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    Differential Detection Improves Accuracy in Diffractive Optical Neural Networks

    Article obtained from Photonics RSS Feed.

    Improvements to an optical neural network being designed at the University of California, Los Angeles (UCLA) take advantage of the parallelization and scalability of optical-based computational systems.

    The system uses a series of 3D-printed layers with uneven surfaces that transmit or reflect incoming light. The layers have tens of thousands of pixel points (essentially artificial neurons) that form an engineered volume of material that computes all optically. Each object that is input has a unique light pathway through the 3D-fabricated layers. Detectors situated behind the layers are assigned to deduce what the input object is according to where the most light ends up after it has traveled through the layers.

    The system uses…

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    Sep, 04 2019 |

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