Inverse design of unparametrized nanostructures by generating images from spectra

Recently, there has been an increasing number of studies applying machine learning techniques for the design of nanostructures. Most of these studies train a deep neural network (DNN) to approximate the highly nonlinear function of the underlying physical mapping between spectra and nanostructures. At the end of training, the DNN allows an on-demand design of nanostructures, i.e., the model can infer nanostructure geometries for desired spectra. While these approaches have presented a new paradigm, they are limited in the complexity of the structures proposed, often bound to parametric geometries. Here we introduce spectra2pix, which is a DNN trained to generate 2D images of the target nanostructures. By predicting an image, our model architecture is not limited to a closed set of nanostructure shapes, and can be trained for the design of a much wider space of geometries. We show, for the first time, to the best of our knowledge, a successful generalization ability, by designing completely unseen shapes of geometries. We attribute the successful generalization to the ability of a pixel-wise architecture to learn local properties of the meta-material, therefore mimicking faithfully the underlying physical process. Importantly, beyond synthetical data, we show our model generalization capability on real experimental data. (C) 2021 Optical Society of America

Automated summary

In recent years, an increasing number of studies have used machine learning techniques to design nanostructures, with a focus on training neural networks to approximate the relationship between spectra and nanostructures. By predicting images, these models are not limited by specific nanostructure shapes and can design a wider range of geometries. The successful generalization of designing unseen geometries is attributed to the ability of pixel-wise architecture to learn local properties of meta-materials accurately.