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

Journal: Nature communications
Published Date: Jul 16, 2025

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. 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.

Authors

  • Changhao Han
    State Key Laboratory of Photonics and Communications, School of Electronics, Peking University, Beijing, China.
  • Qipeng Yang
    State Key Laboratory of Photonics and Communications, School of Electronics, Peking University, Beijing, China.
  • Jun Qin
    Qilu Hospital of Shandong University, Department of Endocrinology, Jinan, Shandong, China.
  • Yan Zhou
    Department of Computer Science, University of Texas at Dallas, Richardson, Texas 75080, United States.
  • Zhao Zheng
    Beijing Key Laboratory of Complex Solid State Batteries & Tsinghua Center for Green Chemical Engineering Electrification, Department of Chemical Engineering, Tsinghua University, Beijing, 100084, P.R. China.
  • Yunhao Zhang
    State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, CAS, Beijing, China; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China.
  • Haoren Wang
    School of Mechanical Engineering, Shanghai Jiao Tong University, 800 Dongchuan Road, Shanghai 200240, PR China.
  • Yu Sun
    Department of Neurology, China-Japan Friendship Hospital, Beijing, China.
  • Junde Lu
    Key Laboratory of Information and Communication Systems, Ministry of Information Industry, Beijing Information Science and Technology University, Beijing, China.
  • Yimeng Wang
    Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai, China.
  • Zhangfeng Ge
    Peking University Yangtze Delta Institute of Optoelectronics, Nantong, China.
  • Yichen Wu
    Department of Electrical Engineering, University of California Los Angeles (UCLA), USA. ozcan@ucla.edu.
  • Lei Wang
    Department of Nursing, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, China.
  • Zhixue He
    Peng Cheng Laboratory, Shenzhen, China.
  • Shaohua Yu
    State Key Laboratory of Photonics and Communications, School of Electronics, Peking University, Beijing, China.
  • Weiwei Hu
    Department of Osteoporosis and Bone Disease, Shanghai Clinical Research Center of Bone Disease, Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China.
  • Chao Peng
    Bioengineering College of Chongqing University, Chongqing University Central Hospital (Chongqing Emergency Medical Center), Chongqing, China.
  • Haowen Shu
    State Key Laboratory of Photonics and Communications, School of Electronics, Peking University, Beijing, China. haowenshu@pku.edu.cn.
  • John E Bowers
    Department of Electrical and Computer Engineering, University of California, Santa Barbara, CA, USA. bowers@ece.ucsb.edu.
  • Xingjun Wang
    Department of Electronic Engineering, Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen, China. wangxingjun@tsinghua.edu.cn.

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