A neural operator-based surrogate solver for free-form electromagnetic inverse design

TL;DR

This paper introduces a neural operator-based surrogate model for electromagnetic inverse design, demonstrating improved data efficiency and enabling complex 3D nanophotonic design tasks previously challenging for deep learning.

Contribution

It develops a modified Fourier neural operator as a surrogate solver for electromagnetic problems and applies it to 3D nanophotonic inverse design, advancing the use of neural operators in complex electromagnetic applications.

Findings

Enhanced data efficiency compared to existing methods

Successful application to 3D electromagnetic inverse design

Enables deep learning techniques for complex free-form scatterers

Abstract

Neural operators have emerged as a powerful tool for solving partial differential equations in the context of scientific machine learning. Here, we implement and train a modified Fourier neural operator as a surrogate solver for electromagnetic scattering problems and compare its data efficiency to existing methods. We further demonstrate its application to the gradient-based nanophotonic inverse design of free-form, fully three-dimensional electromagnetic scatterers, an area that has so far eluded the application of deep learning techniques.