TY - JOUR
T1 - Five-second STEM dislocation tomography for 300 nm thick specimen assisted by deep-learning-based noise filtering
AU - Zhao, Yifang
AU - Koike, Suguru
AU - Nakama, Rikuto
AU - Ihara, Shiro
AU - Mitsuhara, Masatoshi
AU - Murayama, Mitsuhiro
AU - Hata, Satoshi
AU - Saito, Hikaru
N1 - Funding Information:
M. Murayama greatly appreciates the financial support by the JST CREST (JPMJCR1994), JSPS KAKENHI Grant Numbers (JP19H02029, JP20H02479), Virginia Tech National Center for Earth and Environmental Nanotechnology Infrastructure (NanoEarth), a member of the National Nanotechnology Coordinated Infrastructure (NNCI), supported by NSF (ECCS 1542100 and 2025151). S. H. greatly appreciates the financial support by the JST CREST (JPMJCR18J4) and JSPS KAKENHI Grant Numbers (JP18H05479, JP20H02426). H.S. greatly appreciates the valuable discussion with Itsuro Kamimura (Maxnet Co. Ltd.), Katsumi Kawamoto, Nobuya Mamizu, Hiromichi Nagayama, and Hiromitsu Furukawa (SYSTEM IN FRONTIER INC.), and financial support by the JSPS KAKENHI Grant Numbers (JP20K21093).
Publisher Copyright:
© 2021, The Author(s).
PY - 2021/12
Y1 - 2021/12
N2 - 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.
AB - 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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U2 - 10.1038/s41598-021-99914-5
DO - 10.1038/s41598-021-99914-5
M3 - Article
C2 - 34702955
AN - SCOPUS:85118279535
VL - 11
JO - Scientific Reports
JF - Scientific Reports
SN - 2045-2322
IS - 1
M1 - 20720
ER -