A Python Based Open-source Multislice Simulation Package for Transmission Electron Microscopy

  • Brown H
  • Pelz P
  • Ophus C
  • et al.
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Abstract

Strong electron multiple scattering in transmission electron microscopy (TEM) means that most quantitative work in electron microscopy requires accompanying image simulations for reliable interpretation. Recent years have seen a number of different TEM simulation packages released, many of them open source. Most are written in programming languages such as C++ [1-2] and Fortran [3] which results in the fastest possible run-time for simulations but means that modification and extension of the code is difficult for non-expert programmers. Inspired by the popular success of open-source electron microscopy packages such as HyperSpy [4] and py4DSTEM [5], which are written in the more accessible Python programming language, in this presentation we introduce an open-source purely python based TEM simulation package which can run on graphical processing units (GPUs) using the Python pytorch library [6]. The package is capable of Fig 1.(a) convergent beam electron diffraction (CBED), Fig 1. (b) scanning transmission electron microscopy (STEM) and Fig 1. (c) 4D-STEM simulations. Ionization based simulations such as STEM-EELS are implemented using the open-source Flexible Atomic Code (FAC) [7] to calculate ionization cross sections for atoms of interest. Recent algorithmic advances such as the PRISM algorithm, which economized on the number of multislice operations necessary by calculating the scattering matrix for the sample of interest, are implemented both for conventional STEM [8] and STEM-EELS [9] in the package. An example simulation of an O map of a SrTiO 3-CeO 2 interface structure, which took 1.5 hours in our python package but would likely have taken days in conventional multislice, is shown in Fig.

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Brown, H., Pelz, P., Ophus, C., & Ciston, J. (2020). A Python Based Open-source Multislice Simulation Package for Transmission Electron Microscopy. Microscopy and Microanalysis, 26(S2), 2954–2956. https://doi.org/10.1017/s1431927620023326

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