Deep Learning Analysis of Polaritonic Wave Images

ORAL

Abstract

We applied deep learning(DL) to nanoscale deeply sub-diffractional images of propagating polaritonic waves in complex materials. Utilizing the convolutional neural network (CNN), we developed a practical protocol for the rapid regression of images that quantifies the wavelength and the quality factor of polaritonic waves. Using simulated near-field images as training data, the CNN can be made to simultaneously extract polaritonic characteristics and material parameters in a timescale that is at least three orders of magnitude faster than common fitting/processing procedures. The CNN-based analysis was validated by examining the experimental near-field images of charge-transfer plasmon polaritons at graphene/α-RuCl3 interfaces. Our work provides a general framework for extracting quantitative information from images generated with a variety of scanning probe methods.

*Research at Columbia on graphene/RuCl3 interfaces was supported by the US Department of Energy (DOE), Office of Science, Basic Energy Sciences (BES), under award DE-SC0018426 (AM, DJR, DNB). The development of nanofabrication and characterization techniques enabling this work was supported by the US DOE Office of Science, BES, under award DE-SC0019300 (CRD, JCH). The development of the universal cryogenic platform used for scanning probe measurements was supported as part of the Energy Frontier Research Center on Programmable Quantum Materials funded by the US Department of Energy (DOE), Office of Science, Basic Energy Sciences (BES), under award no. DE-SC0019443. The development of ML protocols at Columbia, Stony Brook, and Brookhaven was supported by the U.S. Department of Energy, Office of Science, National Quantum Information Science Research Centers, Co-design Center for Quantum Advantage (C2QA) under contract number DE-SC0012704. Mengkun Liu was partially supported by

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Presenters

  • Suheng Xu

    • Columbia University

Authors

  • Suheng Xu

    • Columbia University

  • Alexander S McLeod

    • Columbia Univ
    • Columbia University

  • Xinzhong Chen

    • Stony Brook University (SUNY)
    • State Univ of NY - Stony Brook

  • Daniel J Rizzo

    • Columbia University

  • Bjarke S Jessen

    • Columbia University

  • Ziheng Yao

    • State Univ of NY - Stony Brook
    • Stony Brook University (SUNY)

  • Zhicai Wang

    • State Univ of NY - Stony Brook

  • Zhiyuan Sun

    • Columbia Univ
    • Harvard University
    • Columbia University

  • Sara Shabani

    • Columbia University

  • Abhay N Pasupathy

    • Columbia University
    • Brookhaven National Laboratory & Columbia University

  • Andrew J Millis

    • Columbia University
    • Columbia University; Flatiron Institute
    • Columbia University, Flatiron Institute

  • Cory R Dean

    • Columbia University
    • Columbia Univ

  • James C Hone

    • Columbia University

  • Mengkun Liu

    • State Univ of NY - Stony Brook

  • Dmitri N Basov

    • Columbia University