Deep learning analysis on transmission electron microscope imaging of atomic defects in two-dimensional materials

Chen Gui; Zhihao Zhang; Zongyi Li; Chen Luo; Jiang Xia; Xing Wu; Junhao Chu

iScience (Oct 2023)

Deep learning analysis on transmission electron microscope imaging of atomic defects in two-dimensional materials

Chen Gui,
Zhihao Zhang,
Zongyi Li,
Chen Luo,
Jiang Xia,
Xing Wu,
Junhao Chu

Affiliations

Chen Gui: Shanghai Key Laboratory of Multidimensional Information Processing, School of Communication and Electronic Engineering, East China Normal University, Shanghai 200241, China
Zhihao Zhang: Shanghai Key Laboratory of Multidimensional Information Processing, School of Communication and Electronic Engineering, East China Normal University, Shanghai 200241, China
Zongyi Li: Shanghai Key Laboratory of Multidimensional Information Processing, School of Communication and Electronic Engineering, East China Normal University, Shanghai 200241, China; JCET SEMICONDUCTOR INTEGRATION (SHAOXING) CO, LTD, Shaoxing, Zhejiang 312000, China
Chen Luo: Shanghai Key Laboratory of Multidimensional Information Processing, School of Communication and Electronic Engineering, East China Normal University, Shanghai 200241, China; Institute of Optoelectronics, Fudan University, Shanghai 200433, China. Frontier Institute of Chip and System, Fudan University, Shanghai 200433, China; Corresponding author
Jiang Xia: JCET SEMICONDUCTOR INTEGRATION (SHAOXING) CO, LTD, Shaoxing, Zhejiang 312000, China; Corresponding author
Xing Wu: Shanghai Key Laboratory of Multidimensional Information Processing, School of Communication and Electronic Engineering, East China Normal University, Shanghai 200241, China; State Key Laboratory of Infrared Physics, Shanghai Institute of Technical Physics, Chinese Academy of Sciences, Shanghai 200083, China; Corresponding author
Junhao Chu: Shanghai Key Laboratory of Multidimensional Information Processing, School of Communication and Electronic Engineering, East China Normal University, Shanghai 200241, China; State Key Laboratory of Infrared Physics, Shanghai Institute of Technical Physics, Chinese Academy of Sciences, Shanghai 200083, China; Institute of Optoelectronics, Fudan University, Shanghai 200433, China. Frontier Institute of Chip and System, Fudan University, Shanghai 200433, China

Journal volume & issue: Vol. 26, no. 10
p. 107982

Abstract

Read online

Summary: Defects are prevalent in two-dimensional (2D) materials due to thermal equilibrium and processing kinetics. The presence of various defect types can affect material properties significantly. With the development of the advanced transmission electron microscopy (TEM), the property-related structures could be investigated in multiple dimensions. It produces TEM datasets containing a large amount of information. Traditional data analysis is influenced by the subjectivity of researchers, and manual analysis is inefficient and imprecise. Recent developments in deep learning provide robust methods for the quantitative identification of defects in 2D materials efficiently and precisely. Taking advantage of big data, it breaks the limitations of TEM as a local characterization tool, making TEM an intelligent macroscopic analysis method. In this review, the recent developments in the TEM data analysis of defects in 2D materials using deep learning technology are summarized. Initially, an in-depth examination of the distinctions between TEM and natural images is presented. Subsequently, a comprehensive exploration of TEM data analysis ensues, encompassing denoising, point defects, line defects, planar defects, quantitative analysis, and applications. Furthermore, an exhaustive assessment of the significant obstacles encountered in the accurate identification of distinct structures is also provided.

Published in iScience

ISSN: 2589-0042 (Online)
Publisher: Elsevier
Country of publisher: United States
LCC subjects: Science
Website: http://www.cell.com/iscience/home

About the journal

Abstract

Keywords