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

Abstract

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.