Deep Neural Networks for the Prediction of the Optical Properties and the Design of Metamaterials

Paper i proceeding, 2021

We will present our work on using deep neural networks for the prediction of the optical properties of free-form nanophotonic structures and for their inverse design. We designed neural networks and created training data, which were labelled with the respective optical properties and the degree of manufacturability. Furthermore, a cGAN network with 5 neural networks was developed to overcome problems with non-uniqueness and mode collapse, and to increase the experimental feasibility of the generated structures.