Deep Neural Network Inverse Design of Integrated Photonic Power Splitters

TLDR

Predicting the physical response of artificially structured materials is a key interest in science and engineering. The study employs deep learning to predict the optical response of engineered nanophotonic devices. The model is trained to reduce reflection below ~−20 dB, achieve >90 % transmission efficiency, and meet target splitting specifications. The DNN not only predicts forward transmission but also inversely designs compact 1×2 silicon‑on‑insulator power splitters with various splitting ratios in seconds, enabling rapid design of complex nanostructured photonic components.

Abstract

Abstract Predicting physical response of an artificially structured material is of particular interest for scientific and engineering applications. Here we use deep learning to predict optical response of artificially engineered nanophotonic devices. In addition to predicting forward approximation of transmission response for any given topology, this approach allows us to inversely approximate designs for a targeted optical response. Our Deep Neural Network (DNN) could design compact (2.6 × 2.6 μm 2 ) silicon-on-insulator (SOI)-based 1 × 2 power splitters with various target splitting ratios in a fraction of a second. This model is trained to minimize the reflection (to smaller than ~ −20 dB) while achieving maximum transmission efficiency above 90% and target splitting specifications. This approach paves the way for rapid design of integrated photonic components relying on complex nanostructures.