Hostname: page-component-78c5997874-t5tsf Total loading time: 0 Render date: 2024-11-19T04:17:39.120Z Has data issue: false hasContentIssue false
  • English
  • Français

Use of Monte Carlo Modeling to Aid Interpretation and Quantification of the Low Energy-Loss Electron Yield at Low Primary Energies

Published online by Cambridge University Press:  16 September 2008

Christopher Bonet ,
Andrew Pratt ,
Mohamed M. El-Gomati ,
Jim A.D. Matthew  and
Steven P. Tear
Christopher Bonet
Affiliation:
Department of Physics, University of York, Heslington, York YO10 5DD, UK
Andrew Pratt
Affiliation:
Department of Physics, University of York, Heslington, York YO10 5DD, UK
Mohamed M. El-Gomati
Affiliation:
Department of Electronics, University of York, Heslington, York YO10 5DD, UK
Jim A.D. Matthew
Affiliation:
Department of Physics, University of York, Heslington, York YO10 5DD, UK
Steven P. Tear*
Affiliation:
Department of Physics, University of York, Heslington, York YO10 5DD, UK
*
Corresponding author. E-mail: [email protected]
Get access

Abstract

Experimental low-loss electron (LLE) yields were measured as a function of loss energy for a range of elemental standards using a high-vacuum scanning electron microscope operating at 5 keV primary beam energy with losses from 0 to 1 keV. The resulting LLE yield curves were compared with Monte Carlo simulations of the LLE yield in the particular beam/sample/detector geometry employed in the experiment to investigate the possibility of modeling the LLE yield for a series of elements. Monte Carlo simulations were performed using both the Joy and Luo [Joy, D.C. & Luo, S., Scanning11(4), 176–180 (1989)] expression for the electron stopping power and recent tabulated values of Tanuma et al. [Tanuma, S. et al., Surf Interf Anal37(11), 978–988 (2005)] to assess the influence of the more recent stopping power data on the simulation results. Further simulations have been conducted to explore the influence of sample/detector geometry on the LLE signal in the case of layered samples consisting of a thin C overlayer on an elemental substrate. Experimental LLE data were collected from a range of elemental samples coated with a thin C overlayer, and comparisons with Monte Carlo simulations were used to establish the overlayer thickness.

Keywords

electron microscopelow-loss electronbackscattered electronMonte CarlomodelingBSEscanning electron microscopeSEM
Type
Materials Applications
Information
Microscopy and Microanalysis , Volume 14 , Issue 5 , October 2008 , pp. 439 - 450
Copyright
Copyright © Microscopy Society of America 2008

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

Aristov, V.V., Rau, E.I. & Yakimov, E.B. (1995). “Apparatus” electron beam microtomography in SEM. Phys Stat Sol A 150(1), 211–219.CrossRefGoogle Scholar
Assa'd, A.M.D. & Gomati, M.M.E. (1998). Backscattering coefficients for low energy electrons. Scanning Microsc 12(1), 185–192.Google Scholar
Barkshire, I.R., Roberts, R.H. & Prutton, M. (1997). The application of a low energy loss electron detector in conjunction with scanning Auger microscopy: An aid to quantitative surface microscopy. Appl Surf Sci 120(1–2), 129–138.CrossRefGoogle Scholar
Baró, J., Sempau, J., Fernández-Varea, J.M. & Salvat, F. (1995). PENELOPE: An algorithm for Monte Carlo simulation of the penetration and energy loss of electrons and positrons in matter. Nucl Instrum Meth Phys Res B 100(1), 31–46.CrossRefGoogle Scholar
Berger, D., Filippov, M., Niedrig, H., Rau, E.I. & Schlichting, F. (1999). Experimental determination of energy resolution and transmission characteristics of an electrostatic toroidal spectrometer adapted to a standard scanning electron microscope. J Electron Spectrosc Relat Phenom 105(2–3), 119–127.CrossRefGoogle Scholar
Dapor, M. (2006). Monte Carlo simulation of low-medium energy electrons backscattered from C/Al double layer thin films. Surf Interf Anal 38(8), 1198–1203.CrossRefGoogle Scholar
El-Gomati, M.M. & Assa'd, A.M.D. (1998). On the measurement of the backscattering coefficient for low energy electrons. Mikrochim Acta 15 (yes), 325–331.Google Scholar
Goldstein, J.I., Newbury, D.E., Joy, D.C., Lyman, C.E., Echlin, P., Lifshin, E., Sawyer, L.C. & Michael, J.R. (2002). Scanning Electron Microscopy and X-Ray Microanalysis, 3rded.Boston: Kluwer Academic/Plenum Publishers.Google Scholar
Hunger, H.J. & Kuchler, L. (1979). Measurements of the electron backscatter coefficient for quantitative EPMA in the energy range of 4 to 40 keV. Phys Stat Sol A 56(1), K45–K48.CrossRefGoogle Scholar
Jablonski, A., Salvat, F. & Powell, C.J. (2002). NIST Electron Elastic-Scattering Cross-Section Database—Version 3.1. Gaithersburg, MD: National Institute of Standards and Technology.Google Scholar
Jablonski, A., Tanuma, S. & Powell, C.J. (2006). New universal expression for the electron stopping power for energies between 200 eV and 30 keV. Surf Interf Anal 38(2), 76–83.CrossRefGoogle Scholar
Joy, D.C. (2001). Database of electron-solid interactions. Available at http://web.utk.edu/~srcutk/htm/interact.htm.Google Scholar
Joy, D.C. & Luo, S. (1989). An empirical stopping power relationship for low-energy electrons. Scanning 11(4), 176–180.CrossRefGoogle Scholar
Mackenzie, A.P. (1993). Recent progress in electron probe microanalysis. Rep Prog Phys 56, 577–604.CrossRefGoogle Scholar
Murata, K. & Sugiyama, K. (1989). Quantitative electron microprobe analysis of ultrathin gold films on substrates. J Appl Phys 66(9), 4456–4461.CrossRefGoogle Scholar
Niedrig, H. & Rau, E.I. (1998). Information depth and spatial resolution in BSE microtomography in SEM. Nucl Instrum Meth Phys Res B 142(4), 523–534.CrossRefGoogle Scholar
Penn, D.R. (1987). Electron mean-free-path calculations using a model dielectric function. Phys Rev B 35(2), 482–486.CrossRefGoogle ScholarPubMed
Postek, M.T., Vladár, A.E., Wells, O.C. & Lowney, J.L. (2001). Application of the low-loss scanning electron microscope image to integrated circuit technology: Part I—Applications to accurate dimension measurements. Scanning 23(5), 298–304.CrossRefGoogle Scholar
Pouchou, J.-L. (1993). X-ray microanalysis of stratified specimens. Analytica Chimica Acta 283(1), 81–97.CrossRefGoogle Scholar
Pratt, A., Matthew, J.A.D., El-Gomati, M. & Tear, S.P. (2007). Quantitative interpretation of the low-loss electron signal. Surf Sci 601(8), 1804–1812.CrossRefGoogle Scholar
Rau, E., Hoffmeister, H., Sennov, R. & Kohl, H. (2002). Comparison of experimental and Monte Carlo simulated BSE spectra of multilayered structures and “in-depth” measurements in a SEM. J Phys D: Appl Phys 35(12), 1433–1437.CrossRefGoogle Scholar
Rau, E.I. & Robinson, V.N.E. (1996). An annular toroidal backscattered electron energy analyser for use in scanning electron microscopy. Scanning 18, 556–561.CrossRefGoogle Scholar
Ritchie, N.W.M. (2005). A new Monte Carlo application for complex sample geometries. Surf Interf Anal 37(11), 1006–1011.CrossRefGoogle Scholar
Salvat, F., Fernández-Varea, J.M. & Sempau, J. (2006). PENELOPE-2006: A code system for Monte Carlo simulation of electron and photon transport. Issy-les-Moulineaux, France: OECD Nuclear Energy Agency.Google Scholar
Tan, Z. & Xia, Y. (2002). A Monte Carlo study of the thickness determination of ultra-thin films. Scanning 24, 257–263.Google Scholar
Tanuma, S., Powell, C.J. & Penn, D.R. (1994). Calculations of electron inelastic mean free paths. V. Data for 14 organic compounds over the 50–2000 eV range. Surf Interf Anal 21(3), 165–176.CrossRefGoogle Scholar
Tanuma, S., Powell, C.J. & Penn, D.R. (2003). Calculation of electron inelastic mean free paths (IMFPs) VII. Reliability of the TPP-2M IMFP predictive equation. Surf Interf Anal 35(3), 268–275.CrossRefGoogle Scholar
Tanuma, S., Powell, C.J. & Penn, D.R. (2005). Calculations of stopping powers of 100 eV to 30 keV electrons in 10 elemental solids. Surf Interf Anal 37(11), 978–988.CrossRefGoogle Scholar
Vladár, A.E. (1999). Time-lapse scanning electron microscopy for measurement of contamination rate and stage drift. Scanning 21(3), 191–196.CrossRefGoogle Scholar
Waldo, R.A., Militello, M.C. & Gaarenstroom, S.W. (1993). Quantitative thin-film analysis with an energy-dispersive X-ray detector. Surf Interf Anal 20(2), 111–114.CrossRefGoogle Scholar
Wells, O.C. (1971). Low-loss image for surface scanning electron microscope. Appl Phys Lett 19(7), 232–235.CrossRefGoogle Scholar
Wells, O.C., Broers, A.N. & Bremer, C.G. (1973). Method for examining solid specimens with improved resolution in the scanning electron microscope (SEM). Appl Phys Lett 23(6), 353–355.CrossRefGoogle Scholar
Wells, O.C., LeGoues, F.K. & Hodgson, R.T. (1990). Magnetically filtered low-loss scanning electron microscopy. Appl Phys Lett 56(23), 2351–2353.CrossRefGoogle Scholar
Wells, O.C., McGlashan-Powell, M., Vladár, A.E. & Postek, M.T. (2001). Applications of the low-loss scanning electron microscope image to integrated circuit technology: Part II—Chemically-mechanically planarized samples. Scanning 23(6), 366–371.CrossRefGoogle ScholarPubMed
Wells, O.C. & Munro, E. (1992). Magnetically filtered low-loss scanning electron microscopy. Ultramicroscopy 47(1–3), 101–108.CrossRefGoogle Scholar
Werner, W.S.M. (2001). Electron transport in solids for quantitative surface analysis. Surf Interf Anal 31(3), 141–176.CrossRefGoogle Scholar