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      All-Optical Machine Learning Using Diffractive Deep Neural Networks

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          Abstract

          We introduce an all-optical Diffractive Deep Neural Network (D2NN) architecture that can learn to implement various functions after deep learning-based design of passive diffractive layers that work collectively. We experimentally demonstrated the success of this framework by creating 3D-printed D2NNs that learned to implement handwritten digit classification and the function of an imaging lens at terahertz spectrum. With the existing plethora of 3D-printing and other lithographic fabrication methods as well as spatial-light-modulators, this 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 can learn to perform unique tasks using D2NNs.

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          Author and article information

          Journal
          14 April 2018
          Article
          1804.08711
          4641c8bf-fab0-40a2-8dc5-1453e14bd9af

          http://arxiv.org/licenses/nonexclusive-distrib/1.0/

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          Custom metadata
          cs.NE cs.LG physics.comp-ph physics.optics

          Mathematical & Computational physics,Optical materials & Optics,Neural & Evolutionary computing,Artificial intelligence

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