Exploring 400 Gbps/λ and beyond with AI-accelerated silicon photonic slow-light technology

作者：Changhao Han, Qipeng Yang, Jun Qin, Yan Zhou, Zhao Zheng, Yunhao Zhang, Haoren Wang, Yu Sun, Junde Lu, Yimeng Wang, Zhangfeng Ge, Yichen Wu, Lei Wang, Zhixue He, Shaohua Yu, Weiwei Hu, Chao Peng, Haowen Shu, John E. Bowers, Xingjun Wang · 发表于：Nature Communications · 年份：2025 · DOI：10.1038/s41467-025-61933-5 · 被引用次数：21 · 研究领域：Photonic and Optical Devices、Neural Networks and Reservoir Computing、Optical Network Technologies

Abstract Silicon photonics is a promising platform for the extensive deployment of optical interconnections, with the feasibility of low-cost and large-scale production at the wafer level. However, the intrinsic efficiency-bandwidth trade-off and nonlinear distortions of pure silicon modulators result in the transmission limits, which raises concerns about the prospects of silicon photonics for ultrahigh-speed scenarios. Here, we propose an artificial intelligence (AI)-accelerated silicon photonic slow-light technology to explore 400 Gbps/ λ and beyond transmission. By utilizing the artificial neural network, we achieve a data capacity of 3.2 Tbps based on an 8-channel wavelength-division-multiplexed silicon slow-light modulator chip with a thermal-insensitive structure, leading to an on-chip data-rate density of 1.6 Tb/s/mm 2 . The demonstration of single-lane 400 Gbps PAM-4 transmission reveals the great potential of standard silicon photonic platforms for next-generation optical interfaces. Our approach increases the transmission rate of silicon photonics significantly and is expected to construct a self-optimizing positive feedback loop with computing centers through AI technology.