Inverse Design of Nanophotonic Devices using Deep Neural Networks
Keisuke Kojima, Yingheng Tang, Toshiaki Koike–Akino, Ye Wang, Devesh K. Jha, Kieran Parsons, Mohammad Hossein Tahersima, Fengqiao Sang, Jonathan Klamkin, Minghao Qi · 2020
We present three different approaches to apply deep learning to inverse design for nanophotonic devices. The forward and inverse regression models use device parameters as inputs and device responses as outputs, and vice versa. The generative model to create a series of improved designs. We demonstrate them to design nanophotonic power splitters with multiple splitting ratios.