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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 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 |
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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.
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Received: 01 October 2024
Revised: 01 November 2024
Accepted manuscript online: 09 December 2024
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PACS:
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68.37.Og
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(High-resolution transmission electron microscopy (HRTEM))
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43.60.Lq
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(Acoustic imaging, displays, pattern recognition, feature extraction)
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43.60.Tj
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(Wave front reconstruction, acoustic time-reversal, and phase conjugation)
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81.07.-b
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(Nanoscale materials and structures: fabrication and characterization)
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42.15.Fr
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(Aberrations)
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| Fund: F. Lin acknowledges financial support from the National Natural Science Foundation of China (Grant No. 61971201). |
Corresponding Authors:
Chuanhong Jin, Shanggang Lin, Fang Lin
E-mail: chhjin@zju.edu.cn;shglin@scau.edu.cn;linfang@scau.edu.cn
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Cite this article:
Yu Meng(蒙宇), Shuya Wang(王淑雅), Xibiao Ren(任锡标), Han Xue(薛涵), Xuejun Yue(岳学军), Chuanhong Jin(金传洪), Shanggang Lin(林上港), and Fang Lin(林芳) Combining machine learning algorithms with traditional methods for resolving the atomic-scale dynamic structure of monolayer MoS2 in high-resolution transmission electron microscopy 2025 Chin. Phys. B 34 016802
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