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 (${{\rm{D}}^2}{\rm{NN}}$) 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 ${{\rm{D}}^2}{\rm{NN}}$ 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 ${{\rm{D}}^2}{\rm{NN}}$ as holographic optical elements (HOEs) interconnected by free space light and describe the theory of ${{\rm{D}}^2}{\rm{NN}}$. Then we cover the nonlinear research and application scenarios for ${{\rm{D}}^2}{\rm{NN}}$. Finally, the future directions and challenges of ${{\rm{D}}^2}{\rm{NN}}$ are briefly discussed. Hopefully, our work can provide support and help to researchers who study the theory and application of ${{\rm{D}}^2}{\rm{NN}}$ in the future.

© 2023 Optica Publishing Group

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