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Secondary Fluorescence Correction for Characteristic and Bremsstrahlung X-Rays Using Monte Carlo X-ray Depth Distributions Applied to Bulk and Multilayer Materials

Published online by Cambridge University Press:  14 March 2019

Yu Yuan Open the ORCID record for Yu Yuan [Opens in a new window] ,
Hendrix Demers ,
Samantha Rudinsky  and
Raynald Gauvin
Yu Yuan
Affiliation:
Department of Mining and Materials Engineering, McGill University, 3610 Rue University, Montreal, Quebec, H3A 0C5, Canada
Hendrix Demers
Affiliation:
Centre d'excellence en électrification des transports et stockage d’énergie, IREQ, 1806 Boulevard Lionel-Boulet, Varennes, Québec, J3X 1S1, Canada
Samantha Rudinsky
Affiliation:
Department of Mining and Materials Engineering, McGill University, 3610 Rue University, Montreal, Quebec, H3A 0C5, Canada
Raynald Gauvin*
Affiliation:
Department of Mining and Materials Engineering, McGill University, 3610 Rue University, Montreal, Quebec, H3A 0C5, Canada
*
*Author for correspondence: Raynald Gauvin, E-mail: [email protected]
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Abstract

Secondary fluorescence effects are important sources of characteristic X-ray emissions, especially for materials with complicated geometries. Currently, three approaches are used to calculate fluorescence X-ray intensities. One is using Monte Carlo simulations, which are accurate but have drawbacks such as long computation times. The second one is to use analytical models, which are computationally efficient, but limited to specific geometries. The last approach is a hybrid model, which combines Monte Carlo simulations and analytical calculations. In this article, a program is developed by combining Monte Carlo simulations for X-ray depth distributions and an analytical model to calculate the secondary fluorescence. The X-ray depth distribution curves of both the characteristic and bremsstrahlung X-rays obtained from Monte Carlo program MC X-ray allow us to quickly calculate the total fluorescence X-ray intensities. The fluorescence correction program can be applied to both bulk and multilayer materials. Examples for both cases are shown. Simulated results of our program are compared with both experimental data from the literature and simulation data from PENEPMA and DTSA-II. The practical application of the hybrid model is presented by comparing with the complete Monte Carlo program.

Keywords

Monte Carlomultilayer materialsquantitative X-ray microanalysissecondary fluorescenceX-ray depth distribution
Type
Materials Science Applications
Information
Microscopy and Microanalysis , Volume 25 , Issue 1 , February 2019 , pp. 92 - 104
Copyright
Copyright © Microscopy Society of America 2019 

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References

Armigliato, A, Desalvo, A & Rosa, R (1982). A Monte Carlo code including an X-ray characteristic fluorescence correction for electron probe microanalysis of a thin film on a substrate. J Phys D: Appl Phys 15(10), L121.Google Scholar
Armstrong, JT & Buseck, PR (1985). A general characteristic fluorescence correction for the quantitative electron microbeam analysis of thick specimens, thin films and particles. X-Ray Spectrom 14(4), 172–182.Google Scholar
Bastin, G & Heijligers, H (2000). A systematic database of thin-film measurements by EPMA part II—palladium films. X-Ray Spectrom 29(5), 373–397.Google Scholar
Benhayoune, H (1996). Characteristic and continuous fluorescence correction for electron probe microanalysis of thin coatings at oblique incidence. J Anal At Spectrom 11(11), 1113–1117.Google Scholar
Chantler, CT (1995). Theoretical form factor, attenuation, and scattering tabulation for Z = 1–92 from E = 1–10 eV to E = 0.4–1.0 MeV. J Phys Chem Ref Data 24(1), 71–643.Google Scholar
Chantler, CT (2000). Detailed tabulation of atomic form factors, photoelectric absorption and scattering cross section, and mass attenuation coefficients in the vicinity of absorption edges in the soft X-ray (Z = 30–36, Z = 60–89, E = 0.1–10 keV), addressing convergence issues of earlier work. J Phys Chem Ref Data 29(4), 597–1056.Google Scholar
Cox, M, Love, G & Scott, V (1979). A characteristic fluorescence correction for electron-probe microanalysis of thin coatings. J Phys D: Appl Phys 12(9), 1441.Google Scholar
Demers, H (2008). Two facets of the x-ray microanalysis at low voltage: the secondary fluorescence x-rays emission and the microcalorimeter energy-dispersive spectrometer. McGill University.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(1), 49–64.Google Scholar
Gauvin, R & Michaud, P (2009). MC X-Ray, a new Monte Carlo program for quantitative X-ray microanalysis of real materials. Microsc Microanal 15(S2), 488–489.Google Scholar
Geller, M & Ng, EW (1969). A table of integrals of the exponential integral. J Res Natl Bur Stand (1934) 71, 120.Google Scholar
Goldstein, J, Newbury, DE, Echlin, P, Joy, DC, Romig, AD Jr., Lyman, CE, Fiori, C & Lifshin, E (1992). Scanning Electron Microscopy and X-ray Microanalysis: A Text for Biologists, Materials Scientists, and Geologists. New York: Plenum Press.Google Scholar
Goldstein, J, Newbury, DE, Joy, DC, Lyman, CE, Echlin, P, Lifshin, E, Sawyer, L & Michael, J (2003). Scanning Electron Microscopy and X-ray Microanalysis. Springer Science & Business Media, New York.Google Scholar
Henoc, J (1968). Quantitative electron probe microanalysis. NBS Special Publication 298.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), 1–14.Google Scholar
Kirkpatrick, P & Wiedmann, L (1945). Theoretical continuous X-ray energy and polarization. Phys Rev 67(11–12), 321.Google Scholar
Llovet, X, Pinard, PT, Donovan, JJ & 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 (2006). PENEPMA, A Monte Carlo code for the simulation of X-ray emission spectra using PENELOPE. Workshop Manual, Madison, Wisconsin.Google Scholar
Llovet, X & Salvat, F (2016). PENEPMA: a Monte Carlo programme for the simulation of X-ray emission in EPMA. IOP Conference Series: Materials Science and Engineering, IOP Publishing.Google Scholar
Maddock, J & Cleary, S (2000). C++ type traits. Dr Dobb's J 25(10), 38.Google Scholar
Michaud, P, Gauvin, R, Demers, H, Brodusch, N & Guinel, M (2012). Simulated X-ray spectra and X-ray maps: Evaluation of models used in MC X-ray Monte Carlo simulation program and comparison with experimental data. Microsc Microanal 18(S2), 1022.Google Scholar
Packwood, R & Brown, J (1981). A Gaussian expression to describe ϕ (ρz) curves for quantitative electron probe microanalysis. X-Ray Spectrom 10(3), 138–146.Google Scholar
Pfeiffer, A, Schiebl, C & Wernisch, J (1996). Continuous fluorescence correction in electron probe microanalysis applying an electron scattering model. X-Ray Spectrom 25(3), 131–137.Google Scholar
Pouchou, J & Pichoir, F (1990). Surface film X-ray microanalysis. Scanning 12(4), 212–224.Google Scholar
Pouchou, J-L & Pichoir, F (1991). Quantitative analysis of homogeneous or stratified microvolumes applying the model “PAP”. Electron Probe Quantitation, Springer, Plenum Press, New York. pp. 31–75.Google Scholar
Ritchie, NW (2009). Spectrum simulation in DTSA-II. Microsc Microanal 15(5), 454–468.Google Scholar
Ritchie, NW (2017). Efficient simulation of secondary fluorescence Via NIST DTSA-II Monte Carlo. Microsc Microanal 23(3), 618–633.Google Scholar
Salvat, F, Fernández-Varea, JM & Sempau, J (2009). PENELOPE-2008: A code system for Monte Carlo simulation of electron and photon transport. the Workshop Proceedings, June.Google Scholar
Schreiber, T & Wims, A (1982). Relative intensity factors for K, L and M shell x-ray lines. X-Ray Spectrom 11(2), 42–45.Google Scholar
Seltzer, SM & Berger, MJ (1985). Bremsstrahlung spectra from electron interactions with screened atomic nuclei and orbital electrons. Nucl Instrum Methods Phys Res Sect B 12(1), 95–134.Google Scholar
Springer, G & Rosner, B (1969). The Magnitude of the “Continuous” Fluorescence Correction in Electronprobe Analysis. Vth International Congress on X-Ray Optics and Microanalysis/V. Internationaler Kongreß für Röntgenoptik und Mikroanalyse/Ve Congrès International sur l'Optique des Rayons X et la Microanalyse, Springer.Google Scholar
Waldo, R (1991). A characteristic x-ray fluorescence correction for thin-film analysis by electron microprobe. Microbeam Anal 45, 45–53.Google Scholar
Youhua, H, Yuencai, H & Jiaguang, C (1988). The calculation equations of characteristic fluorescence for multi-layer films. J Phys D: Appl Phys 21(7), 1221.Google Scholar
Yuan, Y, Demers, H, Brodusch, N & Gauvin, R (2016). X-ray emission from thin films on a substrate-experiments and simulation. Microsc Microanal 22(S3), 400–401.Google Scholar