Han Changhao, Yang Qipeng, Qin Jun, Zhou Yan, Zheng Zhao, Zhang Yunhao, Wang Haoren, Sun Yu, Lu Junde, Wang Yimeng, Ge Zhangfeng, Wu Yichen, Wang Lei, He Zhixue, Yu Shaohua, Hu Weiwei, Peng Chao, Shu Haowen, Bowers John E, Wang Xingjun
State Key Laboratory of Photonics and Communications, School of Electronics, Peking University, Beijing, China.
Department of Electrical and Computer Engineering, University of California, Santa Barbara, CA, USA.
Nat Commun. 2025 Jul 16;16(1):6547. doi: 10.1038/s41467-025-61933-5.
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.
硅光子学是实现光互连广泛应用的一个很有前景的平台，具有在晶圆级进行低成本大规模生产的可行性。然而，纯硅调制器固有的效率-带宽权衡和非线性失真导致了传输限制，这引发了人们对硅光子学在超高速场景下前景的担忧。在此，我们提出一种人工智能（AI）加速的硅光子慢光技术，以探索400 Gbps/λ及更高的传输速率。通过利用人工神经网络，我们基于具有热不敏感结构的8通道波分复用硅慢光调制器芯片实现了3.2 Tbps的数据容量，从而实现了1.
