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      Nano–opto-electro-mechanical switches operated at CMOS-level voltages

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          Abstract

          Combining reprogrammable optical networks with complementary metal-oxide semiconductor (CMOS) electronics is expected to provide a platform for technological developments in on-chip integrated optoelectronics. We demonstrate how opto-electro-mechanical effects in micrometer-scale hybrid photonic-plasmonic structures enable light switching under CMOS voltages and low optical losses (0.1 decibel). Rapid (for example, tens of nanoseconds) switching is achieved by an electrostatic, nanometer-scale perturbation of a thin, and thus low-mass, gold membrane that forms an air-gap hybrid photonic-plasmonic waveguide. Confinement of the plasmonic portion of the light to the variable-height air gap yields a strong opto-electro-mechanical effect, while photonic confinement of the rest of the light minimizes optical losses. The demonstrated hybrid architecture provides a route to develop applications for CMOS-integrated, reprogrammable optical systems such as optical neural networks for deep learning.

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          Most cited references47

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          Electrooptical effects in silicon

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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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              Integrated lithium niobate electro-optic modulators operating at CMOS-compatible voltages

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                Author and article information

                Journal
                Science
                Science
                American Association for the Advancement of Science (AAAS)
                0036-8075
                1095-9203
                November 14 2019
                November 15 2019
                November 14 2019
                November 15 2019
                : 366
                : 6467
                : 860-864
                Article
                10.1126/science.aay8645
                31727832
                cb5decb9-6e95-4957-8601-59cd2d7bb05a
                © 2019

                http://www.sciencemag.org/about/science-licenses-journal-article-reuse

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