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

Tristan Madeleine; Nina Podoliak; Oleksandr Buchnev; Ingrid Membrillo Solis; Giampaolo D'Alessandro; Jacek Brodzki; Malgosia Kaczmarek

arXiv · 2306.13540

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

Tristan Madeleine·Nina Podoliak·Oleksandr Buchnev·Ingrid Membrillo Solis·Giampaolo D'Alessandro·Jacek Brodzki·Malgosia Kaczmarek

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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.

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physics.optics cond-mat.dis-nn math.AT

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Tristan Madeleine, Nina Podoliak, Oleksandr Buchnev, Ingrid Membrillo Solis, Giampaolo D'Alessandro, Jacek Brodzki, Malgosia Kaczmarek. 2023-06-23. Topological learning for the classification of disorder: an application to the design of metasurfaces. https://arxiv.org/abs/2306.13540

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