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Lu B, Xia Y, Ren Y, Xie M, Zhou L, Vinai G, Morton SA, Wee ATS, van der Wiel WG, Zhang W, Wong PKJ. When Machine Learning Meets 2D Materials: A Review. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2024; 11:e2305277. [PMID: 38279508 PMCID: PMC10987159 DOI: 10.1002/advs.202305277] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/31/2023] [Revised: 10/21/2023] [Indexed: 01/28/2024]
Abstract
The availability of an ever-expanding portfolio of 2D materials with rich internal degrees of freedom (spin, excitonic, valley, sublattice, and layer pseudospin) together with the unique ability to tailor heterostructures made layer by layer in a precisely chosen stacking sequence and relative crystallographic alignments, offers an unprecedented platform for realizing materials by design. However, the breadth of multi-dimensional parameter space and massive data sets involved is emblematic of complex, resource-intensive experimentation, which not only challenges the current state of the art but also renders exhaustive sampling untenable. To this end, machine learning, a very powerful data-driven approach and subset of artificial intelligence, is a potential game-changer, enabling a cheaper - yet more efficient - alternative to traditional computational strategies. It is also a new paradigm for autonomous experimentation for accelerated discovery and machine-assisted design of functional 2D materials and heterostructures. Here, the study reviews the recent progress and challenges of such endeavors, and highlight various emerging opportunities in this frontier research area.
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Affiliation(s)
- Bin Lu
- ARTIST Lab for Artificial Electronic Materials and Technologies, School of MicroelectronicsNorthwestern Polytechnical UniversityXi'an710072P. R. China
- Yangtze River Delta Research Institute of Northwestern Polytechnical UniversityTaicang215400P. R. China
| | - Yuze Xia
- ARTIST Lab for Artificial Electronic Materials and Technologies, School of MicroelectronicsNorthwestern Polytechnical UniversityXi'an710072P. R. China
- Yangtze River Delta Research Institute of Northwestern Polytechnical UniversityTaicang215400P. R. China
| | - Yuqian Ren
- ARTIST Lab for Artificial Electronic Materials and Technologies, School of MicroelectronicsNorthwestern Polytechnical UniversityXi'an710072P. R. China
- Yangtze River Delta Research Institute of Northwestern Polytechnical UniversityTaicang215400P. R. China
| | - Miaomiao Xie
- ARTIST Lab for Artificial Electronic Materials and Technologies, School of MicroelectronicsNorthwestern Polytechnical UniversityXi'an710072P. R. China
- Yangtze River Delta Research Institute of Northwestern Polytechnical UniversityTaicang215400P. R. China
| | - Liguo Zhou
- ARTIST Lab for Artificial Electronic Materials and Technologies, School of MicroelectronicsNorthwestern Polytechnical UniversityXi'an710072P. R. China
- Yangtze River Delta Research Institute of Northwestern Polytechnical UniversityTaicang215400P. R. China
| | - Giovanni Vinai
- Instituto Officina dei Materiali (IOM)‐CNRLaboratorio TASCTriesteI‐34149Italy
| | - Simon A. Morton
- Advanced Light Source (ALS)Lawrence Berkeley National LaboratoryBerkeleyCA94720USA
| | - Andrew T. S. Wee
- Department of Physics and Centre for Advanced 2D Materials (CA2DM) and Graphene Research Centre (GRC)National University of SingaporeSingapore117542Singapore
| | - Wilfred G. van der Wiel
- NanoElectronics Group, MESA+ Institute for Nanotechnology and BRAINS Center for Brain‐Inspired Nano SystemsUniversity of TwenteEnschede7500AEThe Netherlands
- Institute of PhysicsUniversity of Münster48149MünsterGermany
| | - Wen Zhang
- ARTIST Lab for Artificial Electronic Materials and Technologies, School of MicroelectronicsNorthwestern Polytechnical UniversityXi'an710072P. R. China
- Yangtze River Delta Research Institute of Northwestern Polytechnical UniversityTaicang215400P. R. China
- NanoElectronics Group, MESA+ Institute for Nanotechnology and BRAINS Center for Brain‐Inspired Nano SystemsUniversity of TwenteEnschede7500AEThe Netherlands
| | - Ping Kwan Johnny Wong
- ARTIST Lab for Artificial Electronic Materials and Technologies, School of MicroelectronicsNorthwestern Polytechnical UniversityXi'an710072P. R. China
- Yangtze River Delta Research Institute of Northwestern Polytechnical UniversityTaicang215400P. R. China
- NPU Chongqing Technology Innovation CenterChongqing400000P. R. China
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Yang H, Hu R, Wu H, He X, Zhou Y, Xue Y, He K, Hu W, Chen H, Gong M, Zhang X, Tan PH, Hernández ER, Xie Y. Identification and Structural Characterization of Twisted Atomically Thin Bilayer Materials by Deep Learning. NANO LETTERS 2024; 24:2789-2797. [PMID: 38407030 PMCID: PMC10921996 DOI: 10.1021/acs.nanolett.3c04815] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/07/2023] [Revised: 02/19/2024] [Accepted: 02/20/2024] [Indexed: 02/27/2024]
Abstract
Two-dimensional materials are expected to play an important role in next-generation electronics and optoelectronic devices. Recently, twisted bilayer graphene and transition metal dichalcogenides have attracted significant attention due to their unique physical properties and potential applications. In this study, we describe the use of optical microscopy to collect the color space of chemical vapor deposition (CVD) of molybdenum disulfide (MoS2) and the application of a semantic segmentation convolutional neural network (CNN) to accurately and rapidly identify thicknesses of MoS2 flakes. A second CNN model is trained to provide precise predictions on the twist angle of CVD-grown bilayer flakes. This model harnessed a data set comprising over 10,000 synthetic images, encompassing geometries spanning from hexagonal to triangular shapes. Subsequent validation of the deep learning predictions on twist angles was executed through the second harmonic generation and Raman spectroscopy. Our results introduce a scalable methodology for automated inspection of twisted atomically thin CVD-grown bilayers.
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Affiliation(s)
- Haitao Yang
- Key
Laboratory of Wide Band-Gap Semiconductor Technology & Shaanxi
Key Laboratory of High-Orbits-Electron Materials and Protection Technology
for Aerospace, School of Advanced Materials and Nanotechnology, Xidian University, Xi’an 710071, China
| | - Ruiqi Hu
- Department
of Materials Science and Engineering, University
of Delaware, Newark, Delaware 19716, United States
| | - Heng Wu
- State
Key Laboratory of Superlattices and Microstructures, Institute of Semiconductors, Chinese Academy of Sciences, Beijing 100083, China
| | - Xiaolong He
- Key
Laboratory of Wide Band-Gap Semiconductor Technology & Shaanxi
Key Laboratory of High-Orbits-Electron Materials and Protection Technology
for Aerospace, School of Advanced Materials and Nanotechnology, Xidian University, Xi’an 710071, China
| | - Yan Zhou
- State
Key Laboratory of Superlattices and Microstructures, Institute of Semiconductors, Chinese Academy of Sciences, Beijing 100083, China
- Phonon
Engineering Research Center of Jiangsu Province, School of Physics
and Technology, Nanjing Normal University, Nanjing 210023, China
| | - Yizhe Xue
- Key
Laboratory of Wide Band-Gap Semiconductor Technology & Shaanxi
Key Laboratory of High-Orbits-Electron Materials and Protection Technology
for Aerospace, School of Advanced Materials and Nanotechnology, Xidian University, Xi’an 710071, China
| | - Kexin He
- Key
Laboratory of Wide Band-Gap Semiconductor Technology & Shaanxi
Key Laboratory of High-Orbits-Electron Materials and Protection Technology
for Aerospace, School of Advanced Materials and Nanotechnology, Xidian University, Xi’an 710071, China
| | - Wenshuai Hu
- Key
Laboratory of Wide Band-Gap Semiconductor Technology & Shaanxi
Key Laboratory of High-Orbits-Electron Materials and Protection Technology
for Aerospace, School of Advanced Materials and Nanotechnology, Xidian University, Xi’an 710071, China
| | - Haosen Chen
- Key
Laboratory of Wide Band-Gap Semiconductor Technology & Shaanxi
Key Laboratory of High-Orbits-Electron Materials and Protection Technology
for Aerospace, School of Advanced Materials and Nanotechnology, Xidian University, Xi’an 710071, China
| | - Mingming Gong
- School
of Materials Science and Engineering, Northwestern
Polytechnical University, Xi’an 710072, China
| | - Xin Zhang
- State
Key Laboratory of Superlattices and Microstructures, Institute of Semiconductors, Chinese Academy of Sciences, Beijing 100083, China
| | - Ping-Heng Tan
- State
Key Laboratory of Superlattices and Microstructures, Institute of Semiconductors, Chinese Academy of Sciences, Beijing 100083, China
| | | | - Yong Xie
- Key
Laboratory of Wide Band-Gap Semiconductor Technology & Shaanxi
Key Laboratory of High-Orbits-Electron Materials and Protection Technology
for Aerospace, School of Advanced Materials and Nanotechnology, Xidian University, Xi’an 710071, China
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Zhang B, Zhang Z, Han H, Ling H, Zhang X, Wang Y, Wang Q, Li H, Zhang Y, Zhang J, Song A. A Universal Approach to Determine the Atomic Layer Numbers in Two-Dimensional Materials Using Dark-Field Optical Contrast. NANO LETTERS 2023; 23:9170-9177. [PMID: 37493397 DOI: 10.1021/acs.nanolett.3c01722] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/27/2023]
Abstract
Two-dimensional (2D) materials possess unique properties primarily due to the quantum confinement effect, which highly depends on their thicknesses. Identifying the number of atomic layers in these materials is a crucial, yet challenging step. However, the commonly used optical reflection method offers only very low contrast. Here, we develop an approach that shows unprecedented sensitivity by analyzing the brightness of dark-field optical images. The brightness of the 2D material edges has a linear dependence on the number of atomic layers. The findings are modeled by Rayleigh scattering, and the results agree well with the experiments. The relative contrast of single-layer graphene can reach 70% under white-light incident conditions. Furthermore, different 2D materials were successfully tested. By adjusting the exposure conditions, we can identify the number of atomic layers ranging from 1 to over 100. Finally, this approach can be applied to various substrates, even transparent ones, making it highly versatile.
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Affiliation(s)
- Baoqing Zhang
- Shandong Technology Center of Nanodevices and Integration, School of Microelectronics, Shandong University, Jinan, 250100, China
| | - Zihao Zhang
- Shandong Technology Center of Nanodevices and Integration, School of Microelectronics, Shandong University, Jinan, 250100, China
| | - Hecheng Han
- Shandong Technology Center of Nanodevices and Integration, School of Microelectronics, Shandong University, Jinan, 250100, China
| | - Haotian Ling
- Shandong Technology Center of Nanodevices and Integration, School of Microelectronics, Shandong University, Jinan, 250100, China
| | - Xijian Zhang
- Shandong Technology Center of Nanodevices and Integration, School of Microelectronics, Shandong University, Jinan, 250100, China
| | - Yiming Wang
- Shandong Technology Center of Nanodevices and Integration, School of Microelectronics, Shandong University, Jinan, 250100, China
| | - Qingpu Wang
- Shandong Technology Center of Nanodevices and Integration, School of Microelectronics, Shandong University, Jinan, 250100, China
| | - Hu Li
- Shandong Technology Center of Nanodevices and Integration, School of Microelectronics, Shandong University, Jinan, 250100, China
| | - Yifei Zhang
- Shandong Technology Center of Nanodevices and Integration, School of Microelectronics, Shandong University, Jinan, 250100, China
| | - Jiawei Zhang
- Shandong Technology Center of Nanodevices and Integration, School of Microelectronics, Shandong University, Jinan, 250100, China
- Suzhou Research Institute, Shandong University, Suzhou, 215123, China
| | - Aimin Song
- Shandong Technology Center of Nanodevices and Integration, School of Microelectronics, Shandong University, Jinan, 250100, China
- Department of Electrical and Electronic Engineering, University of Manchester, Manchester, M13 9PL, United Kingdom
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Botifoll M, Pinto-Huguet I, Arbiol J. Machine learning in electron microscopy for advanced nanocharacterization: current developments, available tools and future outlook. NANOSCALE HORIZONS 2022; 7:1427-1477. [PMID: 36239693 DOI: 10.1039/d2nh00377e] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/16/2023]
Abstract
In the last few years, electron microscopy has experienced a new methodological paradigm aimed to fix the bottlenecks and overcome the challenges of its analytical workflow. Machine learning and artificial intelligence are answering this call providing powerful resources towards automation, exploration, and development. In this review, we evaluate the state-of-the-art of machine learning applied to electron microscopy (and obliquely, to materials and nano-sciences). We start from the traditional imaging techniques to reach the newest higher-dimensionality ones, also covering the recent advances in spectroscopy and tomography. Additionally, the present review provides a practical guide for microscopists, and in general for material scientists, but not necessarily advanced machine learning practitioners, to straightforwardly apply the offered set of tools to their own research. To conclude, we explore the state-of-the-art of other disciplines with a broader experience in applying artificial intelligence methods to their research (e.g., high-energy physics, astronomy, Earth sciences, and even robotics, videogames, or marketing and finances), in order to narrow down the incoming future of electron microscopy, its challenges and outlook.
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Affiliation(s)
- Marc Botifoll
- Catalan Institute of Nanoscience and Nanotechnology (ICN2), CSIC and BIST, Campus UAB, Bellaterra, 08193 Barcelona, Catalonia, Spain.
| | - Ivan Pinto-Huguet
- Catalan Institute of Nanoscience and Nanotechnology (ICN2), CSIC and BIST, Campus UAB, Bellaterra, 08193 Barcelona, Catalonia, Spain.
| | - Jordi Arbiol
- Catalan Institute of Nanoscience and Nanotechnology (ICN2), CSIC and BIST, Campus UAB, Bellaterra, 08193 Barcelona, Catalonia, Spain.
- ICREA, Pg. Lluís Companys 23, 08010 Barcelona, Catalonia, Spain
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Joy NJ, K RM, Balakrishnan J. A simple and robust machine learning assisted process flow for the layer number identification of TMDs using optical contrast spectroscopy. JOURNAL OF PHYSICS. CONDENSED MATTER : AN INSTITUTE OF PHYSICS JOURNAL 2022; 51:025901. [PMID: 36322998 DOI: 10.1088/1361-648x/ac9f96] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/02/2022] [Accepted: 11/02/2022] [Indexed: 06/16/2023]
Abstract
Layered transition metal dichalcogenides (TMDs) like tungsten disulphide (WS2) possess a large direct electronic band gap (∼2 eV) in the monolayer limit, making them ideal candidates for opto-electronic applications. The size and nature of the bandgap is strongly dependent on the number of layers. However, different TMDs require different experimental tools under specific conditions to accurately determine the number of layers. Here, we identify the number of layers of WS2exfoliated on top of SiO2/Si wafer from optical images using the variation of optical contrast with thickness. Optical contrast is a universal feature that can be easily extracted from digital images. But fine variations in the optical images due to different capturing conditions often lead to inaccurate layer number determination. In this paper, we have implemented a simple Machine Learning assisted image processing workflow that uses image segmentation to eliminate this difficulty. The workflow developed for WS2is also demonstrated on MoS2, graphene and h-BN, showing its applicability across various classes of 2D materials. A graphical user interface is provided to enhance the adoption of this technique in the 2D materials research community.
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Affiliation(s)
- Nikhil Joseph Joy
- Department of Physics, Indian Institute of Technology Palakkad, Palakkad 678623, Kerala, India
| | - Ranjuna M K
- Department of Physics, Indian Institute of Technology Palakkad, Palakkad 678623, Kerala, India
| | - Jayakumar Balakrishnan
- Department of Physics, Indian Institute of Technology Palakkad, Palakkad 678623, Kerala, India
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Dong X, Li H, Yan Y, Cheng H, Zhang HX, Zhang Y, Le TD, Wang K, Dong J, Jakobi M, Yetisen AK, Koch AW. Deep‐Learning‐Based Microscopic Imagery Classification, Segmentation, and Detection for the Identification of 2D Semiconductors. ADVANCED THEORY AND SIMULATIONS 2022. [DOI: 10.1002/adts.202200140] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Affiliation(s)
- Xingchen Dong
- Institute for Measurement Systems and Sensor Technology Department of Electrical and Computer Engineering Technical University of Munich 80333 Munich Germany
| | - Hongwei Li
- Department of Computer Science Technical University of Munich 85748 Garching Germany
- Department of Quantitative Biomedicine University of Zurich Zurich 8045 Switzerland
| | - Yuntian Yan
- Institute for Measurement Systems and Sensor Technology Department of Electrical and Computer Engineering Technical University of Munich 80333 Munich Germany
| | - Haoran Cheng
- Institute for Measurement Systems and Sensor Technology Department of Electrical and Computer Engineering Technical University of Munich 80333 Munich Germany
| | - Hui Xin Zhang
- Institute for Measurement Systems and Sensor Technology Department of Electrical and Computer Engineering Technical University of Munich 80333 Munich Germany
| | - Yucheng Zhang
- Institute for Measurement Systems and Sensor Technology Department of Electrical and Computer Engineering Technical University of Munich 80333 Munich Germany
| | - Tien Dat Le
- Institute for Measurement Systems and Sensor Technology Department of Electrical and Computer Engineering Technical University of Munich 80333 Munich Germany
| | - Kun Wang
- Institute for Measurement Systems and Sensor Technology Department of Electrical and Computer Engineering Technical University of Munich 80333 Munich Germany
| | - Jie Dong
- Institute for Measurement Systems and Sensor Technology Department of Electrical and Computer Engineering Technical University of Munich 80333 Munich Germany
| | - Martin Jakobi
- Institute for Measurement Systems and Sensor Technology Department of Electrical and Computer Engineering Technical University of Munich 80333 Munich Germany
| | - Ali K. Yetisen
- Department of Chemical Engineering Imperial College London London SW7 2AZ UK
| | - Alexander W. Koch
- Institute for Measurement Systems and Sensor Technology Department of Electrical and Computer Engineering Technical University of Munich 80333 Munich Germany
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Universal image segmentation for optical identification of 2D materials. Sci Rep 2021; 11:5808. [PMID: 33707609 PMCID: PMC7970966 DOI: 10.1038/s41598-021-85159-9] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2020] [Accepted: 02/19/2021] [Indexed: 11/21/2022] Open
Abstract
Machine learning methods are changing the way data is analyzed. One of the most powerful and widespread applications of these techniques is in image segmentation wherein disparate objects of a digital image are partitioned and classified. Here we present an image segmentation program incorporating a series of unsupervised clustering algorithms for the automatic thickness identification of two-dimensional materials from digital optical microscopy images. The program identifies mono- and few-layer flakes of a variety of materials on both opaque and transparent substrates with a pixel accuracy of roughly 95%. Contrasting with previous attempts, application generality is achieved through preservation and analysis of all three digital color channels and Gaussian mixture model fits to arbitrarily shaped data clusters. Our results provide a facile implementation of data clustering for the universal, automatic identification of two-dimensional materials exfoliated onto any substrate.
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