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Artificial Neuron Networks Enabled Identification and Characterizations of 2D Materials and van der Waals Heterostructures.
Zhu, Li; Tang, Jing; Li, Baichang; Hou, Tianyu; Zhu, Yong; Zhou, Jiadong; Wang, Zhi; Zhu, Xiaorong; Yao, Zhenpeng; Cui, Xu; Watanabe, Kenji; Taniguchi, Takashi; Li, Yafei; Han, Zheng Vitto; Zhou, Wu; Huang, Yuan; Liu, Zheng; Hone, James C; Hao, Yufeng.
Affiliation
  • Zhu L; National Laboratory of Solid State Microstructures, College of Engineering and Applied Sciences, Jiangsu Key Laboratory of Artificial Functional Materials, and Collaborative Innovation Center of Advanced Microstructures, Nanjing University, Nanjing 210023, China.
  • Tang J; National Laboratory of Solid State Microstructures, College of Engineering and Applied Sciences, Jiangsu Key Laboratory of Artificial Functional Materials, and Collaborative Innovation Center of Advanced Microstructures, Nanjing University, Nanjing 210023, China.
  • Li B; Department of Mechanical Engineering, Columbia University, New York, New York 10027, United States.
  • Hou T; National Laboratory of Solid State Microstructures, College of Engineering and Applied Sciences, Jiangsu Key Laboratory of Artificial Functional Materials, and Collaborative Innovation Center of Advanced Microstructures, Nanjing University, Nanjing 210023, China.
  • Zhu Y; School of Physical Sciences, University of Chinese Academy of Sciences, Beijing 100049 China.
  • Zhou J; Beijing Key Lab of Nanophotonics & Ultrafine Optoelectronic Systems and School of Physics, Beijing Institute of Technology, Beijing 100081, China.
  • Wang Z; Shenyang National Laboratory for Materials Science, Institute of Metal Research, Chinese Academy of Sciences, Shenyang 110016, China.
  • Zhu X; School of Material Science and Engineering, University of Science and Technology of China, Anhui 230026, China.
  • Yao Z; Jiangsu Key Laboratory of Biofunctional Materials, College of Chemistry and Materials Science, Nanjing Normal University, Nanjing 210023, China.
  • Cui X; The State Key Laboratory of Metal Matrix Composites, School of Materials Science and Engineering, and Center of Hydrogen Science, Shanghai Jiao Tong University, Shanghai 200240, China.
  • Watanabe K; AutoX Technologies, Inc., San Jose, California 95131, United States.
  • Taniguchi T; Research Center for Functional Materials, National Institute for Materials Science, 1-1 Namiki, Tsukuba 305-0044, Japan.
  • Li Y; International Center for Materials Nanoarchitectonics, National Institute for Materials Science, 1-1 Namiki, Tsukuba 305-0044, Japan.
  • Han ZV; Jiangsu Key Laboratory of Biofunctional Materials, College of Chemistry and Materials Science, Nanjing Normal University, Nanjing 210023, China.
  • Zhou W; State Key Laboratory of Quantum Optics and Quantum Optics Devices, Institute of Opto-Electronics, Shanxi University, Taiyuan 030006, China.
  • Huang Y; Collaborative Innovation Center of Extreme Optics, Shanxi University, Taiyuan 030006, China.
  • Liu Z; School of Physical Sciences, University of Chinese Academy of Sciences, Beijing 100049 China.
  • Hone JC; Advanced Research Institute of Multidisciplinary Science, Beijing Institute of Technology, Beijing 100081, China.
  • Hao Y; Centre for Programmed Materials, School of Materials Science and Engineering, Nanyang Technological University, 639798 Singapore.
ACS Nano ; 16(2): 2721-2729, 2022 Feb 22.
Article in En | MEDLINE | ID: mdl-35040630
Two-dimensional (2D) materials and their in-plane and out-of-plane (i.e., van der Waals, vdW) heterostructures are promising building blocks for next-generation electronic and optoelectronic devices. Since the performance of the devices is strongly dependent on the crystalline quality of the materials and the interface characteristics of the heterostructures, a fast and nondestructive method for distinguishing and characterizing various 2D building blocks is desirable to promote the device integrations. In this work, based on the color space information on 2D materials' optical microscopy images, an artificial neural network-based deep learning algorithm is developed and applied to identify eight kinds of 2D materials with accuracy well above 90% and a mean value of 96%. More importantly, this data-driven method enables two interesting functionalities: (1) resolving the interface distribution of chemical vapor deposition (CVD) grown in-plane and vdW heterostructures and (2) identifying defect concentrations of CVD-grown 2D semiconductors. The two functionalities can be utilized to quickly identify sample quality and optimize synthesis parameters in the future. Our work improves the characterization efficiency of atomically thin materials and is therefore valuable for their research and applications.
Key words

Full text: 1 Collection: 01-internacional Database: MEDLINE Type of study: Diagnostic_studies / Prognostic_studies Language: En Journal: ACS Nano Year: 2022 Document type: Article

Full text: 1 Collection: 01-internacional Database: MEDLINE Type of study: Diagnostic_studies / Prognostic_studies Language: En Journal: ACS Nano Year: 2022 Document type: Article