文献类型：期刊文献

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

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

机构：[1]East China Univ Sci & Technol, Shanghai Key Lab Intelligent Sensing & Detect Tech, Shanghai 200237, Peoples R China;[2]East China Univ Sci & Technol, Sch Mech & Power Engn, Shanghai 200237, Peoples R China;[3]East China Univ Sci & Technol, Key Lab Pressure Syst & Safety, Minist Educ, Shanghai 200237, Peoples R China

年份：2024

卷号：17

期号：4

起止页码：2971

外文期刊名：NANO RESEARCH

收录：;EI(收录号：20233914779691);WOS:【SCI-EXPANDED(收录号:WOS:001070347300001)】；

基金：This work was supported by the National Natural Science Foundation of China (Nos. 52105145 and 12274124), the Shanghai Pilot Program for Basic Research (No. 22TQ1400100-6), and the Fundamental Research Funds for the Central Universities. Additional support was provided by the Feringa Nobel Prize Scientist Joint Research Center of the East China University of Science and Technology.

语种：英文

外文关键词：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.