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Model-Based Electron Microscopy

  • Sandra Van AertEmail author
Chapter
Part of the Springer Handbooks book series (SHB)

Abstract

The growing interest in materials design and control of nanostructures explains the need for precise determination of the atomic arrangement of non-periodic structures. This includes, for example, locating atomic column positions with a precision in the picometer range, a precise determination of the chemical composition of materials, and counting the number of atoms with single atom sensitivity. In order to extract these quantitative measurements from atomic resolution (scanning) transmission electron microscopy () images, statistical analysis methods are needed. For this purpose, statistical parameter estimation theory has been shown to provide reliable results. In this theory, observations are purely considered as data planes, from which structure parameters have to be determined using a parametric model describing the images. This chapter summarizes the underlying theory and highlights some of the recent applications of quantitative model-based (S)TEM.

model-based parameter estimation quantitative structure determination maximum likelihood estimation experiment design precision resolution probability of error atomic column position measurement composition analysis atom counting 
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Notes

Acknowledgements

The author would like to acknowledge all colleagues who contributed to this work over the years, in particular S. Bals, K.J. Batenburg, A. De Backer, A. De wael, R. Erni, A.J. den Dekker, J. Gonnissen, L. Jones, G.T. Martinez, P.D. Nellist, A. Rosenauer, M.D. Rossell, D. Schryvers, J. Sijbers, K. van den Bos, D. Van Dyck, G. Van Tendeloo, and J. Verbeeck. The author also expresses many thanks for all fruitful and enlightening theoretical discussions, as well as all the shared experimental expertise and knowledge. Sincere thanks are due to A. van den Bos, who unfortunately passed away too soon, for his enthusiastic and expert guidance in the author's understanding of statistical parameter estimation theory.

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Authors and Affiliations

  1. 1.Electron Microscopy for Materials Research (EMAT)University of AntwerpAntwerpBelgium

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