Accurate atomic scanning transmission electron microscopy analysis enabled by deep learning  

作　　者：Tianshu Chu Lei Zhou Bowei Zhang Fu-Zhen Xuan 

机构地区：[1]Shanghai Key Laboratory of Intelligent Sensing and Detection Technology,East China University of Science and Technology,Shanghai 200237,China [2]School of Mechanical and Power Engineering,East China University of Science and Technology,Shanghai 200237,China [3]Key Laboratory of Pressure Systems and Safety of Ministry of Education,East China University of Science and Technology,Shanghai 200237,China

出　　处：《Nano Research》2024年第4期2971-2980,共10页纳米研究（英文版）

基　　金：supported by the National Natural Science Foundation of China(Nos.52105145 and 12274124);the Shanghai Pilot Program for Basic Research(No.22TQ1400100-6);the Fundamental Research Funds for the Central Universities.

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.

关 键 词：deep learning low-dimensional materials atomic defects single atoms 

分 类 号：TH742[机械工程—光学工程]