A review: Photonics devices, architectures, and algorithms for optical neural computing

• Published in: Journal of semiconductors 2021-02, Vol.42 (2), p.23105-82
• Main Authors: Xiang, Shuiying, Han, Yanan, Song, Ziwei, Guo, Xingxing, Zhang, Yahui, Ren, Zhenxing, Wang, Suhong, Ma, Yuanting, Zou, Weiwen, Ma, Bowen, Xu, Shaofu, Dong, Jianji, Zhou, Hailong, Ren, Quansheng, Deng, Tao, Liu, Yan, Han, Genquan, Hao, Yue
• Format: Article
• Language: English
• ISSN: 1674-4926; 2058-6140
• DOI: 10.1088/1674-4926/42/2/023105

The explosive growth of data and information has motivated various emerging non-von Neumann computational approaches in the More-than-Moore era. Photonics neuromorphic computing has attracted lots of attention due to the fascinating advantages such as high speed, wide bandwidth, and massive parallelism. Here, we offer a review on the optical neural computing in our research groups at the device and system levels. The photonics neuron and photonics synapse plasticity are presented. In addition, we introduce several optical neural computing architectures and algorithms including photonic spiking neural network, photonic convolutional neural network, photonic matrix computation, photonic reservoir computing, and photonic reinforcement learning. Finally, we summarize the major challenges faced by photonic neuromorphic computing, and propose promising solutions and perspectives.