Five-second STEM dislocation tomography for 300&nbsp;nm thick specimen assisted by deep-learning-based noise filtering

Zhao, Yifang; Koike, Suguru; Nakama, Rikuto; Ihara, Shiro; Mitsuhara, Masatoshi; Murayama, Mitsuhiro; Hata, Satoshi; Saito, Hikaru

doi:10.1038/s41598-021-99914-5

Five-second STEM dislocation tomography for 300 nm thick specimen assisted by deep-learning-based noise filtering

Journal Article · Tue Oct 26 00:00:00 EDT 2021 · Scientific Reports

DOI:https://doi.org/10.1038/s41598-021-99914-5· OSTI ID:2471558

Zhao, Yifang ^[1]; Koike, Suguru ^[1]; Nakama, Rikuto ^[1]; Ihara, Shiro ^[1]; Mitsuhara, Masatoshi ^[1]; Murayama, Mitsuhiro ^[1]; Hata, Satoshi ^[1]; Saito, Hikaru ^[1]

Kyushu University, Fukuoka (Japan)

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.

View Accepted Manuscript (DOE)

Research Organization:: Pacific Northwest National Laboratory (PNNL), Richland, WA (United States)

Sponsoring Organization:: JSPS KAKENHI; USDOE

Grant/Contract Number:: AC05-76RL01830

OSTI ID:: 2471558

Journal Information:: Scientific Reports, Journal Name: Scientific Reports Journal Issue: 1 Vol. 11; ISSN 2045-2322

Publisher:: Nature Publishing GroupCopyright Statement

Country of Publication:: United States

Language:: English

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Five-second STEM dislocation tomography for 300 nm thick specimen assisted by deep-learning-based noise filtering

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