Hostname: page-component-8448b6f56d-tj2md Total loading time: 0 Render date: 2024-04-24T02:09:06.171Z Has data issue: false hasContentIssue false
  • English
  • Français

Data Processing for Atomic Resolution Electron Energy Loss Spectroscopy

Published online by Cambridge University Press:  15 June 2012

Paul Cueva ,
Robert Hovden ,
Julia A. Mundy ,
Huolin L. Xin  and
David A. Muller
Paul Cueva
Affiliation:
School of Applied and Engineering Physics, Cornell University, Ithaca, NY 14853, USA
Robert Hovden*
Affiliation:
School of Applied and Engineering Physics, Cornell University, Ithaca, NY 14853, USA
Julia A. Mundy
Affiliation:
School of Applied and Engineering Physics, Cornell University, Ithaca, NY 14853, USA
Huolin L. Xin
Affiliation:
Department of Physics, Cornell University, Ithaca, NY 14853, USA
David A. Muller
Affiliation:
School of Applied and Engineering Physics, Cornell University, Ithaca, NY 14853, USA Kavli Institute at Cornell for Nanoscale Science, Ithaca, NY 14853, USA
*
Corresponding author. E-mail: rmh244@cornell.edu
Get access

Abstract

The high beam current and subangstrom resolution of aberration-corrected scanning transmission electron microscopes has enabled electron energy loss spectroscopy (EELS) mapping with atomic resolution. These spectral maps are often dose limited and spatially oversampled, leading to low counts/channel and are thus highly sensitive to errors in background estimation. However, by taking advantage of redundancy in the dataset map, one can improve background estimation and increase chemical sensitivity. We consider two such approaches—linear combination of power laws and local background averaging—that reduce background error and improve signal extraction. Principal component analysis (PCA) can also be used to analyze spectrum images, but the poor peak-to-background ratio in EELS can lead to serious artifacts if raw EELS data are PCA filtered. We identify common artifacts and discuss alternative approaches. These algorithms are implemented within the Cornell Spectrum Imager, an open source software package for spectroscopic analysis.

Keywords

spectral mappingEELSSTEMaberration correctionPCAsoftware
Type
Special Section: Aberration-Corrected Electron Microscopy
Information
Microscopy and Microanalysis , Volume 18 , Issue 4 , August 2012 , pp. 667 - 675
Copyright
Copyright © Microscopy Society of America 2012

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

Batson, P.E., Dellby, N. & Krivanek, O.L. (2002). Sub-angstrom resolution using aberration corrected electron optics. Nature 418, 617–620.CrossRefGoogle ScholarPubMed
Bonnet, N. (1999). Extracting information from sequences of spatially resolved EELS spectra using multivariate statistical analysis. Ultramicroscopy 77(3-4), 97–112.CrossRefGoogle Scholar
Bosman, M., Keast, V., Garcia-Munoz, J., Findlay, S. & Allen, L. (2007). Two-dimensional mapping of chemical information at atomic resolution. Phys Rev Lett 99(8), 86102. CrossRefGoogle ScholarPubMed
Bosman, M., Watanabe, M., Alexander, D.T.L. & Keast, V.J. (2006). Mapping chemical and bonding information using multivariate analysis of electron energy-loss spectrum images. Ultramicroscopy 106(11-12), 24–32.CrossRefGoogle ScholarPubMed
Botton, G. A., Lazar, S. & Dwyer, C. (2010). Elemental mapping at the atomic scale using low accelerating voltages. Ultramicroscopy 110(8), 926–934.CrossRefGoogle Scholar
Egerton, R. (2002). Improved background-fitting algorithms for ionization edges in electron energy-loss spectra. Ultramicroscopy 92(2), 47–56.CrossRefGoogle ScholarPubMed
Egerton, R.F. (1975). Inelastic-cattering of 80 kev electrons in amorphous carbon. Philos Mag 31(1), 199–215.CrossRefGoogle Scholar
Egerton, R.F. (1982). A revised expression for signal/noise ratio in EELS. Ultramicroscopy 9, 387–390.CrossRefGoogle Scholar
Egerton, R.F. (2011). Electron Energy-Loss Spectroscopy in the Electron Microscope. Boston, MA: Springer US.CrossRefGoogle Scholar
Friedman, J. (1989). Regularized discriminant analysis. J Am Stat Assoc 84(405), 165–175.CrossRefGoogle Scholar
Haaland, D.M., Jones, H.D.T., Van Benthem, M.H., Sinclair, M.B., Melgaard, D.K., Stork, C.L., Pedroso, M.C., Liu, P., Brasier, A.R., Andrews, N.L. & Lidke, D.S. (2009). Hyperspectral confocal fluorescence imaging: Exploring alternative multivariate curve resolution approaches. Appl Spectrosc 63(3), 271–279.CrossRefGoogle ScholarPubMed
Hunt, J.A. & Williams, D.B. (1991). Electron energy-loss spectrum-imaging. Ultramicroscopy 38(1), 47–73.CrossRefGoogle Scholar
Jeanguillaume, C. & Colliex, C. (1989). Spectrum-image: The next step in EELS digital acquisition and processing. Ultramicroscopy 28(1-4), 252–257.CrossRefGoogle Scholar
Joy, D.C. & Maher, D.M. (1981). The quantitation of electron energy loss spectra. J Microsc 124, 37–48.CrossRefGoogle Scholar
Keenan, M.R. & Kotula, P.G. (2004). Accounting for Poisson noise in the multivariate analysis of ToF-SIMS spectrum images. Surf Interf Anal 36(3), 203–212.CrossRefGoogle Scholar
Krivanek, O.L., Corbin, G.J., Dellby, N., Elston, B.F., Keyse, R.J., Murfitt, M.F., Own, C.S., Szilagyi, Z.S. & Woodruff, J.W. (2008). An electron microscope for the aberration-corrected era. Ultramicroscopy 108(3), 179–195.CrossRefGoogle ScholarPubMed
Leapman, R. (2004). Quantitative EELS analysis. In Transmission Electron Energy Loss Spectrometry in Materials Science and the EELS Atlas, Ahn, C.C. (Ed.), pp. 49–96. Weinheim, Germany: Wiley-VCH Verlag GmbH & Co. CrossRefGoogle Scholar
Liu, D.R. & Brown, L.M. (1987). Influence of some practical factors on background extrapolation in EELS quantification. J Microsc 147, 37–49.CrossRefGoogle Scholar
Muller, D.A. (2009). Structure and bonding at the atomic scale by scanning transmission electron microscopy. Nat Mater 8(4), 263–270.CrossRefGoogle ScholarPubMed
Muller, D.A., Fitting Kourkoutis, L., Murfitt, M., Song, J.H., Hwang, H.Y., Silcox, J., Dellby, N. & Krivanek, O.L. (2008). Atomic-scale chemical imaging of composition and bonding by aberration-corrected microscopy. Science 319(5866), 1073–1076.CrossRefGoogle ScholarPubMed
Okunishi, E., Sawada, H., Kondo, Y. & Kersker, M. (2006). Atomic resolution elemental map of EELS with a Cs corrected STEM. Microsc Microanal 12(S2), 1150–1151.CrossRefGoogle Scholar
Pearson, K. (1901). On lines and planes of closest fit to systems of points in space. Philos Mag 2(6), 559–572.CrossRefGoogle Scholar
Pun, T., Ellis, J. & Eden, M. (1985). Weighted least squares estimation of background in EELS imaging. J Microsc 137, 93–100.CrossRefGoogle ScholarPubMed
Rez, P. (1983). Detection limits and error analysis in energy loss spectrometry. In Microbeam Analysis, Gooley, R. (Ed.), p. 153. San Francisco, CA: San Francisco Press.Google Scholar
Trebbia, P. & Bonnet, N. (1990). EELS elemental mapping with unconventional methods, I. Theoretical basis: Image analysis with multivariate statistics and entropy concepts. Ultramicroscopy 34, 165–178.CrossRefGoogle ScholarPubMed
Unser, M., Ellis, J., Pun, T. & Eden, M. (1987). Optimal background estimation in EELS. J Microsc 145, 245–256.Google ScholarPubMed
Verbeeck, J. & Van Aert, S. (2004). Model based quantification of EELS spectra. Ultramicroscopy 101(2-4), 207–224.CrossRefGoogle ScholarPubMed
Victoreen, J.A. (1943). Probable X-ray mass absorption coefficients for wave-lengths shorter than the K critical absorption wave-length. J Appl Phys 14(2), 95–102.CrossRefGoogle Scholar