Review of diffractive deep neural networks

Abstract

In 2018, a UCLA research group published an important paper on optical neural network (ONN) research in the journal Science. It developed the world’s first all-optical diffraction deep neural network (DNN) system, which can perform MNIST dataset classification tasks at near-light-speed. To be specific, the UCLA research group adopted a terahertz light source as the input, established the all-optical diffractive DNN (D2NN) model using the Rayleigh-Sommerfeld diffraction theory, optimized the model parameters using the stochastic gradient descent algorithm, and then used 3D printing technology to make the diffraction grating and built the D2NN system. This research opened a new ONN research direction. Here, we first review and analyze the development history and basic theory of artificial neural networks (ANNs) and ONNs. Second, we elaborate D2NN as holographic optical elements (HOEs) interconnected by free space light and describe the theory of D2NN. Then we cover the nonlinear research and application scenarios for D2NN. Finally, the future directions and challenges of D2NN are briefly discussed. Hopefully, our work can provide support and help to researchers who study the theory and application of D2NN in the future.