A programmable diffractive deep neural network based on a digital-coding metasurface array

作者：Che Liu, Qian Ma, Z. Luo, Q. Hong, Qiang Xiao, Hao Chi Zhang, Long Miao, W. Yu, Q. Cheng, Lianlin Li, T. Cui · 发表于：Nature Electronics · 年份：2022 · DOI：10.1038/s41928-022-00719-9 · 被引用次数：607

The development of artificial intelligence is typically focused on computer algorithms and integrated circuits. Recently, all-optical diffractive deep neural networks have been created that are based on passive structures and can perform complicated functions designed by computer-based neural networks. However, once a passive diffractive deep neural network architecture is fabricated, its function is fixed. Here we report a programmable diffractive deep neural network that is based on a multi-layer digital-coding metasurface array. Each meta-atom on the metasurfaces is integrated with two amplifier chips and acts an active artificial neuron, providing a dynamic modulation range of 35 dB (from −22 dB to 13 dB). We show that the system, which we term a programmable artificial intelligence machine, can handle various deep learning tasks for wave sensing, including image classification, mobile communication coding–decoding and real-time multi-beam focusing. We also develop a reinforcement learning algorithm for on-site learning and a discrete optimization algorithm for digital coding. Using a multi-layer metasurface array in which each meta-atom of the metasurface acts as an active artificial neuron, a programmable diffractive deep neural network can be created that directly processes electromagnetic waves in free space for wave sensing and wireless communications.