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Challenges to Quantitative Multivariate Statistical Analysis of Atomic-Resolution X-Ray Spectral

Published online by Cambridge University Press:  31 July 2012

Paul G. Kotula ,
Dmitri O. Klenov  and
H. Sebastian von Harrach
Paul G. Kotula*
Affiliation:
Sandia National Laboratories, P.O. Box 5800, MS 0886, Albuquerque, NM 87185-0886, USA
Dmitri O. Klenov
Affiliation:
FEI Company, Building AAE, Achtseweg Noord 5, Eindhoven, The Netherlands
H. Sebastian von Harrach
Affiliation:
FEI Company, Building AAE, Achtseweg Noord 5, Eindhoven, The Netherlands
*
Corresponding author. E-mail: [email protected]
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Abstract

A new aberration-corrected scanning transmission electron microscope equipped with an array of Si-drift energy-dispersive X-ray spectrometers has been utilized to acquire spectral image data at atomic resolution. The resulting noisy data were subjected to multivariate statistical analysis to noise filter, remove an unwanted and partially overlapping non-sample-specific X-ray signal, and extract the relevant correlated X-ray signals (e.g., channels with L and K lines). As an example, the Y2Ti2O7 pyrochlore-structured oxide (assumed here to be ideal) was interrogated at the [011] projection. In addition to pure columns of Y and Ti, at this projection, there are also mixed 50-50 at. % Y-Ti columns. An attempt at atomic-resolution quantification is presented. The method proposed here is to subtract the non-column-specific signal from the elemental components and then quantify the data based upon an internally derived k-factor. However, a theoretical basis to predict this non-column-specific signal is needed to make this generally applicable.

Keywords

atomic-resolution X-ray microanalysisspectral imagingquantificationmultivariate statistical analysisaberration-corrected scanning transmission electron microscopy
Type
Special Section: Aberration-Corrected Electron Microscopy
Information
Microscopy and Microanalysis , Volume 18 , Issue 4 , August 2012 , pp. 691 - 698
Copyright
Copyright © Microscopy Society of America 2012

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References

Browning, N.D., Chisholm, M.F. & Pennycook, S.J. (1993). Atomic resolution chemical analysis using a scanning transmission electron microscope. Nature 366, 143–146.CrossRefGoogle Scholar
Chu, M.-W., Liou, S.C., Chang, C.-P., Choa, F.-S. & Chen, C.H. (2010). Emergent chemical mapping at atomic-column resolution by energy-dispersive X-ray spectroscopy in an aberration-corrected electron microscope. Phys Rev Lett 104, 196101. CrossRefGoogle Scholar
Cliff, G. & Lorimer, G.W. (1975). The quantitative analysis of thin specimens. J Microsc 103, 203–207.CrossRefGoogle Scholar
D'Alfonso, A.J., Freitag, B., Klenov, D. & Allen, L.J. (2010). Atomic-resolution chemical mapping using energy-dispersive X-ray spectroscopy. Phys Rev B 81, 100101(R). Google Scholar
Egerton, R.F. (1996). Electron Energy Loss Spectroscopy in the Electron Microscope, 2nd ed. New York: Plenum Press.Google Scholar
Erni, R., Rossell, M.D., Kisielowski, C. & Dahmen, U. (2009). Atomic-resolution imaging with a sub-50-pm electron probe. Phys Rev Lett 102, 096101. CrossRefGoogle ScholarPubMed
Fiori, C., Swyt, C. & Myklebust, R. (1991). Desktop Spectrum Analyzer, public domain software available from the National Institute of Standards and Technology website: http://www.cstl.nist.gov/div837/Division/outputs/DTSA/oldDTSA.htm.Google Scholar
Goldstein, J.I., Jones, R.H., Kotula, P.G. & Michael, J.R. (2007). Microstructure and thermal history of metal particles in CH chondrites. Meteor Planet Sci 42, 913–933.CrossRefGoogle Scholar
Guiton, B.S., Iberi, V., Li, S., Leonard, D.N., Parish, C.M., Kotula, P.G., Varela, M., Schatz, G.C., Pennycook, S.J. & Camden, J.P. (2011). Correlated optical measurements and plasmon mapping of silver nanorods. Nano Lett 11, 3482–3488.CrossRefGoogle ScholarPubMed
Hillyard, S. & Silcox, J. (1993). Detector geometry, thermal diffuse scattering and strain effects in ADF STEM imaging. Ultramicroscopy 52, 325.CrossRefGoogle Scholar
Keenan, M.R. (2009). Exploiting spatial-domain simplicity in spectral image analysis. Surf Interf Anal 41, 79–87.Google Scholar
Keenan, M.R. & Kotula, P.G. (2003). Apparatus and system for multivariate spectral analysis. U.S. Patent #6,584,413. Google Scholar
Keenan, M.R. & Kotula, P.G. (2004a). Optimal scaling of TOF-SIMS spectrum-images prior to multivariate statistical analysis. Surf Int Anal 36, 203–212.Google Scholar
Keenan, M.R. & Kotula, P.G. (2004b). Method of multivariate spectral analysis. U.S. Patent #6,675,106. Google Scholar
Keenan, M.R., Smentkowski, V.S., Ulfig, R.M., Oltman, E., Larson, D.J. & Kelly, D.F. (2011). Atomic-scale phase composition through multivariate statistical analysis of atom probe tomography data. Microsc Microanal 17, 418–430.Google Scholar
Klenov, D.O. & Stemmer, S. (2006). Contributions to the contrast in experimental high-angle annular dark-field images. Ultramicroscopy 106, 889–901.Google Scholar
Klenov, D.O. & Zide, J.M.O. (2011). Structure of the InAlAs/InP interface by atomically resolved energy dispersive spectroscopy. Appl Phys Lett 99, 141904. Google Scholar
Kotula, P.G. & Keenan, M.R. (2003). Automated analysis of large (>4 Gb) spectral images with efficient out-of-core RAM algorithms. Microsc Microanal 9(Suppl 2), 152–153.CrossRefGoogle Scholar
Kotula, P.G. & Keenan, M.R. (2006). Application of multivariate statistical analysis to STEM X-ray spectral images: Interfacial analysis in microelectronics. Microsc Microanal 12, 538–544.Google Scholar
Kotula, P.G., Keenan, M.R. & Michael, J.R. (2003). Automated analysis of EDS spectral images in the SEM: A powerful new microanalysis technique. Microsc Microanal 9, 1–17.CrossRefGoogle Scholar
Müller, H., Uhlemann, S., Hartel, P. & Haider, M. (2006). Advancing the hexapole Cs-corrector for the scanning transmission electron microscope. Microsc Microanal 12, 442–455.CrossRefGoogle ScholarPubMed
Nellist, P.D. & Pennycook, S.J. (1999). Incoherent imaging using dynamically scattered coherent electrons. Ultramicroscopy 78, 111.Google Scholar
Parish, C.M., Brennecka, G.L., Tuttle, B.A. & Brewer, L.N. (2008). Quantitative X-ray spectrum imaging of lead lanthanum zirconate titanate PLZT thin-films. J Am Ceram Soc 91, 3690–3697.CrossRefGoogle Scholar
Parish, C.M. & Brewer, L.N. (2010). Multivariate statistics applications in phase analysis of STEM-EDS spectrum images. Ultramicroscopy 110, 134–143.CrossRefGoogle ScholarPubMed
Pennycook, S.J., Chisholm, M.F., Lupini, A.R., Varela, M., Borisevich, A.Y., Oxley, M.P., Luo, W.D., Van Benthem, K., Oh, S.-H., Sales, D.L., Molina, S.I., García-barriocanal, J., Leon, C., Santamaría, J., Rashkeev, S.N. & Pantelides, S.T. (2009). Aberration-corrected scanning transmission electron microscopy: From atomic imaging and analysis to solving energy problems. Phil Trans R Soc A 367, 3709–3733.CrossRefGoogle ScholarPubMed
Rossouw, C.J., Allen, L.J., Findlay, S.D. & Oxley, M.P. (2003). Channelling effects in atomic resolution STEM. Ultramicroscopy 96, 299.CrossRefGoogle ScholarPubMed
Titchmarsh, J.M. (1999). EDX spectrum modeling and multivariate analysis of sub-nanometer segregation. Micron 30, 159–171.Google Scholar
Titchmarsh, J.M. & Dumbill, S. (1996). Multivariate statistical analysis of FEG-STEM EDX spectra. J Microsc 184, 195–207.Google Scholar
Von Harrach, H.S., Dona, P., Freitag, B., Soltau, H., Niculae, A. & Rohde, M. (2010). An integrated multiple silicon drift detector system for transmission electron microscopes. J Phys 241, 012015. Google Scholar
Watanabe, M., Ackland, D.W., Burrows, A., Kiely, C.J., Williams, D.B., Krivanek, O.L., Dellby, N., Murfitt, M.F. & Szilagyi, Z. (2006). Improvements in the X-ray analytical capabilities of a scanning transmission electron microscope by spherical-aberration correction. Microsc Microanal 12, 515–526.Google Scholar
Watanabe, M., Kanno, M. & Okunishi, E. (2010). Atomic-resolution elemental mapping by EELS and XEDS in aberration corrected STEM. JEOL News 45, 8–15.Google Scholar
Watanabe, M. & Williams, D.B. (2003). Quantification of elemental segregation to lath and grain boundaries in low-alloy steel by STEM X-ray mapping combined with the ζ-factor method. Z Metallk 94, 307–316.Google Scholar
Williams, D.B. & Carter, C.B. (2009). Transmission Electron Microscopy: A Textbook for Materials Science, 2nd ed. New York: Springer.Google Scholar
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