Deep‐Learning‐Enabled Fast Optical Identification and Characterization of 2D Materials
Name
1906.11220.pdf
Description
Submitted version
Size
3.56 MB
Format
Adobe PDF
Checksum (MD5)
803513e102cac4bab1eb6ad02b73d4f2
Author(s)
Han, Bingnan
Lin, Yuxuan
Yang, Yafang
Mao, Nannan
Li, Wenyue
Wang, Haozhe
Yasuda, Kenji
Wang, Xirui
Fatemi, Valla
Zhou, Lin
Date Issued
2020
Journal
Advanced Materials
Publisher
Wiley
Version
Original manuscript
Abstract
© 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.
MIT Department
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
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
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DOI of Published Version
https://doi.org/10.1002/ADMA.202000953
