Adjoint-Based Gradient Evaluation for Metasurface Inverse Design via Affine Geometric Transformations

TL;DR

This paper introduces an adjoint-based gradient evaluation method for large-scale metasurface inverse design, utilizing affine geometric transformations to efficiently optimize complex nanostructures for electromagnetic control.

Contribution

It presents a systematic methodology combining affine transformations with adjoint methods for efficient large metasurface inverse design.

Findings

The method enables fast gradient computation for geometric parameters.

The approach is theoretically justified and numerically validated.

It reduces computational costs in metasurface optimization.

Abstract

The sharp increasing in fabrication capabilities of nanomaterials, and complex structures such as meta-surfaces and metalens, has opened to the possibility of employing them for accurately control the electromagnetic field, beyond the possibility ensured by traditional devices. The demand for large scale structures and more complex functionalities from meta-surfaces lead to the research for advanced techniques of inverse design, able to conjugate the ability to produce effective designs and limited computational cost. Among the various approaches for inverse design of large meta-surfaces, the ones based on the adjoint variable method are appealing since able to ensure a minimal computational cost for the gradient computation of the cost function. In this work, a systematic methodology for the application of the adjoint variable method for large meta-surface design is presented. The method is based on: (i) a parametrization of the relevant geometric parameters of the meta-atoms, (ii) the fast computation of the gradient with respect such parameters, allowing for the implementation of general affine transformations during the optimization process. The main findings are first theoretically justified and a numerical validation is provided to show the effectiveness of the proposed approach.