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

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作者： Yifang,Zhao ^[1] ; Suguru,Koike ^[1] ; Rikuto,Nakama ^[2] ; Shiro,Ihara ^[3] ; Masatoshi,Mitsuhara ^[4] ; Mitsuhiro,Murayama ^[3] ; Satoshi,Hata ^[5] ; Hikaru,Saito ^[6]

作者单位： Department of Applied Science for Electronics and Materials, Kyushu University, Fukuoka, 816-8580, Japan. ^[1] Department of Energy Science and Engineering, Kyushu University, Fukuoka, 819-0395, Japan. ^[2] Institute for Materials Chemistry and Engineering, Kyushu University, Fukuoka, 816-8580, Japan. ^[3] Department of Advanced Materials Science and Engineering, Kyushu University, Fukuoka, 816-8580, Japan. ^[4] Department of Materials Science and Engineering, Virginia Tech, Blacksburg, VA, 24061, USA. ^[5] Reactor Materials and Mechanical Design Group, Energy and Environmental Directorate, Pacific Northwest National Laboratory, Richland, WA, 99352, USA. ^[6] The Ultramicroscopy Research Center, Kyushu University, Fukuoka, 819-0395, Japan. ^[7] Institute for Materials Chemistry and Engineering, Kyushu University, Fukuoka, 816-8580, Japan. saito.hikaru.961@m.kyushu-u.ac.jp. ^[8] Pan-Omics Data-Driven Research Innovation Center, Kyushu University, Fukuoka, 816-8580, Japan. saito.hikaru.961@m.kyushu-u.ac.jp. ^[9]

DOI 10.1038/s41598-021-99914-5

PMID 34702955

发布时间 2021-10-30

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Scientific reports

2021年11卷1期

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

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