Space‐Time Projection Enabled Ultrafast All‐Optical Diffractive Neural Network

All‐optical neural networks have advantages in higher throughput, higher speed as well as lower energy consumption compared to electrical neural networks. Optical neural networks have already shown great potential in various applications; however, the operation speed of the network is limited by the 2D detector as most optical neural networks rely on space to space projection. Here, a space to time projection approach to build diffractive deep neural network (D2NN) is proposed, which can project spatial intensity distribution into time‐domain intensity variation, thus bypassing the detection speed limit of 2D imaging device. Based on this scheme, high‐speed all‐optical logic gates are theoretically analyzed and experimentally realized. In this case, the network's operation speed is only limited by the photodetector (PD), which can reach GHz levels. Moreover, the method will show great advantage when it comes to wavelengths where 2D detectors are not achievable easily such as infrared, terahertz or microwaves. A space‐time information projection all‐optical diffractive deep neural network (D2NN) is reported. The network can project the spatial light field distribution into temporal intensity variation, thereby bypassing the detection speed limit of 2D imaging devices and achieving GHz‐level operating speed. This design lays the foundation for the development and application of a new generation of high‐speed optical neural networks.