A mixture-density-based tandem optimization network for on-demand inverse design of thin-film high reflectors

Deep learning (DL) has emerged as a promising tool for photonic inverse design. Nevertheless, despite the initial success in retrieving spectra of modest complexity with nearly instantaneous readout, DL-assisted design methods often underperform in accuracy compared with advanced optimization techni...

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Autores principales:	Unni Rohit, Yao Kan, Han Xizewen, Zhou Mingyuan, Zheng Yuebing
Formato:	article
Lenguaje:	EN
Publicado:	De Gruyter 2021
Materias:	artificial neural networks deep learning inverse design nanophotonics optimization thin-film optics Physics QC1-999
Acceso en línea:	https://doaj.org/article/3a69ffa99f924ab8bbdc9fb92a288007
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id	oai:doaj.org-article:3a69ffa99f924ab8bbdc9fb92a288007
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spelling	oai:doaj.org-article:3a69ffa99f924ab8bbdc9fb92a2880072021-12-05T14:10:56ZA mixture-density-based tandem optimization network for on-demand inverse design of thin-film high reflectors2192-861410.1515/nanoph-2021-0392https://doaj.org/article/3a69ffa99f924ab8bbdc9fb92a2880072021-10-01T00:00:00Zhttps://doi.org/10.1515/nanoph-2021-0392https://doaj.org/toc/2192-8614Deep learning (DL) has emerged as a promising tool for photonic inverse design. Nevertheless, despite the initial success in retrieving spectra of modest complexity with nearly instantaneous readout, DL-assisted design methods often underperform in accuracy compared with advanced optimization techniques and have not proven competitive in handling spectra of practical usefulness. Here, we introduce a tandem optimization model that combines a mixture density network (MDN) and a fully connected (FC) network to inversely design practical thin-film high reflectors. The multimodal nature of the MDN gives access to infinite candidate designs described by probability distributions, which are iteratively sampled and evaluated by the FC network to allow for rapid optimization. We show that the proposed model can retrieve the reflectance spectra of 20-layer thin-film structures. More interestingly, it reproduces with high precision the periodic structures of high reflectors derived from physical principles, even though no such information is included in the training data. Improved designs with extended high-reflectance zones are also demonstrated. Our approach combines the high-efficiency advantage of DL with the optimization-enabled performance improvement, enabling efficient and on-demand inverse design for practical applications.Unni RohitYao KanHan XizewenZhou MingyuanZheng YuebingDe Gruyterarticleartificial neural networksdeep learninginverse designnanophotonicsoptimizationthin-film opticsPhysicsQC1-999ENNanophotonics, Vol 10, Iss 16, Pp 4057-4065 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	artificial neural networks deep learning inverse design nanophotonics optimization thin-film optics Physics QC1-999
spellingShingle	artificial neural networks deep learning inverse design nanophotonics optimization thin-film optics Physics QC1-999 Unni Rohit Yao Kan Han Xizewen Zhou Mingyuan Zheng Yuebing A mixture-density-based tandem optimization network for on-demand inverse design of thin-film high reflectors
description	Deep learning (DL) has emerged as a promising tool for photonic inverse design. Nevertheless, despite the initial success in retrieving spectra of modest complexity with nearly instantaneous readout, DL-assisted design methods often underperform in accuracy compared with advanced optimization techniques and have not proven competitive in handling spectra of practical usefulness. Here, we introduce a tandem optimization model that combines a mixture density network (MDN) and a fully connected (FC) network to inversely design practical thin-film high reflectors. The multimodal nature of the MDN gives access to infinite candidate designs described by probability distributions, which are iteratively sampled and evaluated by the FC network to allow for rapid optimization. We show that the proposed model can retrieve the reflectance spectra of 20-layer thin-film structures. More interestingly, it reproduces with high precision the periodic structures of high reflectors derived from physical principles, even though no such information is included in the training data. Improved designs with extended high-reflectance zones are also demonstrated. Our approach combines the high-efficiency advantage of DL with the optimization-enabled performance improvement, enabling efficient and on-demand inverse design for practical applications.
format	article
author	Unni Rohit Yao Kan Han Xizewen Zhou Mingyuan Zheng Yuebing
author_facet	Unni Rohit Yao Kan Han Xizewen Zhou Mingyuan Zheng Yuebing
author_sort	Unni Rohit
title	A mixture-density-based tandem optimization network for on-demand inverse design of thin-film high reflectors
title_short	A mixture-density-based tandem optimization network for on-demand inverse design of thin-film high reflectors
title_full	A mixture-density-based tandem optimization network for on-demand inverse design of thin-film high reflectors
title_fullStr	A mixture-density-based tandem optimization network for on-demand inverse design of thin-film high reflectors
title_full_unstemmed	A mixture-density-based tandem optimization network for on-demand inverse design of thin-film high reflectors
title_sort	mixture-density-based tandem optimization network for on-demand inverse design of thin-film high reflectors
publisher	De Gruyter
publishDate	2021
url	https://doaj.org/article/3a69ffa99f924ab8bbdc9fb92a288007
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A mixture-density-based tandem optimization network for on-demand inverse design of thin-film high reflectors

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