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Research Article

Accurate atomic scanning transmission electron microscopy analysis enabled by deep learning

Tianshu Chu1,2,3,§, Lei Zhou1,2,3,§, Bowei Zhang1,2,3( ), Fu-Zhen Xuan1,2,3( )
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

§ Tianshu Chu and Lei Zhou contributed equally to this work.

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Graphical Abstract

Metal single atoms and atomic vacancies greatly affect the intrinsic activity of materials, but they are difficult to accurately identify by scanning transmission electron microscopy (STEM). In this work, we build up a universal model to analyze the vacancy defects and single atoms accurately and rapidly in experimental STEM images using a full convolution network.

Abstract

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.

Keywords

deep learninglow-dimensional materialsatomic defectssingle atoms

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Nano Research
Volume 17 Issue 4,
April 2024
Pages 2971-2980
DOI: 10.1007/s12274-023-6104-1
Cite this article:
Chu T, Zhou L, Zhang B, et al. Accurate atomic scanning transmission electron microscopy analysis enabled by deep learning. Nano Research, 2024, 17(4): 2971-2980. https://doi.org/10.1007/s12274-023-6104-1
Topics:
Catalyst activityChemical sensors High resolution transmission electron microscopyImage processing

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Received: 11 July 2023
Revised: 14 August 2023
Accepted: 16 August 2023
Published: 23 September 2023
© Tsinghua University Press 2023
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