Applying Machine Learning to the Optics of Dielectric Nanoblobs

Abstract

Dielectric nanostructures are the basic building blocks for photonic metasurfaces exhibiting designer optical responses. As their optical responses are nonintuitive, design procedures often consider only primitive geometries such as circles, ellipses, and rectangles. Despite these simplified geometries, achieving a target response still requires the forward design problem of solving Maxwell's equations to build a database of geometric parameters and their spectral responses. Herein, this work aims to leverage on the strength of deep neural networks (DNN) in image recognition to tackle the intractable inverse design problem of complex geometries, in which geometric parameters cannot be extracted. The work focuses on nanoblob geometries, i.e., irregular structures with rounded corners. When given a desired spectral response, the work investigates the ability of a well-trained DNN in generating suitable geometries of the dielectric nanostructure and a corresponding source polarization.