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

Topological learning for the classification of disorder: an application to the design of metasurfaces
Topological learning for the classification of disorder: an application to the design of metasurfaces
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. For example, correlated disorder, induced by a minimum inter-nanostructure distance or by hyperuniformity properties, is particularly beneficial for light extraction. Inspired by topology, we introduce numerical descriptors to provide quantitative measures of disorder with universal properties, suitable to treat both uncorrelated and correlated disorder at all length scales. The accuracy of these topological descriptors is illustrated both theoretically and experimentally by using them to design plasmonic metasurfaces with controlled disorder that we then correlate to the strength of their surface lattice resonances. These descriptors are an example of topological tools that can be used for the fast and accurate design of disordered structures or as aid in improving their fabrication methods.
Surface lattice resonance, design, disorder, metasurface, optimisation, plasmonic, topological data analysis, optimization, surface lattice resonance
1936-0851
Madeleine, Tristan
7a9d1327-0a94-4e8c-9003-d80f1778db54
Podoliak, Nina
0908b951-00a7-48a5-bc82-631640910b9c
Buchnev, Oleksandr
60cdb0d2-3388-47be-a066-61b3b396f69d
Membrillo Solis, Ingrid
c458faf5-8cdb-4618-ba90-f8a90209f20a
Orlova, Tetiana
7b190d43-4489-4d4f-89d9-75eec72030ae
van Rossem, Maria
7f83bf9e-7375-4056-8d30-6e1552e77d9f
Kaczmarek, Malgosia
408ec59b-8dba-41c1-89d0-af846d1bf327
D'Alessandro, Giampaolo
bad097e1-9506-4b6e-aa56-3e67a526e83b
Brodzki, Jacek
b1fe25fd-5451-4fd0-b24b-c59b75710543
Madeleine, Tristan
7a9d1327-0a94-4e8c-9003-d80f1778db54
Podoliak, Nina
0908b951-00a7-48a5-bc82-631640910b9c
Buchnev, Oleksandr
60cdb0d2-3388-47be-a066-61b3b396f69d
Membrillo Solis, Ingrid
c458faf5-8cdb-4618-ba90-f8a90209f20a
Orlova, Tetiana
7b190d43-4489-4d4f-89d9-75eec72030ae
van Rossem, Maria
7f83bf9e-7375-4056-8d30-6e1552e77d9f
Kaczmarek, Malgosia
408ec59b-8dba-41c1-89d0-af846d1bf327
D'Alessandro, Giampaolo
bad097e1-9506-4b6e-aa56-3e67a526e83b
Brodzki, Jacek
b1fe25fd-5451-4fd0-b24b-c59b75710543

Madeleine, Tristan, Podoliak, Nina, Buchnev, Oleksandr, Membrillo Solis, Ingrid, Orlova, Tetiana, van Rossem, Maria, Kaczmarek, Malgosia, D'Alessandro, Giampaolo and Brodzki, Jacek (2023) Topological learning for the classification of disorder: an application to the design of metasurfaces. ACS Nano, 18 (1). (doi:10.1021/acsnano.3c08776).

Record type: Article

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. For example, correlated disorder, induced by a minimum inter-nanostructure distance or by hyperuniformity properties, is particularly beneficial for light extraction. Inspired by topology, we introduce numerical descriptors to provide quantitative measures of disorder with universal properties, suitable to treat both uncorrelated and correlated disorder at all length scales. The accuracy of these topological descriptors is illustrated both theoretically and experimentally by using them to design plasmonic metasurfaces with controlled disorder that we then correlate to the strength of their surface lattice resonances. These descriptors are an example of topological tools that can be used for the fast and accurate design of disordered structures or as aid in improving their fabrication methods.

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Accepted/In Press date: 6 December 2023
e-pub ahead of print date: 18 December 2023
Published date: 2023
Additional Information: Funding Information: The authors acknowledge the use of the IRIDIS High Performance Computing Facility and associated support services at the University of Southampton, in the completion of this work. This work was supported by the Leverhulme Trust (grant RPG-2019-055). Publisher Copyright: © 2023 The Authors. Published by American Chemical Society
Keywords: Surface lattice resonance, design, disorder, metasurface, optimisation, plasmonic, topological data analysis, optimization, surface lattice resonance

Identifiers

Local EPrints ID: 485807
URI: http://eprints.soton.ac.uk/id/eprint/485807
ISSN: 1936-0851
PURE UUID: 693e1094-9b62-48de-a5d7-c035d12cbd4e
ORCID for Tristan Madeleine: ORCID iD orcid.org/0000-0001-7655-8367
ORCID for Nina Podoliak: ORCID iD orcid.org/0000-0002-3146-0355
ORCID for Ingrid Membrillo Solis: ORCID iD orcid.org/0000-0002-9209-3042
ORCID for Giampaolo D'Alessandro: ORCID iD orcid.org/0000-0001-9166-9356
ORCID for Jacek Brodzki: ORCID iD orcid.org/0000-0002-4524-1081

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Date deposited: 19 Dec 2023 18:00
Last modified: 18 Jun 2024 01:57

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Contributors

Author: Nina Podoliak ORCID iD
Author: Oleksandr Buchnev
Author: Tetiana Orlova
Author: Maria van Rossem
Author: Jacek Brodzki ORCID iD

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