  Determining the dimensions of nanostructures is critical to ensuring the
maximum performance of many geometry-sensitive nanoscale functional devices.
However, accurate metrology at the nanoscale is difficult using optics-based
methods due to the diffraction limit. In this article, we propose an optical
nano-profilometry framework with convolutional neural networks, which can
retrieve deep sub-wavelength geometrical profiles of nanostructures from their
optical images or scattering spectra. The generality, efficiency, and accuracy
of the proposed framework are validated by performing two different
measurements on three distinct nanostructures. We believe this work may
catalyze more explorations of optics-based nano-metrology with deep learning.
