Learning and Avoiding Disorder in Multimode Fibers

Multimode optical fibers (MMFs) have gained renewed interest in the past decade, emerging as a way to boost optical communication data rates in the context of an expected saturation of current single-mode fiber-based networks. They are also attractive for endoscopic applications, offering the possib...

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Autores principales:	Maxime W. Matthès, Yaron Bromberg, Julien de Rosny, Sébastien M. Popoff
Formato:	article
Lenguaje:	EN
Publicado:	American Physical Society 2021
Materias:	Physics QC1-999
Acceso en línea:	https://doaj.org/article/39faac53fe4c49f086ef45737265025b
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spelling	oai:doaj.org-article:39faac53fe4c49f086ef45737265025b2021-12-02T18:03:01ZLearning and Avoiding Disorder in Multimode Fibers10.1103/PhysRevX.11.0210602160-3308https://doaj.org/article/39faac53fe4c49f086ef45737265025b2021-06-01T00:00:00Zhttp://doi.org/10.1103/PhysRevX.11.021060http://doi.org/10.1103/PhysRevX.11.021060https://doaj.org/toc/2160-3308Multimode optical fibers (MMFs) have gained renewed interest in the past decade, emerging as a way to boost optical communication data rates in the context of an expected saturation of current single-mode fiber-based networks. They are also attractive for endoscopic applications, offering the possibility to achieve a similar information content as multicore fibers, but with a much smaller footprint, thus reducing the invasiveness of endoscopic procedures. However, these advances are hindered by the unavoidable presence of disorder that affects the propagation of light in MMFs and limits their practical applications. We introduce here a general framework to study and avoid the effect of disorder in wave-based systems and demonstrate its application for multimode fibers. We experimentally find an almost complete set of optical channels that are resilient to disorder induced by strong deformations. These deformation principal modes are obtained by only exploiting measurements for weak perturbations harnessing the generalized Wigner-Smith operator. We explain this effect by demonstrating that, even for a high level of disorder, the propagation of light in MMFs can be characterized by just a few key properties. These results are made possible thanks to a precise and fast estimation of the modal transmission matrix of the fiber which relies on a model-based optimization using deep learning frameworks.Maxime W. MatthèsYaron BrombergJulien de RosnySébastien M. PopoffAmerican Physical SocietyarticlePhysicsQC1-999ENPhysical Review X, Vol 11, Iss 2, p 021060 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Physics QC1-999
spellingShingle	Physics QC1-999 Maxime W. Matthès Yaron Bromberg Julien de Rosny Sébastien M. Popoff Learning and Avoiding Disorder in Multimode Fibers
description	Multimode optical fibers (MMFs) have gained renewed interest in the past decade, emerging as a way to boost optical communication data rates in the context of an expected saturation of current single-mode fiber-based networks. They are also attractive for endoscopic applications, offering the possibility to achieve a similar information content as multicore fibers, but with a much smaller footprint, thus reducing the invasiveness of endoscopic procedures. However, these advances are hindered by the unavoidable presence of disorder that affects the propagation of light in MMFs and limits their practical applications. We introduce here a general framework to study and avoid the effect of disorder in wave-based systems and demonstrate its application for multimode fibers. We experimentally find an almost complete set of optical channels that are resilient to disorder induced by strong deformations. These deformation principal modes are obtained by only exploiting measurements for weak perturbations harnessing the generalized Wigner-Smith operator. We explain this effect by demonstrating that, even for a high level of disorder, the propagation of light in MMFs can be characterized by just a few key properties. These results are made possible thanks to a precise and fast estimation of the modal transmission matrix of the fiber which relies on a model-based optimization using deep learning frameworks.
format	article
author	Maxime W. Matthès Yaron Bromberg Julien de Rosny Sébastien M. Popoff
author_facet	Maxime W. Matthès Yaron Bromberg Julien de Rosny Sébastien M. Popoff
author_sort	Maxime W. Matthès
title	Learning and Avoiding Disorder in Multimode Fibers
title_short	Learning and Avoiding Disorder in Multimode Fibers
title_full	Learning and Avoiding Disorder in Multimode Fibers
title_fullStr	Learning and Avoiding Disorder in Multimode Fibers
title_full_unstemmed	Learning and Avoiding Disorder in Multimode Fibers
title_sort	learning and avoiding disorder in multimode fibers
publisher	American Physical Society
publishDate	2021
url	https://doaj.org/article/39faac53fe4c49f086ef45737265025b
work_keys_str_mv	AT maximewmatthes learningandavoidingdisorderinmultimodefibers AT yaronbromberg learningandavoidingdisorderinmultimodefibers AT julienderosny learningandavoidingdisorderinmultimodefibers AT sebastienmpopoff learningandavoidingdisorderinmultimodefibers
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Learning and Avoiding Disorder in Multimode Fibers

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