Nanophotonic inverse design with deep neural networks based on knowledge transfer using imbalanced datasets

Abstract

Deep neural networks (DNNs) have been used as a new method for nanophotonic inverse design. However, DNNs need a huge dataset to train if we need to select materials from the material library for the inverse design. This puts the DNN method into a dilemma of poor performance with a small training dataset or loss of the advantage of short design time, for collecting a large amount of data is time consuming. In this work, we propose a multi-scenario training method for the DNN model using imbalanced datasets. The imbalanced datasets used by our method is nearly four times smaller compared with other training methods. We believe that as the material library increases, the advantages of the imbalanced datasets will become more obvious. Using the high-precision predictive DNN model obtained by this new method, different multilayer nanoparticles and multilayer nanofilms have been designed with a hybrid optimization algorithm combining genetic algorithm and gradient descent optimization algorithm. The advantage of our method is that it can freely select discrete materials from the material library and simultaneously find the inverse design of discrete material type and continuous structural parameters of the nanophotonic devices.