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Research Article

Intelligent identification of two-dimensional nanostructures by machine-learning optical microscopy

Xiaoyang Lin1,2,§( ), Zhizhong Si1,§, Wenzhi Fu3,§, Jianlei Yang3,§, Side Guo1, Yuan Cao1, Jin Zhang4, Xinhe Wang1,4, Peng Liu4, Kaili Jiang4, Weisheng Zhao1,2( )
Fert Beijing Research InstituteSchool of Microelectronics & Beijing Advanced Innovation Center for Big Data and Brain Computing (BDBC)Beihang UniversityBeijing100191China
Beihang-Goertek Joint Microelectronics InstituteQingdao Research InstituteBeihang UniversityQingdao266000China
Fert Beijing Research InstituteSchool of Computer Science and Engineering & Beijing Advanced Innovation Center for Big Data and Brain Computing (BDBC)Beihang UniversityBeijing100191China
State Key Laboratory of Low-Dimensional Quantum PhysicsDepartment of Physics & Tsinghua-Foxconn Nanotechnology Research CenterCollaborative Innovation Center of Quantum MatterTsinghua UniversityBeijing100084China

§Xiaoyang Lin, Zhizhong Si, Wenzhi Fu, and Jianlei Yang contributed equally to this work.

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Abstract

Two-dimensional (2D) materials and their heterostructures, with wafer-scale synthesis methods and fascinating properties, have attracted significant interest and triggered revolutions in corresponding device applications. However, facile methods to realize accurate, intelligent, and large-area characterizations of these 2D nanostructures are still highly desired. Herein, we report the successful application of machine-learning strategy in the optical identification of 2D nanostructures. The machine-learning optical identification (MOI) method endows optical microscopy with intelligent insight into the characteristic color information of 2D nanostructures in the optical photograph. The experimental results indicate that the MOI method enables accurate, intelligent, and large-area characterizations of graphene, molybdenum disulfide, and their heterostructures, including identifications of the thickness, existence of impurities, and even stacking order. With the convergence of artificial intelligence and nanoscience, this intelligent identification method can certainly promote fundamental research and wafer-scale device applications of 2D nanostructures.

Keywords

machine-learning optical identificationtwo-dimensional (2D) materialheterostructureartificial intelligence

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References

1

Das Sarma, S.; Adam, S.; Hwang, E. H.; Rossi, E. Electronic transport in two-dimensional graphene. Rev. Mod. Phys. 2011, 83, 407–470.
Crossref Google Scholar
2

Geim, A. K.; Novoselov, K. S. The rise of graphene. Nat. Mater. 2007, 6, 183–191.
Crossref Google Scholar
3

Lin, X. Y.; Su, L.; Si, Z. Z.; Zhang, Y. G.; Bournel, A.; Zhang, Y.; Klein, J. O.; Fert, A.; Zhao, W. S. Gate-driven pure spin current in graphene. Phys. Rev. Appl. 2017, 8, 034006.
Crossref Google Scholar
4

Novoselov, K. S.; Mishchenko, A.; Carvalho, A.; Castro Neto, A. H. 2D materials and van der Waals heterostructures. Science 2016, 353, aac9439.
Crossref Google Scholar
5

Xia, F. N.; Wang, H.; Xiao, D.; Dubey, M.; Ramasubramaniam, A. Two-dimensional material nanophotonics. Nat. Photonics 2014, 8, 899–907.
Crossref Google Scholar
6

Geim, A. K.; Grigorieva, I. V. Van der Waals heterostructures. Nature 2013, 499, 419–425.
Crossref Google Scholar
7

Zhang, Z. W.; Chen, P.; Duan, X. D.; Zang, K. T.; Luo, J.; Duan, X. F. Robust epitaxial growth of two-dimensional heterostructures, multiheterostructures, and superlattices. Science 2017, 357, 788–792.
Crossref Google Scholar
8

Wu, T. R.; Zhang, X. F.; Yuan, Q. H.; Xue, J. C.; Lu, G. Y.; Liu, Z. H.; Wang, H. S.; Wang, H. M.; Ding, F.; Yu, Q. K. et al. Fast growth of inch-sized single-crystalline graphene from a controlled single nucleus on Cu-Ni alloys. Nat. Mater. 2015, 15, 43–47.
Crossref Google Scholar
9

Xu, X. Z.; Zhang, Z. H.; Dong, J. C.; Yi, D.; Niu, J. J.; Wu, M. H.; Lin, L.; Yin, R. K.; Li, M. Q.; Zhou, J. Y. et al. Ultrafast epitaxial growth of metre-sized single-crystal graphene on industrial Cu foil. Sci. Bull. 2017, 62, 1074–1080.
Crossref Google Scholar
10

Lee, J. H.; Lee, E. K.; Joo, W. J.; Jang, Y.; Kim, B. S.; Lim, J. Y.; Choi, S. H.; Ahn, S. J.; Ahn, J. R.; Park, M. H. et al. Wafer-scale growth of single-crystal monolayer graphene on reusable hydrogen-terminated germanium. Science 2014, 344, 286–289.
Crossref Google Scholar
11

Huang, P. Y.; Ruiz-Vargas, C. S.; van der Zande, A. M.; Whitney, W. S.; Levendorf, M. P.; Kevek, J. W.; Garg, S.; Alden, J. S.; Hustedt, C. J.; Zhu, Y. et al. Grains and grain boundaries in single-layer graphene atomic patchwork quilts. Nature 2011, 469, 389–392.
Crossref Google Scholar
12

Meyer, J. C.; Geim, A. K.; Katsnelson, M. I.; Novoselov, K. S.; Booth, T. J.; Roth, S. The structure of suspended graphene sheets. Nature 2007, 446, 60–63.
Crossref Google Scholar
13

Zhao, W.; Xia, B. Y.; Lin, L.; Xiao, X. Y.; Liu, P.; Lin, X. Y.; Peng, H. L.; Zhu, Y. M.; Yu, R.; Lei, P. et al. Low-energy transmission electron diffraction and imaging of large-area graphene. Sci. Adv. 2017, 3, e1603231.
Crossref Google Scholar
14

Zhang, Y. B.; Tang, T. T.; Girit, C.; Hao, Z.; Martin, M. C.; Zettl, A.; Crommie, M. F.; Shen, Y. R.; Wang, F. Direct observation of a widely tunable bandgap in bilayer graphene. Nature 2009, 459, 820–823.
Crossref Google Scholar
15

Wastl, D. S.; Weymouth, A. J.; Giessibl, F. J. Atomically resolved graphitic surfaces in air by atomic force microscopy. ACS Nano 2014, 8, 5233–5239.
Crossref Google Scholar
16

Tu, Q.; Lange, B.; Parlak, Z.; Lopes, J. M. J.; Blum, V.; Zauscher, S. Quantitative subsurface atomic structure fingerprint for 2D materials and heterostructures by firstprinciples-calibrated contact-resonance atomic force microscopy. ACS Nano 2016, 10, 6491–6500.
Crossref Google Scholar
17

Ferrari, A. C.; Basko, D. M. Raman spectroscopy as a versatile tool for studying the properties of graphene. Nat. Nanotechnol. 2013, 8, 235–246.
Crossref Google Scholar
18

Duong, D. L.; Han, G. H.; Lee, S. M.; Gunes, F.; Kim, E. S.; Kim, S. T.; Kim, H.; Ta, Q. H.; So, K. P.; Yoon, S. J. et al. Probing graphene grain boundaries with optical microscopy. Nature 2012, 490, 235–239.
Crossref Google Scholar
19

Li, H.; Wu, J.; Huang, X.; Lu, G.; Yang, J.; Lu, X.; Xiong, Q. H.; Zhang, H. Rapid and reliable thickness identification of two-dimensional nanosheets using optical microscopy. ACS Nano 2013, 7, 10344–10353.
Crossref Google Scholar
20

Li, W.; Moon, S.; Wojcik, M.; Xu, K. Direct optical visualization of graphene and its nanoscale defects on transparent substrates. Nano Lett. 2016, 16, 5027–5031.
Crossref Google Scholar
21

Blake, P.; Hill, E. W.; Neto, A. H. C.; Novoselov, K. S.; Jiang, D.; Yang, R.; Booth, T. J.; Geim, A. K. Making graphene visible. Appl. Phys. Lett. 2007, 91, 063124.
Crossref Google Scholar
22

Ni, Z. H.; Wang, H. M.; Kasim, J.; Fan, H. M.; Yu, T.; Wu, Y. H.; Feng, Y. P.; Shen, Z. X. Graphene thickness determination using reflection and contrast spectroscopy. Nano Lett. 2007, 7, 2758–2763.
Crossref Google Scholar
23

Li, X. S.; Cai, W. W.; An, J. H.; Kim, S.; Nah, J.; Yang, D. X.; Piner, R.; Velamakanni, A.; Jung, I.; Tutuc, E. et al. Large-area synthesis of high-quality and uniform graphene films on copper foils. Science 2009, 324, 1312–1314.
Crossref Google Scholar
24

Reina, A.; Jia, X. T.; Ho, J.; Nezich, D.; Son, H.; Bulovic, V.; Dresselhaus, M. S.; Kong, J. Large area, few-layer graphene films on arbitrary substrates by chemical vapor deposition. Nano Lett. 2009, 9, 30–35.
Crossref Google Scholar
25

Lee, Y.; Lee, J.; Bark, H.; Oh, I.; Ryu, G. H.; Lee, Z.; Kim, H.; Cho, J. H.; Ahn, J.; Lee, C. Synthesis of wafer-scale uniform molybdenum disulfide films with control over the layer number using a gas phase sulfur precursor. Nanoscale 2014, 6, 2821–2826.
Crossref Google Scholar
26

Zhao, M.; Ye, Y.; Han, Y. M.; Xia, Y.; Zhu, H. Y.; Wang, S. Q.; Wang, Y.; Muller, D. A.; Zhang, X. Large-scale chemical assembly of atomically thin transistors and circuits. Nat. Nanotechnol. 2016, 11, 954–959.
Crossref Google Scholar
27

Alexeev, E. M.; Catanzaro, A.; Skrypka, O. V.; Nayak, P. K.; Ahn, S.; Pak, S.; Lee, J.; Sohn, J. I.; Novoselov, K. S.; Shin, H. S. et al. Imaging of interlayer coupling in van der Waals heterostructures using a bright-field optical microscope. Nano Lett. 2017, 17, 5342–5349.
Crossref Google Scholar
28

Tan, Y.; Liu, X. B.; He, Z. L.; Liu, Y. R.; Zhao, M. W.; Zhang, H.; Chen, F. Tuning of interlayer coupling in largearea graphene/WSe2 van der Waals heterostructure via ion irradiation: Optical evidences and photonic applications. ACS Photonics 2017, 4, 1531–1538.
Crossref Google Scholar
29

Nolen, C. M.; Denina, G.; Teweldebrhan, D.; Bhanu, B.; Balandin, A. A. High-throughput large-area automated identification and quality control of graphene and few-layer graphene films. ACS Nano 2011, 5, 914–922.
Crossref Google Scholar
30

Maxmen, A. Deep learning sharpens views of cells and genes. Nature 2018, 553, 9–10.
Crossref Google Scholar
31

Christmann, A.; Steinwart, I. Support Vector Machines; Springer-Verlag: New York, 2008; pp 613.
32

Castellanos-Gomez, A.; Agraït, N.; Rubio-Bollinger, G. Optical identification of atomically thin dichalcogenide crystals. Appl. Phys. Lett. 2010, 96, 213116.
Crossref Google Scholar
33

Zhu, F.; Lin, X. Y.; Liu, P.; Jiang, K. L.; Wei, Y.; Wu, Y.; Wang, J. P.; Fan, S. S. Heating graphene to incandescence and the measurement of its work function by thermionic emission method. Nano Res. 2014, 7, 553–560.
Crossref Google Scholar
34

Castro Neto, A. H.; Guinea, F.; Peres, N. M. R.; Novoselov, K. S.; Geim, A. K. The electronic properties of graphene. Rev. Mod. Phys. 2009, 81, 109–162.
Crossref Google Scholar
35

Lu, Z. X.; Sun, L. F.; Xu, G. C.; Zheng, J. Y.; Zhang, Q.; Wang, J. Y.; Jiao, L. Y. Universal transfer and stacking of chemical vapor deposition grown two-dimensional atomic layers with water-soluble polymer mediator. ACS Nano 2016, 10, 5237–5242.
Crossref Google Scholar
36

Knittl, Z. Optics of Thin Films: An Optical Multilayer Theory; Wiley: London, 1976; pp 548.
37

Zhang, H.; Ma, Y. G.; Wan, Y.; Rong, X.; Xie, Z. A.; Wang, W.; Dai, L. Measuring the refractive index of highly crystalline monolayer MoS2 with high confidence. Sci. Rep. 2015, 5, 8440.
Crossref Google Scholar
38

Palik, E. D. Handbook of Optical Constants of Solids; Elsevier: Amsterdam, 1997.
39

Masubuchi, S.; Morimoto, M.; Morikawa, S.; Onodera, M.; Asakawa, Y.; Watanabe, K.; Taniguchi, T.; Machida, T. Autonomous robotic searching and assembly of two-dimensional crystals to build van der Waals superlattices. Nat. Commun. 2018, 9, 1413.
Crossref Google Scholar
40

Novoselov, K. S.; Geim, A. K.; Morozov, S. V.; Jiang, D.; Zhang, Y.; Dubonos, S. V.; Grigorieva, I. V.; Firsov, A. A. Electric field effect in atomically thin carbon films. Science 2004, 306, 666–669.
Crossref Google Scholar
Nano Research
Volume 11 Issue 12,
December 2018
Pages 6316-6324
DOI: 10.1007/s12274-018-2155-0
Cite this article:
Lin X, Si Z, Fu W, et al. Intelligent identification of two-dimensional nanostructures by machine-learning optical microscopy. Nano Research, 2018, 11(12): 6316-6324. https://doi.org/10.1007/s12274-018-2155-0

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Received: 14 April 2018
Revised: 20 July 2018
Accepted: 23 July 2018
Published: 07 August 2018
© Tsinghua University Press and Springer-Verlag GmbH Germany, part of Springer Nature 2018
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