期刊
SCIENTIFIC REPORTS
卷 11, 期 1, 页码 -出版社
NATURE PORTFOLIO
DOI: 10.1038/s41598-021-99914-5
关键词
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资金
- JST CREST [JPMJCR1994, JPMJCR18J4]
- JSPS KAKENHI [JP19H02029, JP20H02479, JP18H05479, JP20H02426, JP20K21093]
- Virginia Tech National Center for Earth and Environmental Nanotechnology Infrastructure (NanoEarth), a member of the National Nanotechnology Coordinated Infrastructure (NNCI) - NSF [ECCS 1542100, 2025151]
- Div Of Electrical, Commun & Cyber Sys
- Directorate For Engineering [2025151] Funding Source: National Science Foundation
STEM is suitable for visualizing the inside of thick specimens with higher resolution than conventional transmission electron microscopy, but suffers from noise and artifacts during rapid imaging. This study demonstrates a deep-learning-assisted rapid STEM tomography method that can visualize 3D dislocation arrangement within five seconds, even in a 300 nm thick steel specimen, offering a new platform for various in situ 3D microanalyses.
Scanning transmission electron microscopy (STEM) is suitable for visualizing the inside of a relatively thick specimen than the conventional transmission electron microscopy, whose resolution is limited by the chromatic aberration of image forming lenses, and thus, the STEM mode has been employed frequently for computed electron tomography based three-dimensional (3D) structural characterization and combined with analytical methods such as annular dark field imaging or spectroscopies. However, the image quality of STEM is severely suffered by noise or artifacts especially when rapid imaging, in the order of millisecond per frame or faster, is pursued. Here we demonstrate a deep-learning-assisted rapid STEM tomography, which visualizes 3D dislocation arrangement only within five-second acquisition of all the tilt-series images even in a 300 nm thick steel specimen. The developed method offers a new platform for various in situ or operando 3D microanalyses in which dealing with relatively thick specimens or covering media like liquid cells are required.
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