文献类型：期刊文献

中文题名：Accurate atomic scanning transmission electron microscopy analysis enabled by deep learning

作者：Tianshu Chu[1,2,3];Lei Zhou[1,2,3];Bowei Zhang[1,2,3];Fu-Zhen Xuan[1,2,3]

机构：[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

年份：2024

卷号：17

期号：4

起止页码：2971

中文期刊名：Nano Research

外文期刊名：纳米研究（英文版）

收录：CSTPCD;;Scopus;CSCD:【CSCD2023_2024】；PubMed;

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.

语种：英文

中文关键词：deep learning;low-dimensional materials;atomic defects;single atoms

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.