Exploring 400 Gbps/λ and beyond with AI-accelerated silicon photonic slow-light technology
- 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
2025-07-16
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.