期刊
MICROSCOPY AND MICROANALYSIS
卷 27, 期 4, 页码 712-743出版社
CAMBRIDGE UNIV PRESS
DOI: 10.1017/S1431927621000477
关键词
calibration; diffraction; open source; STEM; 4D-STEM
资金
- Toyota Research Institute
- Office of Science, Office of Basic Energy Sciences, of the U.S. Department of Energy [DE-AC02-05CH11231]
- STROBE, an NSF Science and Technology Center [DMR 1548924]
- US Office of Naval Research [N00014-17-1-2283]
- DFG-project [BR 5095/2-1]
- Dow University Partnership Initiative Program
- U.S. Department of Energy Basic Energy Research Materials Sciences and Engineering Division Contract [KC22ZH]
- U.S. Department of Energy, Office of Science, Basic Energy Sciences, Materials Sciences and Engineering Division [LANLE4BU]
- National Nuclear Security Administration of the U.S. Department of Energy [89233218CNA000001]
- Department of Energy Early Career Research Award
- Presidential Early Career Award for Scientists and Engineers (PECASE) through the U.S. Department of Energy
- U.S. Department of Energy Office of Science User Facility [DE-AC02-05CH11231]
STEM enables imaging, diffraction, and spectroscopy of materials at various length scales, producing rich 4D-STEM datasets. The py4DSTEM analysis toolkit, written in Python, aims to extract material properties through complex analysis pipelines, with the goal of improving standards in electron microscopy data and methods.
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.
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