Physics-Informed PointNets for Modeling Electromagnetic Scattering from All-Dielectric Metasurfaces with Inclined Nanopillars

TL;DR

This paper introduces a mesh-free Physics-Informed PointNet model for efficient electromagnetic scattering simulation of complex dielectric metasurfaces with irregular geometries and manufacturing variations.

Contribution

It develops a novel mesh-free, physics-informed machine learning approach using PointNet to model electromagnetic scattering in irregular metasurfaces, including inclined nanopillars.

Findings

Demonstrates accurate predictions on datasets with varying refractive indices.

Shows generalization to different inclination angles simulating manufacturing defects.

Provides a computationally efficient alternative to traditional solvers.

Abstract

Metasurfaces are innovative planar optical structures capable of manipulating incident light properties. Accurate and computationally efficient modeling of such metasurfaces, particularly those with irregular geometries, remains a challenge for conventional solvers. In this work, we present a mesh-free Physics-Informed PointNet (PIPN) to model electromagnetic scattering from all-dielectric metasurfaces that feature spatially varying nanopillars. Our approach uses the PointNet architecture to directly encode spatially varying material properties into the Physics-Informed Machine Learning (PIML) framework. We demonstrate the generalization capability of our PIPN through evaluations on datasets; these datasets are generated with varying refractive indices representing common dielectric materials. Furthermore, the inclination angles are varied within each dataset, which represent expected manufacturing defects. Overall, our method provides a promising, mesh-free framework for accurate and efficient modeling of complex optical structures represented by irregular geometries.