Deep Learning Assisted Optical Identification of Exfoliated Two-Dimensional Crystals

ORAL

Abstract

Up to now, hundreds of two-dimensional materials are being studied in the fields of condensed matter physics, material sciences and electrical engineering. The overwhelming approach to obtain 2D crystals in laboratory is a combination of the mechanical exfoliation and the exhaustive search under an optical microscope by a well-trained researcher. Here we report a generic flake-hunting approach assisted by deep learning that can achieve the automatic, real-time, accurate, and robust optical identification of the type and the thickness of various 2D crystals. A semantic segmentation method using the encoder-decoder convolutional neural networks (SegNet) was developed and trained to identify the type and the thickness of the mechanically exfoliated 2D crystals on a SiO2/Si wafer. Besides the commonly used parameters such as the optical contrasts of the 2D crystals, deep graphical features can also be extracted and harnessed by the SegNet for accurate and robust identification. Our proposed method can be used for a wide range of research topics where initial screening and identification of nanomaterials are necessary.

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Presenters

  • Yuxuan Lin

    • Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology

Authors

  • Bingnan Han

    • School of Astronautics, Beihang University

  • Yuxuan Lin

    • Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology

  • Wenyue Li

    • School of Astronautics, Beihang University

  • Nannan Mao

    • Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology
    • Massachusetts Institute of Technology
    • Electrical Engineering and Computer Science, Massachusetts Institute of Technology (MIT)

  • Yafang Yang

    • Department of Physics, Massachusetts Institute of Technology
    • Massachusetts Institute of Technology

  • Haozhe Wang

    • Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology
    • Electrical Engineering and Computer Science, Massachusetts Institute of Technology (MIT)

  • Wei Sun Leong

    • Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology

  • Pablo Jarillo-Herrero

    • Physics, Massachusetts Institute of Technology
    • Department of Physics, Massachusetts Institute of Technology
    • Massachusetts Institute of Technology
    • Dept. of Physics, Massachusetts Institute of Technology, USA
    • Massachusetts Inst of Tech-MIT
    • Physics, MIT

  • Tomas Palacios

    • Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology

  • Jihao Yin

    • School of Astronautics, Beihang University

  • Jing Kong

    • Department of Electrical Engineering and Computer Sciences, Massachusetts Institute of Technology
    • Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology
    • Massachusetts Institute of Technology
    • Electrical Engineering and Computer Science, Massachusetts Institute of Technology (MIT)
    • Research Laboratory of Electronics, Massachusetts Institute of Technology