  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. Compared to its traditional counterparts, our method
demonstrates superior accuracy, stability against noise, and computational
speed when applied to simulated near-field spectra.
