Inverse Design of Nanophotonic Devices using Deep Neural Networks
Abstract: Deep learning is now playing a major role in designing photonic devices, including nanostructured photonics. In this article, we investigate three models for designing nanophonic power splitters with multiple splitting ratios. The first model is a forward regression model, wherein the trained deep neural network (DNN) is used within the optimization loop. The second is an inverse regression model, in which the trained DNN constructs a structure with the desired target performance given as input. The third model is a generative network, which can randomly produce a series of optimized designs for a target performance. Focusing on the nanophotonic power splitters, we show how the devices can be designed by these three types of DNN models. Authors: Keisuke Kojima, Yingheng Tang, Toshiaki Koike-Akino, Ye Wang, Devesh Jha, Kieran Parsons, Mohammad H. Tahersima, Fengqiao Sang, Jonathan Klamkin, and Minghao Qi (Mitsubishi Electric Research Laboratories)