Deep Neural Networks for Inverse Design of Nanophotonic Devices

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.

Automated summary

This article explores three models for designing nanophonic power splitters using deep neural networks, including a forward regression model, an inverse regression model, and a generative network. These models demonstrate how deep learning can be applied to optimize the design of nanophotonic devices.