Deep‐Learning‐Enabled Fast Optical Identification and Characterization of 2D Materials
© 2020 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim Advanced microscopy and/or spectroscopy tools play indispensable roles in nanoscience and nanotechnology research, as they provide rich information about material processes and properties. However, the interpretation of imaging data heavily rel...
| Main Authors: | , , , , , , , , , , , , , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2021
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| Online Access: | https://hdl.handle.net/1721.1/133692 |
| _version_ | 1826195100776005632 |
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| author | Han, Bingnan Lin, Yuxuan Yang, Yafang Mao, Nannan Li, Wenyue Wang, Haozhe Yasuda, Kenji Wang, Xirui Fatemi, Valla Zhou, Lin Wang, Joel I-Jan Ma, Qiong Cao, Yuan Rodan-Legrain, Daniel Bie, Ya-Qing Navarro-Moratalla, Efrén Klein, Dahlia MacNeill, David Wu, Sanfeng Kitadai, Hikari Ling, Xi Jarillo-Herrero, Pablo Kong, Jing Yin, Jihao Palacios, Tomás |
| author2 | Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science |
| author_facet | Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science Han, Bingnan Lin, Yuxuan Yang, Yafang Mao, Nannan Li, Wenyue Wang, Haozhe Yasuda, Kenji Wang, Xirui Fatemi, Valla Zhou, Lin Wang, Joel I-Jan Ma, Qiong Cao, Yuan Rodan-Legrain, Daniel Bie, Ya-Qing Navarro-Moratalla, Efrén Klein, Dahlia MacNeill, David Wu, Sanfeng Kitadai, Hikari Ling, Xi Jarillo-Herrero, Pablo Kong, Jing Yin, Jihao Palacios, Tomás |
| author_sort | Han, Bingnan |
| collection | MIT |
| description | © 2020 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim Advanced microscopy and/or spectroscopy tools play indispensable roles in nanoscience and nanotechnology research, as they provide rich information about material processes and properties. However, the interpretation of imaging data heavily relies on the “intuition” of experienced researchers. As a result, many of the deep graphical features obtained through these tools are often unused because of difficulties in processing the data and finding the correlations. Such challenges can be well addressed by deep learning. In this work, the optical characterization of 2D materials is used as a case study, and a neural-network-based algorithm is demonstrated for the material and thickness identification of 2D materials with high prediction accuracy and real-time processing capability. Further analysis shows that the trained network can extract deep graphical features such as contrast, color, edges, shapes, flake sizes, and their distributions, based on which an ensemble approach is developed to predict the most relevant physical properties of 2D materials. Finally, a transfer learning technique is applied to adapt the pretrained network to other optical identification applications. This artificial-intelligence-based material characterization approach is a powerful tool that would speed up the preparation, initial characterization of 2D materials and other nanomaterials, and potentially accelerate new material discoveries. |
| first_indexed | 2024-09-23T10:07:01Z |
| format | Article |
| id | mit-1721.1/133692 |
| institution | Massachusetts Institute of Technology |
| language | English |
| last_indexed | 2024-09-23T10:07:01Z |
| publishDate | 2021 |
| publisher | Wiley |
| record_format | dspace |
| spelling | mit-1721.1/1336922023-12-13T21:22:36Z Deep‐Learning‐Enabled Fast Optical Identification and Characterization of 2D Materials Han, Bingnan Lin, Yuxuan Yang, Yafang Mao, Nannan Li, Wenyue Wang, Haozhe Yasuda, Kenji Wang, Xirui Fatemi, Valla Zhou, Lin Wang, Joel I-Jan Ma, Qiong Cao, Yuan Rodan-Legrain, Daniel Bie, Ya-Qing Navarro-Moratalla, Efrén Klein, Dahlia MacNeill, David Wu, Sanfeng Kitadai, Hikari Ling, Xi Jarillo-Herrero, Pablo Kong, Jing Yin, Jihao Palacios, Tomás Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology. Department of Physics Massachusetts Institute of Technology. Research Laboratory of Electronics © 2020 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim Advanced microscopy and/or spectroscopy tools play indispensable roles in nanoscience and nanotechnology research, as they provide rich information about material processes and properties. However, the interpretation of imaging data heavily relies on the “intuition” of experienced researchers. As a result, many of the deep graphical features obtained through these tools are often unused because of difficulties in processing the data and finding the correlations. Such challenges can be well addressed by deep learning. In this work, the optical characterization of 2D materials is used as a case study, and a neural-network-based algorithm is demonstrated for the material and thickness identification of 2D materials with high prediction accuracy and real-time processing capability. Further analysis shows that the trained network can extract deep graphical features such as contrast, color, edges, shapes, flake sizes, and their distributions, based on which an ensemble approach is developed to predict the most relevant physical properties of 2D materials. Finally, a transfer learning technique is applied to adapt the pretrained network to other optical identification applications. This artificial-intelligence-based material characterization approach is a powerful tool that would speed up the preparation, initial characterization of 2D materials and other nanomaterials, and potentially accelerate new material discoveries. 2021-10-27T19:54:10Z 2021-10-27T19:54:10Z 2020 2020-10-29T15:19:06Z Article http://purl.org/eprint/type/JournalArticle https://hdl.handle.net/1721.1/133692 en 10.1002/ADMA.202000953 Advanced Materials Creative Commons Attribution-Noncommercial-Share Alike http://creativecommons.org/licenses/by-nc-sa/4.0/ application/pdf Wiley arXiv |
| spellingShingle | Han, Bingnan Lin, Yuxuan Yang, Yafang Mao, Nannan Li, Wenyue Wang, Haozhe Yasuda, Kenji Wang, Xirui Fatemi, Valla Zhou, Lin Wang, Joel I-Jan Ma, Qiong Cao, Yuan Rodan-Legrain, Daniel Bie, Ya-Qing Navarro-Moratalla, Efrén Klein, Dahlia MacNeill, David Wu, Sanfeng Kitadai, Hikari Ling, Xi Jarillo-Herrero, Pablo Kong, Jing Yin, Jihao Palacios, Tomás Deep‐Learning‐Enabled Fast Optical Identification and Characterization of 2D Materials |
| title | Deep‐Learning‐Enabled Fast Optical Identification and Characterization of 2D Materials |
| title_full | Deep‐Learning‐Enabled Fast Optical Identification and Characterization of 2D Materials |
| title_fullStr | Deep‐Learning‐Enabled Fast Optical Identification and Characterization of 2D Materials |
| title_full_unstemmed | Deep‐Learning‐Enabled Fast Optical Identification and Characterization of 2D Materials |
| title_short | Deep‐Learning‐Enabled Fast Optical Identification and Characterization of 2D Materials |
| title_sort | deep learning enabled fast optical identification and characterization of 2d materials |
| url | https://hdl.handle.net/1721.1/133692 |
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