Hostname: page-component-586b7cd67f-g8jcs Total loading time: 0 Render date: 2024-11-24T06:16:14.822Z Has data issue: false hasContentIssue false

Structural Mapping of Disordered Materials by Nanobeam Diffraction Imaging and Multivariate Statistical Analysis

Published online by Cambridge University Press:  11 March 2013

Ping Lu*
Affiliation:
Sandia National Laboratories, Materials Characterization Department, P.O. Box 5800, Albuquerque, NM 87185-1411, USA
Bryan D. Gauntt
Affiliation:
Sandia National Laboratories, Materials Characterization Department, P.O. Box 5800, Albuquerque, NM 87185-1411, USA
*
*Corresponding author. E-mail: [email protected]
Get access

Abstract

A hybrid nanobeam diffraction/imaging method, which combines well-developed diffraction imaging with nanobeam diffraction (NBD) pattern analysis, is described for structural mapping of disordered materials. Spatially resolved crystallographic information is obtained by NBD imaging by collecting NBD patterns at predefined intervals within a field of interest. The resulting dataset of NBD patterns is preprocessed to produce a spectral-imaging-like dataset and is further analyzed via multivariate statistical analysis methods in order to extract the relevant structural components and their distribution within the area of the sample under study without prior knowledge. Additional radial distribution function analysis of either the principal components or averaged data provides real-space maps of short-range order within the field of interest. This technique is demonstrated for two systems, one with multiple amorphous phases and one with multiple phases (amorphous and nanocrystalline) with similar chemistry.

Type
Materials Applications
Copyright
Copyright © Microscopy Society of America 2013

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

Alloyeau, D., Ricolleau, C., Oikawa, T., Langlois, C., Bouar, Y.L. & Loiseau, A. (2008). STEM nanodiffraction technique for structural analysis of CoPt nanoparticles. Ultramicroscopy 108, 656662.CrossRefGoogle ScholarPubMed
Armigliato, A., Frabboni, S. & Gazzadi, G.C. (2008). Electron diffraction with ten nanometer beam size for strain analysis of nanodevices. Appl Phys Lett 93, 161906. CrossRefGoogle Scholar
Beche, A., Clement, L. & Rouviere, J.-L. (2010). Improved accuracy in nano beam electron diffraction. J Phys 209, 012063. Google Scholar
Beche, A., Rouviere, J.L., Clement, L. & Hartmann, J.M. (2009). Improved precision in strain measurement using nanobeam electron diffraction. Appl Phys Lett 95, 123114. CrossRefGoogle Scholar
Bonnet, N., Brun, N. & Colliex, C. (1999). Extracting information from sequences of spatially resolved EELS spectra using multivariate statistical analysis. Ultramicroscopy 77, 97112.CrossRefGoogle Scholar
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), 10241032.CrossRefGoogle ScholarPubMed
Brewer, L.N., Kotula, P.G. & Michael, J.R. (2008). Multivariate statistical approach to electron backscattered diffraction. Ultramicroscopy 108, 567578.CrossRefGoogle ScholarPubMed
Cockayne, D.J.H. (2007). The study of nanovolumes of amorphous materials using electron scattering. Ann Rev Mater Res 37, 159187.CrossRefGoogle Scholar
Cockayne, D., Chen, Y., Li, G. & Borisenko, K. (2009). The technique of RDF of nanovolumes using electron diffraction. J Phys Conf Ser 241, 012006. Google Scholar
Cockayne, D.J.H., McKenzie, D.R., McBride, W., Goringe, C. & McCulloch, D. (2000). Characterization of amorphous material by electron diffraction and atomistic modeling. Microsc Microanal 6, 329334.CrossRefGoogle ScholarPubMed
Cooper, D., Beche, A., Hartmann, J.-M., Carron, V. & Rouviere, J.-L. (2010). Strain mapping for the semiconductor industry by dark-field electron holography and nanobeam electron diffraction with nm resolution. Semicond Sci Technol 25, 095012. CrossRefGoogle Scholar
Cowley, J.M. (1999). Electron nanodiffraction. Microsc Res Techniq 46, 7597.3.0.CO;2-S>CrossRefGoogle ScholarPubMed
Du, H.L., Datta, P.K., Inman, I., Geurts, R. & Kubel, C. (2003). Microscopy of wear affected surface produced during sliding of Nimonic 80A against Stellite 6 at 20°C. Mater Sci Eng A 357, 412422.CrossRefGoogle Scholar
Favia, P., Klenov, D., Eneman, G., Verheyen, P., Bauer, M., Weeks, D., Thomas, S.G. & Bender, H. (2008). Strain study in transistors with SiC and SiGe source and drain by STEM nano beam diffraction. EMC 2008 14th European Microscopy Congress, Aachen, Germany, September 1–5, 2008, pp. 1516. Berlin, Heidelberg: Springer.Google Scholar
Ganesh, K.J., Kawasaki, M., Zhou, J.P. & Ferreira, P.J. (2010). D-STEM: A parallel electron diffraction technique applied to nanomaterials. Microsc Microanal 16, 614621.CrossRefGoogle ScholarPubMed
Hirata, A., Guan, P., Fujita, T., Hirotsu, Y., Inoue, A., Yavari, A.R., Sakurai, T. & Chen, M. (2010). Direct observation of local atomic order in a metallic glass. Nat Mater 10, 2833.CrossRefGoogle Scholar
Hirotsu, Y., Ishimaru, M., Ohkubo, T., Hanada, T. & Sugiyama, M. (2001). Application of nano-diffraction to local atomic distribution function analysis of amorphous materials. J Electron Microsc 50, 435442.CrossRefGoogle ScholarPubMed
Ishimaru, M. (2006). Electron-beam radial distribution analysis of irradiation-induced amorphous SiC. Nucl Instrum Methods Phys Res B 250, 309314.CrossRefGoogle Scholar
Ishimaru, M., Bae, I.-T., Hirotsu, Y., Matsumura, S. & Sickafus, K.E. (2002). Structural relaxation of amorphous silicon carbide. Phys Rev Lett 89, 055502. CrossRefGoogle ScholarPubMed
Ishimaru, M., Hirata, A., Naito, M., Bae, I.-T., Zhang, Y. & Weber, W.J. (2008). Direct observations of thermally induced structural changes in amorphous silicon carbide. J Appl Phys 104, 033503. CrossRefGoogle Scholar
Kaiser, H.F. (1958). The varimax criterion for analytic rotation in factor analysis. Psychometrika 23, 187200.CrossRefGoogle Scholar
Keenan, M.R. (2009). Exploiting spatial-domain simplicity in spectral image analysis. Surf Interf Anal 41(2), 7987.CrossRefGoogle Scholar
Keenan, M.R. & Kotula, P.G. (2004a). Accounting for Poisson noise in the multivariate analysis of ToF-SIMS spectrum images. Surf Interf Anal 36, 203212.CrossRefGoogle Scholar
Keenan, M.R. & Kotula, P.G. (2004b). Optimal scaling of TOF-SIMS spectrum-images prior to multivariate statistical analysis. Appl Surf Sci 231232, 240244.CrossRefGoogle Scholar
Keenan, M.R., Smentkowski, V.S., Ulfig, R.M., Oltman, E., Larson, D.J. & Kelly, T.F. (2010). Multivariate statistical analysis of atom probe tomography data. Microsc Microanal 16(Suppl 2), 270271.CrossRefGoogle Scholar
Kimoto, K., Isakozawa, S., Aoyama, T. & Matsui, Y. (2001). Spatially-resolved EELS analysis of multilayer using EFTEM and STEM. J Electron Microsc 50, 523528.CrossRefGoogle ScholarPubMed
Kolb, U., Gorelik, T., Kubel, C., Otten, M.T. & Hubert, D. (2007). Towards automated diffraction tomography: Part I—Data acquisition. Ultramicroscopy 107, 507513.CrossRefGoogle ScholarPubMed
Kolb, U., Mugnaioli, E. & Gorelik, T.E. (2011). Automated electron diffraction tomography—A new tool for nano crystal structure analysis. Crystal Res Technol 46, 542554.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, 538544.CrossRefGoogle Scholar
Kotula, P.G., Kennan, M.R. & Michael, J.R. (2003). Automated analysis of SEM X-ray spectral images: A powerful new microanalysis tool. Microsc Microanal 9, 117.CrossRefGoogle ScholarPubMed
McBride, W. & Cockayne, D.J.H. (2003). The structure of nanovolumes of amorphous materials. J Non-Cryst Solids 318, 233238.CrossRefGoogle Scholar
Mitchell, D.R.G. & Petersen, T.C. (2012). RDFTools: A software tool for quantifying short-range order in amorphous materials. Microsc Res Techniq 75, 153162.CrossRefGoogle ScholarPubMed
Naito, M., Ishimaru, M. & Hirotsu, Y. (2004). Local structural analysis of Ge-Sb-Te phase change materials using high-resolution electron microscopy and nanobeam diffraction. J Appl Phys 95, 81308135.CrossRefGoogle Scholar
Sarahan, M.C., Chi, M., Masiel, D.J. & Browning, N.D. (2011). Point defect characterization in HAADF-STEM images using multivariate staistical analysis. Ultramicroscopy 111(3), 251257.CrossRefGoogle Scholar
Street, R.A. (1991). Hydrogenated Amorphous Silicon. Cambridge, UK: Cambridge University Press.CrossRefGoogle Scholar
Tewes, M., Zweck, J. & Hoffmann, H. (1994). Derivation of partial pair distribution functions for amorphous FeTb from electron scattering data based on a new concept. J Phys Cond Matter 6, 835848.CrossRefGoogle Scholar
Usada, K., Numata, T., Irisawa, T., Hirashita, N. & Takagi, S. (2005). Strain characterization in SOI and strained-Si on SGOI MOSFET channel using nano-beam electron diffraction (NBD). Mater Sci Eng B 124125, 143147.CrossRefGoogle Scholar