py4DSTEM: A Software Package for Four-Dimensional Scanning Transmission Electron Microscopy Data Analysis

• DOI: 10.1017/s1431927621000477
• Publication Date: 2021-05-21T09:33:43Z
• Authors: Benjamin H. Savitzky; Steven E. Zeltmann; Lauren A. Hughes; Hamish G. Brown; Shiteng Zhao; Philipp M. Pelz; Thomas C. Pekin; Edward S. Barnard; Jennifer Donohue; Luis Rangel DaCosta; Ellis Kennedy; Yujun Xie; Matthew T. Janish; Matthew M. Schneider; Patrick Herring; Chirranjeevi Gopal; Abraham Anapolsky; Rohan Dhall; Karen C. Bustillo; Peter Ercius; Mary C. Scott; Jim Ciston; Andrew M. Minor; Colin Ophus
• Keywords: diffraction; FOS: Physical sciences; Bioengineering; Applied Physics (physics.app-ph); 530; Engineering; open source; cond-mat.mes-hall; Mesoscale and Nanoscale Physics (cond-mat.mes-hall); Microscopy; Condensed Matter - Materials Science; Condensed Matter - Mesoscale and Nanoscale Physics; Materials engineering; Data Science; 500; Materials Science (cond-mat.mtrl-sci); Materials Engineering; Physics - Applied Physics; Biological Sciences; STEM; calibration; Condensed Matter Physics; cond-mat.mtrl-sci; Networking and Information Technology R&D (NITRD); Biochemistry and cell biology; Biochemistry and Cell Biology; physics.app-ph; 4D-STEM

Scanning transmission electron microscopy (STEM) allows for imaging, diffraction, and spectroscopy of materials on length scales ranging from microns to atoms. By using a high-speed, direct electron detector, it is now possible to record a full two-dimensional (2D) image of the diffracted electron beam at each probe position, typically a 2D grid of probe positions. These 4D-STEM datasets are rich in information, including signatures of the local structure, orientation, deformation, electromagnetic fields, and other sample-dependent properties. However, extracting this information requires complex analysis pipelines that include data wrangling, calibration, analysis, and visualization, all while maintaining robustness against imaging distortions and artifacts. In this paper, we present py4DSTEM, an analysis toolkit for measuring material properties from 4D-STEM datasets, written in the Python language and released with an open-source license. We describe the algorithmic steps for dataset calibration and various 4D-STEM property measurements in detail and present results from several experimental datasets. We also implement a simple and universal file format appropriate for electron microscopy data in py4DSTEM, which uses the open-source HDF5 standard. We hope this tool will benefit the research community and help improve the standards for data and computational methods in electron microscopy, and we invite the community to contribute to this ongoing project.