Gu Min, Dong Yibo, Yu Haoyi, Luan Haitao, Zhang Qiming
Institute of Photonic Chips, University of Shanghai for Science and Technology, Shanghai 200093 China.
Centre for Artificial-Intelligence Nanophotonics, School of Optical-Electrical and Computer Engineering, University of Shanghai for Science and Technology, Shanghai 200093 China.
Nanophotonics. 2023 Jan 13;12(5):827-832. doi: 10.1515/nanoph-2022-0437. eCollection 2023 Mar.
The rapid development of artificial intelligence has stimulated the interest in the novel designs of photonic neural networks. As three-dimensional (3D) neural networks, the diffractive neural networks (DNNs) relying on the diffractive phenomena of light, has demonstrated their superb performance in the direct parallel processing of two-dimensional (2D) optical data at the speed of light. Despite the outstanding achievements, DNNs utilize centimeter-scale devices to generate the input data passively, making the miniaturization and on-chip integration of DNNs a challenging task. Here, we provide our perspective on utilizing addressable vertical-cavity surface-emitting laser (VCSEL) arrays as a promising data input device and integrated platform to achieve compact, active DNNs for next-generation on-chip vertical-stacked photonic neural networks. Based on the VCSEL array, micron-scale 3D photonic chip with a modulation bandwidth at tens of GHz can be available. The possible future directions and challenges of the 3D photonic chip are analyzed.
人工智能的快速发展激发了人们对光子神经网络新颖设计的兴趣。作为三维（3D）神经网络，基于光的衍射现象的衍射神经网络（DNN）在以光速直接并行处理二维（2D）光学数据方面展现出了卓越的性能。尽管取得了显著成就，但DNN使用厘米级设备来被动生成输入数据，这使得DNN的小型化和片上集成成为一项具有挑战性的任务。在此，我们提出利用可寻址垂直腔面发射激光器（VCSEL）阵列作为一种有前景的数据输入设备和集成平台，以实现用于下一代片上垂直堆叠光子神经网络的紧凑、有源DNN的观点。基于VCSEL阵列，可以获得具有数十GHz调制带宽的微米级3D光子芯片。分析了3D光子芯片未来可能的发展方向和面临的挑战。
