Machine Learning for Optical Scanning Probe Nanoscopy

Authors:
  • XCXinzhong Chen
    Xinzhong Chen
  • SXSuheng Xu
    Suheng Xu
  • SSSara Shabani
    Sara Shabani
  • YZYueqi Zhao
    Yueqi Zhao
  • MFMatthew Fu
    Matthew Fu
  • AMAndrew J. Millis
    Andrew J. Millis
  • MFMichael M. Fogler
    Michael M. Fogler
  • APAbhay N. Pasupathy
    Abhay N. Pasupathy
  • MLMengkun Liu
    Mengkun Liu
  • DBD. N. Basov
    D. N. Basov
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Abstract
The ability to perform nanometer-scale optical imaging and spectroscopy is key to deciphering the low-energy effects in quantum materials, as well as vibrational fingerprints in planetary and extraterrestrial particles, catalytic substances, and aqueous biological samples. The scattering-type scanning near-field optical microscopy (s-SNOM) technique has recently spread to many research fields and enabled notable discoveries. In this brief perspective, we show that the s-SNOM, together with scanning probe research in general, can benefit in many ways from artificial intelligence (AI) and machine learning (ML) algorithms. We show that, with the help of AI- and ML-enhanced data acquisition and analysis, scanning probe optical nanoscopy is poised to become more efficient, accurate, and intelligent.
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