All-optical information-processing capacity of diffractive surfaces

Optical computing: the potential of layered diffractive surfaces Layers of materials that diffract light with variable spacing between them can be adjusted or “trained” to perform information-processing tasks using light alone. Diffraction is the alteration of the propagation of light waves by struc...

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Main Authors: Onur Kulce, Deniz Mengu, Yair Rivenson, Aydogan Ozcan
Format: Article
Language:English
Published: Nature Publishing Group 2021-01-01
Series:Light: Science & Applications
Online Access:https://doi.org/10.1038/s41377-020-00439-9
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author Onur Kulce
Deniz Mengu
Yair Rivenson
Aydogan Ozcan
author_facet Onur Kulce
Deniz Mengu
Yair Rivenson
Aydogan Ozcan
author_sort Onur Kulce
collection DOAJ
description Optical computing: the potential of layered diffractive surfaces Layers of materials that diffract light with variable spacing between them can be adjusted or “trained” to perform information-processing tasks using light alone. Diffraction is the alteration of the propagation of light waves by structural features of the materials they encounter. Aydogan Ozcan and colleagues at the University of California, Los Angeles, USA, performed an analysis of optical neural networks composed of spatially engineered diffractive surfaces. They explored the power of multilayered networks to perform optical processing tasks, including image recognition and classification. They also determined mathematical rules describing the performance limits of the networks in relation to the number of diffractive surfaces they contained. Their work is relevant to various diffractive surfaces, including metasurfaces patterned with features smaller than the wavelength of light, and plasmonic materials governed by the coherent behavior of surface electrons.
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spelling doaj.art-8ea035f41ea24dbbb26de026d0dc3dfe2022-12-22T04:06:04ZengNature Publishing GroupLight: Science & Applications2047-75382021-01-0110111710.1038/s41377-020-00439-9All-optical information-processing capacity of diffractive surfacesOnur Kulce0Deniz Mengu1Yair Rivenson2Aydogan Ozcan3Electrical and Computer Engineering Department, University of CaliforniaElectrical and Computer Engineering Department, University of CaliforniaElectrical and Computer Engineering Department, University of CaliforniaElectrical and Computer Engineering Department, University of CaliforniaOptical computing: the potential of layered diffractive surfaces Layers of materials that diffract light with variable spacing between them can be adjusted or “trained” to perform information-processing tasks using light alone. Diffraction is the alteration of the propagation of light waves by structural features of the materials they encounter. Aydogan Ozcan and colleagues at the University of California, Los Angeles, USA, performed an analysis of optical neural networks composed of spatially engineered diffractive surfaces. They explored the power of multilayered networks to perform optical processing tasks, including image recognition and classification. They also determined mathematical rules describing the performance limits of the networks in relation to the number of diffractive surfaces they contained. Their work is relevant to various diffractive surfaces, including metasurfaces patterned with features smaller than the wavelength of light, and plasmonic materials governed by the coherent behavior of surface electrons.https://doi.org/10.1038/s41377-020-00439-9
spellingShingle Onur Kulce
Deniz Mengu
Yair Rivenson
Aydogan Ozcan
All-optical information-processing capacity of diffractive surfaces
Light: Science & Applications
title All-optical information-processing capacity of diffractive surfaces
title_full All-optical information-processing capacity of diffractive surfaces
title_fullStr All-optical information-processing capacity of diffractive surfaces
title_full_unstemmed All-optical information-processing capacity of diffractive surfaces
title_short All-optical information-processing capacity of diffractive surfaces
title_sort all optical information processing capacity of diffractive surfaces
url https://doi.org/10.1038/s41377-020-00439-9
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