Some rules of the diffractive deep neural network (D^ 2NN) are discovered. They reveal that
the inner product of any two optical fields in D^ 2NN is invariant and the D^ 2NN acts as a …

To alleviate the problems of long training time and computational resource consumption in
diffractive deep neural networks (D 2 NNs), we present the 2bit nonlinear diffractive deep …

A photonic artificial intelligence chip is based on an optical neural network (ONN), low
power consumption, low delay, and strong antiinterference ability. The all‐optical diffractive …

As an all-optical learning framework, diffractive deep neural networks (D^ 2NNs) have great
potential in running speed, data throughput, and energy consumption. The depth of …

Optical neural networks (ONNs) enable high speed, parallel, and energy efficient processing
compared to their conventional digital electronic counterparts. However, realizing large …

Large-scale, highly integrated and low-power-consuming hardware is becoming
progressively more important for realizing optical neural networks (ONNs) capable of …

Optical neural networks have significant advantages in terms of power consumption,
parallelism, and high computing speed, which has intrigued extensive attention in both …

As a successful case of combining deep learning with photonics, the research on optical
machine learning has recently undergone rapid development. Among various optical …

Deep learning has been transformative in many fields, motivating the emergence of various
optical computing architectures. Diffractive optical network is a recently introduced optical …