Deep Learning Analysis of Polaritonic Wave Images

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.