Accurate atomic scanning transmission electron microscopy analysis enabled by deep learning

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外文摘要：Currently,the machine learning(ML)-based scanning transmission electron microscopy(STEM)analysis is limited in the simulative stage,its application in experimental STEM is needed but challenging.Herein,we built up a universal model to analyze the vacancy defects and single atoms accurately and rapidly in experimental STEM images using a full convolution network.In our model,the unavoidable interference factors of noise,aberration,and carbon contamination were fully considered during the training,which were difficult to be considered in the past.Even toward the simultaneous identification of various vacancy types and low-contrast single atoms in the low-quality STEM images,our model showed rapid process speed(45 images per second)and high accuracy(＞95％).This work represents an improvement in experimental STEM image analysis by ML.

外文关键词：

deep learninglow-dimensional materialsatomic defectssingle atoms

作者：

Tianshu Chu、Lei Zhou、Bowei Zhang、Fu-Zhen Xuan

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作者单位：

Shanghai Key Laboratory of Intelligent Sensing and Detection Technology,East China University of Science and Technology,Shanghai 200237,China

School of Mechanical and Power Engineering,East China University of Science and Technology,Shanghai 200237,China

Key Laboratory of Pressure Systems and Safety of Ministry of Education,East China University of Science and Technology,Shanghai 200237,China

基金：

National Natural Science Foundation of ChinaNational Natural Science Foundation of ChinaShanghai Pilot Program for Basic ResearchFundamental Research Funds for the Central UniversitiesFeringa Nobel Prize Scientist Joint Research Center of the East China University of Science and Technology

项目编号：

521051451227412422TQ1400100-6

出版年：

2024

DOI：

10.1007/s12274-023-6104-1

纳米研究(英文版)

CSTPCD

ISSN：

年,卷(期)：2024.17(4)

参考文献量58