Deep Neural Networks for Inverse Design of Nanophotonic Devices
Keisuke Kojima, Mohammad H. Tahersima, Toshiaki Koike–Akino, Devesh K. Jha, Yingheng Tang, Ye Wang, Kieran Parsons
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.