Robust and Scalable Flat‐Optics on Flexible Substrates via Evolutionary Neural Networks

In the past 20 years, flat‐optics has emerged as a promising light manipulation technology, surpassing bulk optics in performance, versatility, and miniaturization capabilities. As of today, however, this technology is yet to find widespread commercial applications. One of the challenges is obtainin...

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Autores principales:	Maksim Makarenko, Qizhou Wang, Arturo Burguete-Lopez, Fedor Getman, Andrea Fratalocchi
Formato:	article
Lenguaje:	EN
Publicado:	Wiley 2021
Materias:	flexible flat optics inverse design neural networks robust design Computer engineering. Computer hardware TK7885-7895 Control engineering systems. Automatic machinery (General) TJ212-225
Acceso en línea:	https://doaj.org/article/c4831cf3f7db405e8c7c46cebdf5f5ea
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id	oai:doaj.org-article:c4831cf3f7db405e8c7c46cebdf5f5ea
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spelling	oai:doaj.org-article:c4831cf3f7db405e8c7c46cebdf5f5ea2021-11-23T07:58:48ZRobust and Scalable Flat‐Optics on Flexible Substrates via Evolutionary Neural Networks2640-456710.1002/aisy.202100105https://doaj.org/article/c4831cf3f7db405e8c7c46cebdf5f5ea2021-11-01T00:00:00Zhttps://doi.org/10.1002/aisy.202100105https://doaj.org/toc/2640-4567In the past 20 years, flat‐optics has emerged as a promising light manipulation technology, surpassing bulk optics in performance, versatility, and miniaturization capabilities. As of today, however, this technology is yet to find widespread commercial applications. One of the challenges is obtaining scalable and highly efficient designs that can withstand the fabrication errors associated with nanoscale manufacturing techniques. This problem becomes more severe in flexible structures, in which deformations appear naturally when flat‐optics structures are conformally applied to, for example, biocompatible substrates. Herein, an inverse design platform that enables the fast design of flexible flat‐optics that maintain high performance under deformations of their original geometry is presented. The platform leverages on suitably designed evolutionary large‐scale optimizers, equipped with fast‐trained neural network predictors based on encoder decoder architectures. This approach supports the implementation of flexible flat‐optics robust to both fabrication errors or user‐defined perturbation stress. This method is validated by a series of experiments in which broadband flexible light polarizers, which maintain an average polarization efficiency of 80% over 200 nm bandwidths when measured under large mechanical deformations, are realized. These results could be helpful for the realization of a robust class of flexible flat‐optics for biosensing, imaging, and biomedical devices.Maksim MakarenkoQizhou WangArturo Burguete-LopezFedor GetmanAndrea FratalocchiWileyarticleflexible flat opticsinverse designneural networksrobust designComputer engineering. Computer hardwareTK7885-7895Control engineering systems. Automatic machinery (General)TJ212-225ENAdvanced Intelligent Systems, Vol 3, Iss 11, Pp n/a-n/a (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	flexible flat optics inverse design neural networks robust design Computer engineering. Computer hardware TK7885-7895 Control engineering systems. Automatic machinery (General) TJ212-225
spellingShingle	flexible flat optics inverse design neural networks robust design Computer engineering. Computer hardware TK7885-7895 Control engineering systems. Automatic machinery (General) TJ212-225 Maksim Makarenko Qizhou Wang Arturo Burguete-Lopez Fedor Getman Andrea Fratalocchi Robust and Scalable Flat‐Optics on Flexible Substrates via Evolutionary Neural Networks
description	In the past 20 years, flat‐optics has emerged as a promising light manipulation technology, surpassing bulk optics in performance, versatility, and miniaturization capabilities. As of today, however, this technology is yet to find widespread commercial applications. One of the challenges is obtaining scalable and highly efficient designs that can withstand the fabrication errors associated with nanoscale manufacturing techniques. This problem becomes more severe in flexible structures, in which deformations appear naturally when flat‐optics structures are conformally applied to, for example, biocompatible substrates. Herein, an inverse design platform that enables the fast design of flexible flat‐optics that maintain high performance under deformations of their original geometry is presented. The platform leverages on suitably designed evolutionary large‐scale optimizers, equipped with fast‐trained neural network predictors based on encoder decoder architectures. This approach supports the implementation of flexible flat‐optics robust to both fabrication errors or user‐defined perturbation stress. This method is validated by a series of experiments in which broadband flexible light polarizers, which maintain an average polarization efficiency of 80% over 200 nm bandwidths when measured under large mechanical deformations, are realized. These results could be helpful for the realization of a robust class of flexible flat‐optics for biosensing, imaging, and biomedical devices.
format	article
author	Maksim Makarenko Qizhou Wang Arturo Burguete-Lopez Fedor Getman Andrea Fratalocchi
author_facet	Maksim Makarenko Qizhou Wang Arturo Burguete-Lopez Fedor Getman Andrea Fratalocchi
author_sort	Maksim Makarenko
title	Robust and Scalable Flat‐Optics on Flexible Substrates via Evolutionary Neural Networks
title_short	Robust and Scalable Flat‐Optics on Flexible Substrates via Evolutionary Neural Networks
title_full	Robust and Scalable Flat‐Optics on Flexible Substrates via Evolutionary Neural Networks
title_fullStr	Robust and Scalable Flat‐Optics on Flexible Substrates via Evolutionary Neural Networks
title_full_unstemmed	Robust and Scalable Flat‐Optics on Flexible Substrates via Evolutionary Neural Networks
title_sort	robust and scalable flat‐optics on flexible substrates via evolutionary neural networks
publisher	Wiley
publishDate	2021
url	https://doaj.org/article/c4831cf3f7db405e8c7c46cebdf5f5ea
work_keys_str_mv	AT maksimmakarenko robustandscalableflatopticsonflexiblesubstratesviaevolutionaryneuralnetworks AT qizhouwang robustandscalableflatopticsonflexiblesubstratesviaevolutionaryneuralnetworks AT arturoburguetelopez robustandscalableflatopticsonflexiblesubstratesviaevolutionaryneuralnetworks AT fedorgetman robustandscalableflatopticsonflexiblesubstratesviaevolutionaryneuralnetworks AT andreafratalocchi robustandscalableflatopticsonflexiblesubstratesviaevolutionaryneuralnetworks
_version_	1718416848824827904

Robust and Scalable Flat‐Optics on Flexible Substrates via Evolutionary Neural Networks

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