Deep-Learning-Enabled Fast Optical Identification and Characterization of 2D Materials

Bingnan Han, Yuxuan Lin, Yafang Yang, Nannan Mao, Wenyue Li, Haozhe Wang, Kenji Yasuda, Xirui Wang, Valla Fatemi, Lin Zhou, Joel I.Jan Wang, Qiong Ma, Yuan Cao, Daniel Rodan-Legrain, Ya Qing Bie, Efrén Navarro-Moratalla, Dahlia Klein, David MacNeill, Sanfeng Wu, Hikari KitadaiXi Ling, Pablo Jarillo-Herrero, Jing Kong, Jihao Yin, Tomás Palacios

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

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.

Original languageEnglish (US)
Article number2000953
JournalAdvanced Materials
Volume32
Issue number29
DOIs
StatePublished - Jul 1 2020
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Materials Science(all)
  • Mechanics of Materials
  • Mechanical Engineering

Keywords

  • 2D materials
  • deep learning
  • machine learning
  • material characterization
  • optical microscopy

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