中国物理B ›› 2025, Vol. 34 ›› Issue (1): 16802-016802.doi: 10.1088/1674-1056/ad9ba3

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Combining machine learning algorithms with traditional methods for resolving the atomic-scale dynamic structure of monolayer MoS2 in high-resolution transmission electron microscopy

Yu Meng(蒙宇)1,†, Shuya Wang(王淑雅)1,†, Xibiao Ren(任锡标)2, Han Xue(薛涵)3, Xuejun Yue(岳学军)1, Chuanhong Jin(金传洪)2,‡, Shanggang Lin(林上港)1,†, and Fang Lin(林芳)1,¶   

  1. 1 Department of Applied Physics, College of Electronic Engineering, South China Agricultural University, Guangzhou 510642, China;
    2 State Key Laboratory of Silicon and Advanced Semiconductor Materials, School of Materials Science and Engineering, Zhejiang University, Hangzhou 310027, China;
    3 Materials Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
  • 收稿日期:2024-10-01 修回日期:2024-11-01 接受日期:2024-12-09 发布日期:2024-12-31
  • 通讯作者: Chuanhong Jin, Shanggang Lin, Fang Lin E-mail:chhjin@zju.edu.cn;shglin@scau.edu.cn;linfang@scau.edu.cn
  • 基金资助:
    F. Lin acknowledges financial support from the National Natural Science Foundation of China (Grant No. 61971201).

Combining machine learning algorithms with traditional methods for resolving the atomic-scale dynamic structure of monolayer MoS2 in high-resolution transmission electron microscopy

Yu Meng(蒙宇)1,†, Shuya Wang(王淑雅)1,†, Xibiao Ren(任锡标)2, Han Xue(薛涵)3, Xuejun Yue(岳学军)1, Chuanhong Jin(金传洪)2,‡, Shanggang Lin(林上港)1,†, and Fang Lin(林芳)1,¶   

  1. 1 Department of Applied Physics, College of Electronic Engineering, South China Agricultural University, Guangzhou 510642, China;
    2 State Key Laboratory of Silicon and Advanced Semiconductor Materials, School of Materials Science and Engineering, Zhejiang University, Hangzhou 310027, China;
    3 Materials Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
  • Received:2024-10-01 Revised:2024-11-01 Accepted:2024-12-09 Published:2024-12-31
  • Contact: Chuanhong Jin, Shanggang Lin, Fang Lin E-mail:chhjin@zju.edu.cn;shglin@scau.edu.cn;linfang@scau.edu.cn
  • Supported by:
    F. Lin acknowledges financial support from the National Natural Science Foundation of China (Grant No. 61971201).

摘要: High-resolution transmission electron microscopy (HRTEM) promises rapid atomic-scale dynamic structure imaging. Yet, the precision limitations of aberration parameters and the challenge of eliminating aberrations in $Cs$-corrected transmission electron microscopy constrain resolution. A machine learning algorithm is developed to determine the aberration parameters with higher precision from small, lattice-periodic crystal images. The proposed algorithm is then validated with simulated HRTEM images of graphene and applied to the experimental images of a molybdenum disulfide (MoS$_{2}$) monolayer with 25 variables (14 aberrations) resolved in wide ranges. Using these measured parameters, the phases of the exit-wave functions are reconstructed for each image in a focal series of MoS$_{2}$ monolayers. The images were acquired due to the unexpected movement of the specimen holder. Four-dimensional data extraction reveals time-varying atomic structures and ripple. In particular, the atomic evolution of the sulfur-vacancy point and line defects, as well as the edge structure near the amorphous, is visualized as the resolution has been improved from about 1.75 Å to 0.9 Å. This method can help salvage important transmission electron microscope images and is beneficial for the images obtained from electron microscopes with average stability.

关键词: aberration measurement, high-resolution transmission electron microscopy, feature-extraction networks, exit-wave reconstruction, monolayer MoS$_{2}$

Abstract: High-resolution transmission electron microscopy (HRTEM) promises rapid atomic-scale dynamic structure imaging. Yet, the precision limitations of aberration parameters and the challenge of eliminating aberrations in $Cs$-corrected transmission electron microscopy constrain resolution. A machine learning algorithm is developed to determine the aberration parameters with higher precision from small, lattice-periodic crystal images. The proposed algorithm is then validated with simulated HRTEM images of graphene and applied to the experimental images of a molybdenum disulfide (MoS$_{2}$) monolayer with 25 variables (14 aberrations) resolved in wide ranges. Using these measured parameters, the phases of the exit-wave functions are reconstructed for each image in a focal series of MoS$_{2}$ monolayers. The images were acquired due to the unexpected movement of the specimen holder. Four-dimensional data extraction reveals time-varying atomic structures and ripple. In particular, the atomic evolution of the sulfur-vacancy point and line defects, as well as the edge structure near the amorphous, is visualized as the resolution has been improved from about 1.75 Å to 0.9 Å. This method can help salvage important transmission electron microscope images and is beneficial for the images obtained from electron microscopes with average stability.

Key words: aberration measurement, high-resolution transmission electron microscopy, feature-extraction networks, exit-wave reconstruction, monolayer MoS$_{2}$

中图分类号:  (High-resolution transmission electron microscopy (HRTEM))

  • 68.37.Og
43.60.Lq (Acoustic imaging, displays, pattern recognition, feature extraction) 43.60.Tj (Wave front reconstruction, acoustic time-reversal, and phase conjugation) 81.07.-b (Nanoscale materials and structures: fabrication and characterization) 42.15.Fr (Aberrations)