Artificial Neuron Networks Enabled Identification and Characterizations of 2D Materials and van der Waals Heterostructures

doi:10.1021/acsnano.1c09644

IMR OpenIR

	Artificial Neuron Networks Enabled Identification and Characterizations of 2D Materials and van der Waals Heterostructures
	Zhu, Li 1,2; Tang, Jing 1,2; Li, Baichang 3; Hou, Tianyu 1,2; Zhu, Yong 4; Zhou, Jiadong 5,6; Wang, Zhi 7,8; Zhu, Xiaorong 9; Yao, Zhenpeng 10,11; Cui, Xu 12; Watanabe, Kenji 13; Taniguchi, Takashi 14; Li, Yafei 9; Han, Zheng Vitto 15,16; Zhou, Wu 4; Huang, Yuan 17; Liu, Zheng 18; Hone, James C.3; Hao, Yufeng 1,2
通讯作者	Hao, Yufeng(haoyufeng@nju.edu.cn)
	2022-01-18
发表期刊	ACS NANO
ISSN	1936-0851
页码	9
摘要	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.
关键词	two-dimensional materials arti fi cial neuron networks fast characterization defect concentration heterostructures
资助者	National 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
DOI	10.1021/acsnano.1c09644
收录类别	SCI
语种	英语
资助项目	National 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研究方向	Chemistry ; Science & Technology - Other Topics ; Materials Science
WOS类目	Chemistry, Multidisciplinary ; Chemistry, Physical ; Nanoscience & Nanotechnology ; Materials Science, Multidisciplinary
WOS记录号	WOS:000745265500001
出版者	AMER CHEMICAL SOC
引用统计	被引频次：24[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.imr.ac.cn/handle/321006/173698
专题	中国科学院金属研究所
通讯作者	Hao, Yufeng
作者单位	1.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
推荐引用方式 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.