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基于垂直腔面发射激光器阵列的3D垂直集成光子神经网络展望。

Perspective on 3D vertically-integrated photonic neural networks based on VCSEL arrays.

作者信息

Gu Min, Dong Yibo, Yu Haoyi, Luan Haitao, Zhang Qiming

机构信息

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.

出版信息

Nanophotonics. 2023 Jan 13;12(5):827-832. doi: 10.1515/nanoph-2022-0437. eCollection 2023 Mar.

Abstract

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.

摘要

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。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0ca3/11501276/81bf331c1847/j_nanoph-2022-0437_fig_001.jpg

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