Machine learning for nanophotonics

Abstract

The past decade has witnessed the advent of nanophotonics, where light–matter interaction is shaped, almost at will, with human-made designed nanostructures. However, the design process for these nanostructures has remained complex, often relying on the intuition and expertise of the designer, ultimately limiting the reach and penetration of this groundbreaking approach. Recently, there has been an increasing number of studies in applying machine learning techniques for the design of nanostructures. Most of these studies engage deep learning techniques, which entail training a deep neural network (DNN) to approximate the highly nonlinear function of the underlying physical process of the interaction between light and the nanostructures. At the end of the training, the DNN allows for on-demand design of nanostructures (i.e., the model can infer nanostructure geometries for desired light spectra). In this article, we review previous studies for designing nanostructures, including recent advances where a DNN is trained to generate a two-dimensional image of the designed nanostructure, which is not limited to a closed set of nanostructure shapes, and can be trained for the design of any geometry. This allows for better generalization, with higher applicability for real-world design problems.