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.
@article{arxiv.1908.07017,
title = {Optical deep learning nano-profilometry},
author = {Jinlong Zhu and Yanan Liu and Sanyogita Purandare and Jian-Ming Jin and Shiyuan Liu and Lynford L. Goddard},
journal= {arXiv preprint arXiv:1908.07017},
year = {2019}
}