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Artificial Neuron Networks Enabled Identification and Characterizations of 2D Materials and van der Waals Heterostructures
Zhu, Li1,2; Tang, Jing1,2; Li, Baichang3; Hou, Tianyu1,2; Zhu, Yong4; Zhou, Jiadong5,6; Wang, Zhi7,8; Zhu, Xiaorong9; Yao, Zhenpeng10,11; Cui, Xu12; Watanabe, Kenji13; Taniguchi, Takashi14; Li, Yafei9; Han, Zheng Vitto15,16; Zhou, Wu4; Huang, Yuan17; Liu, Zheng18; Hone, James C.3; Hao, Yufeng1,2
Corresponding AuthorHao, Yufeng(haoyufeng@nju.edu.cn)
2022-01-18
Source PublicationACS NANO
ISSN1936-0851
Pages9
AbstractTwo-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.
Keywordtwo-dimensional materials arti fi cial neuron networks fast characterization defect concentration heterostructures
Funding OrganizationNational Key R&D Program of China ; National Natural Science Foundation of China ; Natural Science Foundation of Jiangsu Province ; 333 high level talent training project of Jiangsu, and JiangHai talent program of Nantong ; Elemental Strategy Initiative ; JSPS KAKENHI
DOI10.1021/acsnano.1c09644
Indexed BySCI
Language英语
Funding ProjectNational Key R&D Program of China[2018YFA0305800] ; National Natural Science Foundation of China[51772145] ; Natural Science Foundation of Jiangsu Province[BK20180003] ; 333 high level talent training project of Jiangsu, and JiangHai talent program of Nantong ; Elemental Strategy Initiative[JPMXP0112101001] ; JSPS KAKENHI[JP19H05790] ; JSPS KAKENHI[JP20H00354]
WOS Research AreaChemistry ; Science & Technology - Other Topics ; Materials Science
WOS SubjectChemistry, Multidisciplinary ; Chemistry, Physical ; Nanoscience & Nanotechnology ; Materials Science, Multidisciplinary
WOS IDWOS:000745265500001
PublisherAMER CHEMICAL SOC
Citation statistics
Document Type期刊论文
Identifierhttp://ir.imr.ac.cn/handle/321006/173698
Collection中国科学院金属研究所
Corresponding AuthorHao, Yufeng
Affiliation1.Nanjing Univ, Jiangsu Key Lab Artificial Funct Mat, Coll Engn & Appl Sci, Natl Lab Solid State Microstruct, Nanjing 210023, Peoples R China
2.Nanjing Univ, Collaborat Innovat Ctr Adv Microstruct, Nanjing 210023, Peoples R China
3.Columbia Univ, Dept Mech Engn, New York, NY 10027 USA
4.Univ Chinese Acad Sci, Sch Phys Sci, Beijing 100049, Peoples R China
5.Beijing Inst Technol, Beijing Key Lab Nanophoton & Ultrafine Optoelect, Beijing 100081, Peoples R China
6.Beijing Inst Technol, Sch Phys, Beijing 100081, Peoples R China
7.Chinese Acad Sci, Shenyang Natl Lab Mat Sci, Inst Met Res, Shenyang 110016, Peoples R China
8.Univ Sci & Technol, Sch Mat Sci & Engn, Hefei 230026, Anhui, Peoples R China
9.Nanjing Normal Univ, Coll Chem & Mat Sci, Jiangsu Key Lab Biofunct Mat, Nanjing 210023, Peoples R China
10.Shanghai Jiao Tong Univ, Sch Mat Sci & Engn, State Key Lab Met Matrix Composites, Shanghai 200240, Peoples R China
11.Shanghai Jiao Tong Univ, Ctr Hydrogen Sci, Shanghai 200240, Peoples R China
12.AutoX Technol Inc, San Jose, CA 95131 USA
13.Natl Inst Mat Sci, Funct Mat Res Ctr, Tsukuba, Ibaraki 3050044, Japan
14.Natl Inst Mat Sci, Int Ctr Mat Nanoarchitecton, Tsukuba, Ibaraki 3050044, Japan
15.Shanxi Univ, State Key Lab Quantum Opt & Quantum Opt Devices, Inst Optoelect, Taiyuan 030006, Peoples R China
16.Shanxi Univ, Collaborat Innovat Ctr Extreme Opt, Taiyuan 030006, Peoples R China
17.Beijing Inst Technol, Adv Res Inst Multidisciplinary Sci, Beijing 100081, Peoples R China
18.Nanyang Technol Univ, Sch Mat Sci & Engn, Ctr Programmed Mat, Singapore 639798, Singapore
Recommended Citation
GB/T 7714
Zhu, Li,Tang, Jing,Li, Baichang,et al. Artificial Neuron Networks Enabled Identification and Characterizations of 2D Materials and van der Waals Heterostructures[J]. ACS NANO,2022:9.
APA Zhu, Li.,Tang, Jing.,Li, Baichang.,Hou, Tianyu.,Zhu, Yong.,...&Hao, Yufeng.(2022).Artificial Neuron Networks Enabled Identification and Characterizations of 2D Materials and van der Waals Heterostructures.ACS NANO,9.
MLA Zhu, Li,et al."Artificial Neuron Networks Enabled Identification and Characterizations of 2D Materials and van der Waals Heterostructures".ACS NANO (2022):9.
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