Machine Learning for Optical Scanning Probe Nanoscopy
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.
Cite
@article{arxiv.2204.09820,
title = {Machine Learning for Optical Scanning Probe Nanoscopy},
author = {Xinzhong Chen and Suheng Xu and Sara Shabani and Yueqi Zhao and Matthew Fu and Andrew J. Millis and Michael M. Fogler and Abhay N. Pasupathy and Mengkun Liu and D. N. Basov},
journal= {arXiv preprint arXiv:2204.09820},
year = {2022}
}