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STEM Image Analysis Based on Deep Learning: Identification of Vacancy Defects and Polymorphs of MoS2

Authors
kihyun LeeJINSUB PARKChoi SoyeonYANGJIN LEE이솔Jung JoowonLee Jong-YoungUllah FarmanTahir ZeeshanKim Yong SooLee Gwan-HyoungKWANPYO KIM
Issue Date
Jun-2022
Publisher
AMER CHEMICAL SOC
Keywords
Deep learning; TEM image analysis; Molybdenum disulfide; Defect; Polymorph
Citation
NANO LETTERS, v.22, no.12, pp.4,677 - 4,685
Journal Title
NANO LETTERS
Volume
22
Number
12
Start Page
4,677
End Page
4,685
URI
https://yscholarhub.yonsei.ac.kr/handle/2021.sw.yonsei/6303
DOI
10.1021/acs.nanolett.2c00550
ISSN
1530-6984
Abstract
Scanning transmission electron microscopy (STEM) is an indispensable tool for atomic-resolution structural analysis for a wide range of materials. The conventional analysis of STEM images is an extensive hands-on process, which limits efficient handling of high-throughput data. Here, we apply a fully convolutional network (FCN) for identification of important structural features of two-dimensional crystals. ResUNet, a type of FCN, is utilized in identifying sulfur vacancies and polymorph types of MoS2 from atomic resolution STEM images. Efficient models are achieved based on training with simulated images in the presence of different levels of noise, aberrations, and carbon contamination. The accuracy of the FCN models toward extensive experimental STEM images is comparable to that of careful hands-on analysis. Our work provides a guideline on best practices to train a deep learning model for STEM image analysis and demonstrates FCN's application for efficient processing of a large volume of STEM data.
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