Integration of diffractive optical neural networks with electronic neural networks (Conference Presentation)

Abstract

We demonstrate significant improvements in the inference accuracy of diffractive optical neural networks and report that a five-layer, phase-only (or amplitude/phase) modulation diffractive network can achieve 97.18% (97.81%) and 89.13% (89.32%) blind-testing accuracy for MNIST and Fashion-MNIST datasets, respectively. Moreover, the integration of diffractive neural networks with electronic deep neural networks is investigated. Using a single fully-connected layer on the electronic part and a five-layer, phase-only diffractive neural network at the optical front-end, we achieved blind-testing accuracies of 98.71% and 90.04% for MNIST and Fashion-MNIST datasets, respectively, despite a >7.8-fold reduction in the number of pixels at the opto-electronic sensor-array.