Deep Learning for The Inverse Design of Mid-infrared Graphene Plasmons

TL;DR

This paper uses deep learning to enable the inverse design of mid-infrared graphene plasmonic metamaterials, facilitating tailored optical properties through neural network models trained on simulated data.

Contribution

It introduces a neural network-based inverse design method for graphene plasmonic structures, combining theoretical modeling with deep learning to optimize metamaterial configurations.

Findings

Neural network accurately predicts structural parameters from optical spectra.

The approach aligns well with experimental data.

Limitations of data-driven inverse design are discussed.

Abstract

We theoretically investigate the plasmonic properties of mid-infrared graphene-based metamaterials and apply deep learning of a neural network for the inverse design. These artificial structures have square periodic arrays of graphene plasmonic resonators deposited on dielectric thin films. Optical spectra vary significantly with changes in structural parameters. Our numerical results are in accordance with previous experiments. Then, the theoretical approach is employed to generate data for training and testing deep neural networks. By merging the pre-trained neural network with the inverse network, we implement calculations for inverse design of the graphene-based metameterials. We also discuss the limitation of the data-driven approach.