Topological learning for the classification of disorder: an application to the design of metasurfaces

TL;DR

This paper introduces topological descriptors to quantitatively analyze and control disorder in metasurfaces, enhancing their optical properties and aiding in the design and fabrication process.

Contribution

It presents novel topological measures for disorder that are effective for both uncorrelated and correlated cases, validated through theoretical and experimental studies.

Findings

Topological descriptors accurately quantify disorder in metasurfaces.

Controlled disorder influences surface lattice resonances.

Descriptors enable fast, precise metasurface design.

Abstract

Structural disorder can improve the optical properties of metasurfaces, whether it is emerging from some large-scale fabrication methods, or explicitly designed and built lithographically. Correlated disorder, induced by a minimum inter-nanostructure distance or by hyperuniformity properties, is particularly beneficial in some applications such as light extraction. We introduce numerical descriptors inspired from topology to provide quantitative measures of disorder whose universal properties make them suitable for both uncorrelated and correlated disorder, where statistical descriptors are less accurate. We prove theoretically and experimentally the accuracy of these topological descriptors of disorder by using them to design plasmonic metasurfaces of controlled disorder, that we correlate to the strength of their surface lattice resonances. These tools can be used for the fast and accurate design of disordered metasurfaces, or to help tuning large-scale fabrication methods.