Deep-learning-aided extraction of optical constants in scanning near-field optical microscopy
Yueqi Zhao, Xinzhong Chen, Ziheng Yao, Mengkun Liu, M. M. Fogler
Abstract
Scanning near-field optical microscopy is one of the most effective techniques for spectroscopy of nanoscale systems. However, inferring optical constants from the measured near-field signal can be challenging because of a complicated and highly nonlinear interaction between the scanned probe and the sample. Conventional fitting methods applied to this problem often suffer from the lack of convergence or require human intervention. Here, we develop an alternative approach where the optical parameter extraction is automated by a deep learning network. The network provides an initial estimate that is subsequently refined by a traditional fitting algorithm. We show that this method demonstrates superior accuracy, stability against noise, and computational speed when applied to simulated near-field spectra.