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Accurate inverse design of Fabry–Perot-cavity-based color filters far beyond sRGB via a bidirectional artificial neural network

Accurate inverse design of Fabry–Perot-cavity-based color filters far beyond sRGB via a bidirectional artificial neural network
Accurate inverse design of Fabry–Perot-cavity-based color filters far beyond sRGB via a bidirectional artificial neural network
Structural color based on Fabry–Perot (F-P) cavity enables a wide color gamut with high resolution at submicroscopic scale by varying its geometrical parameters. The ability to design such parameters that can accurately display the desired color is therefore crucial to the manufacturing of F-P cavities for practical applications. This work reports the first inverse design of F-P cavity structure using deep learning through a bidirectional artificial neural network. It enables the production of a significantly wider coverage of color space that is over 215% of sRGB with extremely high accuracy, represented by an average Δ퐸2000 value below 1.2. The superior performance of this structural color-based neural network is directly ascribed to the definition of loss function in the uniform CIE 1976-Lab color space. Over 100,000 times improvement in the design efficiency has been demonstrated by comparing the neural network to the metaheuristic optimization technique using an evolutionary algorithm when designing the famous painting of “Haystacks, end of Summer” by Claude Monet. Our results demonstrate that, with the correct selection of loss function, deep learning can be very powerful to achieve extremely accurate design of nanostructured color filters with very high efficiency.
FP cavity, inverse design, multilayer, neural network, structural color
2327-9125
B236-B246
Dai, Peng
1150a00a-e54b-438b-bf51-4e8521c07f66
Huang, Ruomeng
c6187811-ef2f-4437-8333-595c0d6ac978
De Groot, Kees
92cd2e02-fcc4-43da-8816-c86f966be90c
Muskens, Otto
2284101a-f9ef-4d79-8951-a6cda5bfc7f9
Wang, Yasi
1fd3d73d-bee4-49cd-9d86-6c7578a10492
Duan, Huigao
f26e2028-5fdb-4d42-8054-3c07ddd5325d
Dai, Peng
1150a00a-e54b-438b-bf51-4e8521c07f66
Huang, Ruomeng
c6187811-ef2f-4437-8333-595c0d6ac978
De Groot, Kees
92cd2e02-fcc4-43da-8816-c86f966be90c
Muskens, Otto
2284101a-f9ef-4d79-8951-a6cda5bfc7f9
Wang, Yasi
1fd3d73d-bee4-49cd-9d86-6c7578a10492
Duan, Huigao
f26e2028-5fdb-4d42-8054-3c07ddd5325d

Dai, Peng, Huang, Ruomeng, De Groot, Kees, Muskens, Otto, Wang, Yasi and Duan, Huigao (2021) Accurate inverse design of Fabry–Perot-cavity-based color filters far beyond sRGB via a bidirectional artificial neural network. Photonics Research, 9 (5), B236-B246. (doi:10.1364/PRJ.415141).

Record type: Article

Abstract

Structural color based on Fabry–Perot (F-P) cavity enables a wide color gamut with high resolution at submicroscopic scale by varying its geometrical parameters. The ability to design such parameters that can accurately display the desired color is therefore crucial to the manufacturing of F-P cavities for practical applications. This work reports the first inverse design of F-P cavity structure using deep learning through a bidirectional artificial neural network. It enables the production of a significantly wider coverage of color space that is over 215% of sRGB with extremely high accuracy, represented by an average Δ퐸2000 value below 1.2. The superior performance of this structural color-based neural network is directly ascribed to the definition of loss function in the uniform CIE 1976-Lab color space. Over 100,000 times improvement in the design efficiency has been demonstrated by comparing the neural network to the metaheuristic optimization technique using an evolutionary algorithm when designing the famous painting of “Haystacks, end of Summer” by Claude Monet. Our results demonstrate that, with the correct selection of loss function, deep learning can be very powerful to achieve extremely accurate design of nanostructured color filters with very high efficiency.

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Accepted/In Press date: 1 March 2021
Published date: 1 May 2021
Keywords: FP cavity, inverse design, multilayer, neural network, structural color

Identifiers

Local EPrints ID: 450060
URI: http://eprints.soton.ac.uk/id/eprint/450060
ISSN: 2327-9125
PURE UUID: 861f3e1b-ce6b-431f-a790-c26331e1fe3a
ORCID for Kees De Groot: ORCID iD orcid.org/0000-0002-3850-7101
ORCID for Otto Muskens: ORCID iD orcid.org/0000-0003-0693-5504

Catalogue record

Date deposited: 07 Jul 2021 16:31
Last modified: 15 Sep 2021 01:55

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Contributors

Author: Peng Dai
Author: Ruomeng Huang
Author: Kees De Groot ORCID iD
Author: Otto Muskens ORCID iD
Author: Yasi Wang
Author: Huigao Duan

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