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      Single planar photonic chip with tailored angular transmission for multiple-order analog spatial differentiator

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          Abstract

          Analog spatial differentiation is used to realize edge-based enhancement, which plays an important role in data compression, microscopy, and computer vision applications. Here, a planar chip made from dielectric multilayers is proposed to operate as both first- and second-order spatial differentiator without any need to change the structural parameters. Third- and fourth-order differentiations that have never been realized before, are also experimentally demonstrated with this chip. A theoretical analysis is proposed to explain the experimental results, which furtherly reveals that more differentiations can be achieved. Taking advantages of its differentiation capability, when this chip is incorporated into conventional imaging systems as a substrate, it enhances the edges of features in optical amplitude and phase images, thus expanding the functions of standard microscopes. This planar chip offers the advantages of a thin form factor and a multifunctional wave-based analogue computing ability, which will bring opportunities in optical imaging and computing.

          Abstract

          The authors present a planar photonic chip, which operate as a multiple-order analog spatial differentiator. It provides a route for designing fast, power-efficient, compact and low-cost devices used in edge detection and optical image processing, thus expanding the functions of standard microscopes.

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          Most cited references43

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          All-optical machine learning using diffractive deep neural networks

          Deep learning has been transforming our ability to execute advanced inference tasks using computers. We introduce a physical mechanism to perform machine learning by demonstrating an all-optical Diffractive Deep Neural Network (D2NN) architecture that can implement various functions following the deep learning-based design of passive diffractive layers that work collectively. We create 3D-printed D2NNs that implement classification of images of handwritten digits and fashion products as well as the function of an imaging lens at terahertz spectrum. Our all-optical deep learning framework can perform, at the speed of light, various complex functions that computer-based neural networks can implement, and will find applications in all-optical image analysis, feature detection and object classification, also enabling new camera designs and optical components that perform unique tasks using D2NNs.
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            Cylindrical vector beams: from mathematical concepts to applications

            Qiwen Zhan (2009)
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              Performing mathematical operations with metamaterials.

              We introduce the concept of metamaterial analog computing, based on suitably designed metamaterial blocks that can perform mathematical operations (such as spatial differentiation, integration, or convolution) on the profile of an impinging wave as it propagates through these blocks. Two approaches are presented to achieve such functionality: (i) subwavelength structured metascreens combined with graded-index waveguides and (ii) multilayered slabs designed to achieve a desired spatial Green's function. Both techniques offer the possibility of miniaturized, potentially integrable, wave-based computing systems that are thinner than conventional lens-based optical signal and data processors by several orders of magnitude.
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                Author and article information

                Contributors
                dgzhang@ustc.edu.cn
                Journal
                Nat Commun
                Nat Commun
                Nature Communications
                Nature Publishing Group UK (London )
                2041-1723
                26 December 2022
                26 December 2022
                2022
                : 13
                : 7944
                Affiliations
                [1 ]GRID grid.59053.3a, ISNI 0000000121679639, Advanced Laser Technology Laboratory of Anhui Province, Department of Optics and Optical Engineering, , University of Science and Technology of China, ; Hefei, Anhui 230026 China
                [2 ]GRID grid.59053.3a, ISNI 0000000121679639, Hefei National Laboratory, , University of Science and Technology of China, ; Hefei, 230088 China
                Author information
                http://orcid.org/0000-0001-8711-4593
                http://orcid.org/0000-0003-3610-0903
                http://orcid.org/0000-0002-8364-4738
                http://orcid.org/0000-0003-1230-8742
                Article
                35588
                10.1038/s41467-022-35588-5
                9792592
                36572704
                ca7c285e-bf52-4843-a29e-7a8299a749f9
                © The Author(s) 2022

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 28 July 2022
                : 9 December 2022
                Funding
                Funded by: National Key Research and Development Program of China (2021YFA1400700), the National Nature Science Foundation of China (grant nos. 12134013 and 62127818), the Hefei Municipal Natural Science Foundation (grant no. 2021007), the Key Research & Development Program of Anhui Province (202104a05020010), and the Fundamental Research Funds for the Central Universities (WK2340000109), Inovation Program for Quantum Science and Technology (No. 2021ZD0303301).
                Categories
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                © The Author(s) 2022

                Uncategorized
                imaging and sensing,transmission light microscopy
                Uncategorized
                imaging and sensing, transmission light microscopy

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