Multiplexed Supercell Metasurface Design and Optimization with Tandem Residual Networks

TL;DR

This paper introduces a deep learning approach using tandem residual networks for inverse design of complex nanophotonic supercells, enabling efficient exploration of vast design spaces for tailored optical responses.

Contribution

It presents a novel deep neural network framework capable of inverse designing multiplexed supercells in nanophotonics, significantly improving design efficiency over classical methods.

Findings

Accurately generates complex supercell designs from spectral targets.

Handles a design space of over three trillion possibilities.

Explores structure-property relationships in broadband absorption.

Abstract

Complex nanophotonic structures hold the potential to deliver exquisitely tailored optical responses for a range of applications. Metal-insulator-metal (MIM) metasurfaces arranged in supercells, for instance, can be tailored by geometry and material choice to exhibit a variety of absorption properties and resonant wavelengths. With this flexibility, however, comes a vast space of design possibilities that classical design paradigms struggle to effectively navigate. To overcome this challenge, here we demonstrate a tandem residual network approach to efficiently generate multiplexed supercells through inverse design. By using a training dataset with several thousand full-wave electromagnetic simulations in a design space of over three trillion possible designs, the deep learning model can accurately generate a wide range of complex supercell designs given a spectral target. Beyond inverse design, the presented approach can also be used to explore the structure-property relationships of broadband absorption and emission in such supercell configurations. Thus, this study demonstrates the feasibility of high-dimensional supercell inverse design with deep neural networks that is applicable to complex nanophotonic structures composed of multiple subunit elements that may exhibit coupling.