photon transport in arbitrary materials, and on the geometry subroutine package PENGEOM, which tracks particles through complex material structures defined by quadric surfaces. After a brief description of the interaction models implemented in the simulation subroutines and of the structure and operation of PENEPMA, we provide an overview of the capabilities of the program along with several examples of its application to the modeling of electron probe microanalysis measurements.

Keywords

Monte Carlo simulationelectron probe microanalysisPENELOPEX-ray spectra
Type
Instrumentation and Software
Information
Microscopy and Microanalysis , Volume 23 , Issue 3 , June 2017 , pp. 634 - 646
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

Almansa, J., Salvat-Pujol, F., Daz-Londoño, G., Carnicer, A., Lallena, A.M. & Salvat, F. (2016). PENGEOM. A general-purpose geometry package for Monte Carlo simulation of radiation transport in complex material structures. Comput Phys Commun 199, 102–113.Google Scholar
Acosta, E., Llovet, X. & Salvat, F. (2002). Monte Carlo simulation of bremsstrahlung emission by electrons. Appl Phys Lett 80, 3228–3330.CrossRefGoogle Scholar
Bastin, G.F. & Heijligers, H.J.M. (2000 a). A systematic database of thin-film measurements by EPMA Part I—Aluminum films. X-Ray Spectrom 29, 212–238.Google Scholar
Bastin, G.F. & Heijligers, H.J.M. (2000 b). A systematic database of thin-film measurements by EPMA Part II—Paladium films. X-Ray Spectrom 29, 373–397.3.0.CO;2-S>CrossRefGoogle Scholar
Bastin, G.F., van Loo, F.J., Vosters, P.J. & Vroljik, J.W. (1983). A correction procedure for characteristic f1uorescence encountered in microprobe analysis near phase boundaries. Scanning 5, 172–183.Google Scholar
Bearden, J.A. (1967). X-ray wavelengths. Rev Mod Phys 39, 78–124.Google Scholar
Berger, M.J. & Hubbell, J.H. (1987). XCOM: Photon cross sections on a personal computer. Report NBSIR 87-3597, National Bureau of Standards, Gaithersburg, MD, USA.CrossRefGoogle Scholar
Bote, D., Llovet, X. & Salvat, F. (2008). Monte Carlo simulation of characteristic x-ray emission from thick samples bombarded by kiloelectronvolt electrons. J Phys D Appl Phys 14, 105304.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
Brusa, D., Stutz, G., Riveros, J.A., Fernández-Varea, J.M. & Salvat, F. (1996). Fast sampling algorithm for the simulation of photon Compton scattering. Nucl Instrum Meth A 379, 167–175.Google Scholar
Cazaux, J (1989). Minimum detectable dimension, resolving power and quantification of scanning auger microscopy at high lateral resolution. Surf Interface Anal 14, 354–366.CrossRefGoogle Scholar
Cullen, D.E., Chen, M.H, Hubbell, J.H., Perkins, P.T., Plechaty, E.F., Rathkopf, J.A. & Scofield, J.A. (1989). Tables and graphs of photon-interaction cross sections from 10 eV to 100 GeV derived from the LLNL evaluated photon data library (EPDL). Report UCRL-50400, vol. 6, rev. 4, parts A and B, Lawrence Livermore National Laboratory, Livermore, CA.Google Scholar
Deslattes, R.D., Kessler, E.G., Indelicato, P., de Billy, L., Lindroth, E. & Anton, J. (2003). X-ray transition energies: new approach to a comprehensive evaluation. Rev Mod Phys 75, 36–99.Google Scholar
Fernández, J.E., Scot, V., Di Giulio, E. & Salvat, F. (2015). Evaluation of bremsstrahlung contribution to photon transport in coupled photon-electron problems. Radiat Phys Chem 116, 203–207.CrossRefGoogle Scholar
Fournelle, J.H., Kim, S. & Perepezko, J.H. (2005). Monte Carlo simulation of Nb Ka secondary fluorescence in EPMA: comparison of PENELOPE simulations with experimental results. Surf Interface Anal 37, 1012–1016.Google Scholar
Gauvin, R., Lifshin, E., Demers, H., Horny, P. & Campbell, H. (2006). Win X-ray: A new Monte Carlo program that computes X-ray spectra obtained with a scanning electron microscope. Microsc Microanal 12, 49–64.Google Scholar
Hovington, P., Drouin, D. & Gauvin, R. (1997). CASINO: A new Monte Carlo code in C language for electron beam interaction part I: description of the program. Scanning 19, 1–14.Google Scholar
Hovington, P., Timoshevskii, V., Burgess, S., Demers, H., Statham, P., Gauvin, R. & Zaghib, K. (2016). Can we detect Li K X-ray in lithium compounds using energy dispersive spectroscopy? Scanning 38, 574–578.CrossRefGoogle 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≤92. At Data Nucl Data Tables 28, 381–460.Google Scholar
Li, X.L., Zhao, J., Tian, L.X., An, Z., Zhu, J.J. & Liu, M.T. (2014). Measurements of inner-shell characteristic X-ray yields of thick W, Mo and Zr targets by low-energy electron impact and comparison with Monte Carlo simulations. Nucl Instrum Method B 333, 106–112.Google Scholar
Liljequist, D. (1983). A simple calculation of inelastic mean free path and stopping power for 50 eV–50 keV electrons in solids. J Phys D Appl Phys 16, 1567–1582.Google Scholar
Lin, C., Mao, L., Huang, N. & An, Z. (2012). Simulation study of quantitative X-ray fluorescence analysis of ore slurry using partial least-square regression. Plasma Sci Tech 14, 427–430.Google Scholar
Llovet, X., Fernández-Varea, J.M., Sempau, J. & Salvat, F. (2005). Monte Carlo simulation of X-ray emission using the general-purpose code PENELOPE. Surf Interface Anal 37, 1054–1058.Google Scholar
Llovet, X. & Galán, G. (2003). Correction of secondary X-ray fluorescence near grain boundaries in electron microprobe analysis: Application to thermobarometry of spinel lherzolites. Am Mineral 88, 121–130.Google Scholar
Llovet, X. & Merlet, C. (2010). Electron probe microanalysis of thin films and multilayers using the computer program XFILM. Microsc Microanal 16, 21–32.Google Scholar
Llovet, X., Sorbier, L., Campos, C.S., Acosta, E. & Salvat, F. (2003). Monte Carlo simulation of X-ray spectra generated by kilo-electron-volt electrons. J Appl Phys 93, 3844–3851.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, 225301.Google Scholar
Llovet, X., Powell, C.J., Jablonski, A. & Salvat, F. (2014). Cross sections for inner-shell ionization by electron impact. J Phys Chem Ref Data 43, 013102.Google Scholar
Llovet, X. & Salvat, F. (2016). PENEPMA: a Monte Carlo programme for the simulation of X-ray spectra in EPMA. IOP Conf Ser Mater Sci Eng 109, 012009.Google Scholar
Merlet, C. & Llovet, X. (2006). Absolute determination of characteristic X-ray yields with a wavelength-dispersive spectrometer. Microchim Acta 155, 199–204.Google Scholar
Miller, T.M., Patton, B.W. & Weber, C.F. (2014). Simulation of electron probe microanalysis for the purposes of automated material identification initial evaluation of available Monte Carlo tools. Trans Am Nucl Soc 110, 497–450.Google Scholar
Moy, A., Merlet, C. & Dugne, O. (2015). Standardless quantification of heavy elements by electron probe microanalysis. Anal Chem 87, 7779–7788.Google Scholar
Naito, M., Hasebe, N., Kusano, H., Nagaoka, H., Kuwako, M., Oyama, Y., Shibamura, E., Amano, Y., Ohta, T., Kim, K.J. & Lopes, J.A.M. (2015). Future lunar mission Active X-ray Spectrometer development: Surface roughness and geometry studies. Nucl Instrum Meth A 788, 182–187.CrossRefGoogle Scholar
Perkins, S.T., Cullen, D.E., Chen, M.H., Hubbell, J.H., Rathkopf, J. & Scofield, J. (1991). Tables and graphs of atomic subshell and relaxation data derived from the LLNL evaluated atomic data library (EADL), Z=1–100. Report UCRL-50400, vol. 30, Lawrence Livermore National Laboratory, Livermore, CA.Google Scholar
Petaccia, M., Segui, S. & Castellano, G. (2015). Monte Carlo Simulation of characteristic secondary fluorescence in electron probe microanalysis of homogeneous samples using the splitting technique. Microsc Microanal 21, 753–758.CrossRefGoogle ScholarPubMed
Pistorius, P.C. & Verma, N. (2011). Matrix effects in the energy dispersive X-ray analysis of CaO-Al2O3-MgO inclusions in steel. Microsc Microanal 17, 963–971.Google Scholar
Poskus, A. (2016). Evaluation of computational models and cross sections used by MCNP6 for simulation of characteristic X-ray emission from thick targets bombarded by kiloelectronvolt electrons. Nucl Instrum Meth B 383, 65–80.Google Scholar
Procop, M. (2004). Measurement of X-ray emission efficiency for K-lines. Microsc Microanal 10, 481–490.Google Scholar
Rinaldi, R. & Llovet, X. (2015). Electron probe microanalysis: A review of the past, present, and future. Microsc Microanal 21, 1053–1069.Google Scholar
Ritchie, N.W.M. (2005). A new Monte Carlo application for complex sample geometries. Surf Interface Anal 37, 1006–1011.Google Scholar
Roet, D., Ceballos, C. & Van Espen, P. (2006). Comparison between MCNP and PENELOPE for the simulation of X-ray spectra in electron microscopy in the keV range. Nucl Instrum Meth B 251, 317–325.Google Scholar
Sabbatucci, L. & Salvat, F. (2016). Theory and calculation of the atomic photoeffect. Radiat Phys Chem 121, 122–140.Google Scholar
Salvat, F. (2015). PENELOPE-2014: A code system for Monte Carlo simulation of electron and photon transport. OECD/NEA Data Bank, Issy-les-Moulineaux. Available at https://www.oecd-nea.org/dbprog/courses/nsc-doc2015-3.pdf (retrieved April 27, 2017).Google Scholar
Salvat, F., Jablonski, A. & Powell, C.J. (2005). ELSEPA—Dirac partial-wave calculation of elastic scattering of electrons and positrons by atoms, positive ions and molecules. Comput Phys Commun 165, 157–190.Google Scholar
Salvat, F., Llovet, X., Fernández–Varea, J.M. & Sempau, J. (2006). Monte Carlo simulation in electron probe microanalysis. Comparison of different simulation algorithms. Microchim Acta 155, 67–74.Google Scholar
Seltzer, S.M. & Berger, M.J. (1985). Bremsstrahlung spectra from electron interactions with screened atomic nuclei and orbital electrons. Nucl Instrum Meth B 12, 95–134.Google Scholar
Shima, K., Okuda, M., Suzuki, T., Tsubota, T. & Mikumo, T. (1983). Lα X-ray production efficiency from Z=50-82 thick target elements by electron impacts from thresholds energy to 30 keV. J Appl Phys 54, 1202–1208.Google Scholar
Sternheimer, R.M. (1952). The density effect for the ionization loss in various materials. Phys Rev 88, 851–859.Google Scholar
Statham, P., Llovet, X. & Duncumb, P. (2012). Systematic discrepancies in Monte Carlo predictions of k-ratios emitted from thin films on substrates. IOP Conf Ser Mater Sci Eng 32, 012024.Google Scholar
Tian, L., Zhu, J., Liu, M. & An, Z. (2009). Bremsstrahlung spectra produced by kilovolt electron impact on thick targets. Nucl Instrum Method B 267, 3495–3499.Google Scholar
Wade, J. & Wood, B.J. (2012). Metal–silicate partitioning experiments in the diamond anvil cell: A comment on potential analytical errors. Phys Earth Plan Inter 192–193, 54–58.Google Scholar
Weber, C.F., Bekar, K.B., Patton, B.W. & Miller, T.M. (2014). Simulation of SEM-EDSB spectra: Monte Carlo code development and inverse analysis. Report ORNL/LTR-2014/557, Oak Ridge National Laboratory, Oak Ridge, Tennessee.Google Scholar
Zhao, J.L., Tian, L.X., Li, X.L., An, Z., Zhu, J.J. & Liu, M. T. (2015). Measurements of L shell X-ray yields of thick Ag target by 629 keV electron impact. Radiat Phys Chem 107, 47–53.Google Scholar