Emerging memory devices have been demonstrated as artificial synapses for neural networks. However, the process of rewriting these synapses is often inefficient, in terms of hardware and energy usage. Herein, we present a novel surface plasmon resonance polarizer-based all-optical synapse for realizing convolutional filters and optical convolutional neural networks. The synaptic device comprises nanoscale crossed gold arrays with varying vertical and horizontal arms that respond strongly to the incident light's polarization angle. The presented synapse in an optical convolutional neural network achieved excellent performance in four different convolutional results for classifying the Modified National Institute of Standards and Technology (MNIST) handwritten digit data set. After training on 1,000 images, the network achieved a classification accuracy of over 98% when tested on a separate set of 10,000 images. This presents a promising approach for designing artificial neural networks with efficient hardware and energy consumption, low cost, and scalable fabrication.
Keywords: Optical convolutional neural network; Optical synapse; Polarizer; Surface plasmon polariton.