TY - GEN
T1 - Deep Learning for Analysis of Two-Dimensional Materials in High-Resolution Transmission Electron Microscopy Image
AU - Sheng, Zhiwei
AU - Xie, Jing
AU - Zhang, Shiyi
AU - Zhang, Mingyang
AU - Luo, Chen
AU - Dong, Zuoyuan
AU - Wang, Yan
AU - Wu, Xing
AU - Wang, Chaolun
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - Two-dimensional (2D) materials possess exceptional electrical, mechanical, thermal, and optical properties, making them widely applicable in fields of electronics, energy, optoelectronics, and medicine. The geometry of 2D materials at the nanoscale, such as the number of layers, interlayer spacing, and thickness of the layers are of particular interest, as they have great influence to the properties of the material. Transmission electron microscopy (TEM) with atomic resolution is an ideal research method for 2D materials. However, analyzing the properties of 2D materials by interpreting the high-resolution TEM images not only require rich expertise but also is a time-consuming and labor-intensive task. In this work, we propose a neural network based on the U-Net architecture for high efficiency TEM image analysis of the geometry of 2D materials. The effectiveness of this method is verified on MoS2 image datasets and obtained dice coefficients of 0.92. The physical parameters such as layer number, thickness, and interlayer spacing information of 2D materials can be analyzed subsequently. Our results provide an artificial intelligent approach to analyze 2D materials.
AB - Two-dimensional (2D) materials possess exceptional electrical, mechanical, thermal, and optical properties, making them widely applicable in fields of electronics, energy, optoelectronics, and medicine. The geometry of 2D materials at the nanoscale, such as the number of layers, interlayer spacing, and thickness of the layers are of particular interest, as they have great influence to the properties of the material. Transmission electron microscopy (TEM) with atomic resolution is an ideal research method for 2D materials. However, analyzing the properties of 2D materials by interpreting the high-resolution TEM images not only require rich expertise but also is a time-consuming and labor-intensive task. In this work, we propose a neural network based on the U-Net architecture for high efficiency TEM image analysis of the geometry of 2D materials. The effectiveness of this method is verified on MoS2 image datasets and obtained dice coefficients of 0.92. The physical parameters such as layer number, thickness, and interlayer spacing information of 2D materials can be analyzed subsequently. Our results provide an artificial intelligent approach to analyze 2D materials.
KW - image segmentation
KW - neural network
KW - transmission electron microscopy
KW - two-dimensional materials
UR - https://www.scopus.com/pages/publications/85173565634
U2 - 10.1109/IPFA58228.2023.10249141
DO - 10.1109/IPFA58228.2023.10249141
M3 - 会议稿件
AN - SCOPUS:85173565634
T3 - Proceedings of the International Symposium on the Physical and Failure Analysis of Integrated Circuits, IPFA
BT - 2023 IEEE International Symposium on the Physical and Failure Analysis of Integrated Circuits, IPFA 2023
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2023 IEEE International Symposium on the Physical and Failure Analysis of Integrated Circuits, IPFA 2023
Y2 - 24 July 2023 through 27 July 2023
ER -