Deep learning analysis of polaritonic waves images
Abstract
Deep learning (DL) is an emerging analysis tool across sciences and engineering. Encouraged by the successes of DL in revealing quantitative trends in massive imaging data, we applied this approach to nano-scale deeply sub-diffractional images of propagating polaritonic waves in complex materials. We developed a practical protocol for the rapid regression of images that quantifies the wavelength and the quality factor of polaritonic waves utilizing the convolutional neural network (CNN). Using simulated near-field images as training data, the CNN can be made to simultaneously extract polaritonic characteristics and materials 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/{\alpha}-RuCl3 interfaces. Our work provides a general framework for extracting quantitative information from images generated with a variety of scanning probe methods.
Cite
@article{arxiv.2108.07222,
title = {Deep learning analysis of polaritonic waves images},
author = {Suheng Xu and Alexander S. McLeod and Xinzhong Chen and Daniel J. Rizzo and Bjarke S. Jessen and Ziheng Yao and Zhiyuan Sun and Sara Shabani and Abhay N. Pasupathy and Andrew J. Millis and Cory R. Dean and James C. Hone and Mengkun Liu and D. N. Basov},
journal= {arXiv preprint arXiv:2108.07222},
year = {2024}
}