面向每波长每秒兆兆比特的多维硅光子引擎
Towards Terabit/$λ$/s Multidimensional Silicon Photonic Engine
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中文总结 AI 辅助
针对AI工作负载对高吞吐量、低延迟光学互连的需求，提出单片集成多组件的多维硅光子引擎，实现超1.8Tb/s/λ容量，功耗延迟较DSP降超5000倍，完成300米光纤全双工通信，为光学引擎带来范式转变。
中文摘要 AI 辅助
不断增长的人工智能（AI）工作负载推动了共封装光学（CPO）技术，该技术将光学引擎与电子组件集成在一起。光学互连可延长传输距离并降低延迟，使AI工厂中的分布式集群能够作为统一计算单元运行。然而，不断提升的数据吞吐量需要在超紧凑外形因子内实现光的更大并行化，同时保持严格的能效和延迟约束。在此，我们提出一种多维硅光子引擎，其通信容量超过1.8太比特/秒/波长。通过在单个芯片上单片集成收发器、空间和偏振（解）复用器以及光信号处理器，我们消除了 bulky（此处保留原表述，实际应为“体积庞大的”）分立（解）复用器和功耗高昂的数字信号处理（DSP）。实验中，该光子引擎可自配置以识别每光纤2、4或6个并发空间和偏振通道，同时缓解动态通道串扰。与最先进的DSP相比，我们的方法在6阶MIMO处理下，功耗和处理延迟均降低了5000倍以上。此外，我们演示了通过300米光纤的全双工、调制格式透明的片间通信。这些结果代表了未来高性能计算和AI驱动数据中心中光学引擎的范式转变。
英文摘要
Increasing artificial intelligence (AI) workloads drive co-packaged optics (CPO), which integrates optical engines with electronic components. Optical interconnects can extend transmission distances and reduce latency, allowing distributed clusters in AI factories to operate as a unified computational unit. However, escalating data throughput necessitates greater parallelization of light within ultracompact form factors while maintaining stringent energy efficiency and latency constraints. Here, we present a multidimensional silicon photonic engine that achieves a communication capacity exceeding 1.8 terabit/s/lambda/s. By monolithically integrating transceivers, spatial and polarization (de)multiplexers, and optical signal processors on a single chip, we eliminate bulky discrete (de)multiplexers and power-hungry digital signal processing (DSP). In experiments, the photonic engine can be self-configured to identify two, four, or six concurrent spatial and polarization channels per fiber while mitigating dynamic channel crosstalk. Compared with the state-of-art DSP, our approach achieves >5,000-fold reductions in both power consumption and processing latency at a MIMO processing order of six. Furthermore, we demonstrate full-duplex, modulation-format-transparent inter-chip communication over 300-meter fiber. These results represent a paradigm shift for optical engines in future high-performance computing and AI-driven data centers.
