Gaolei Hu | Searcharxiv

arXiv2026

Real-Time Dynamic Crosstalk Tracking and Compensation via Photonic Blind Source Separation

Space-division multiplexing in few-mode fibers can substantially increase optical-link capacity, but its practical deployment is hindered by dynamically varying inter-modal crosstalk induced by environmental perturbations. Although silicon photonic reconfigurable mode processors have been widely demonstrated, continuous adaptation during high-speed intensity-modulation direct-detection (IM/DD) transmission remains challenging. Here, we experimentally demonstrate real-time tracking and compensation of dynamically varying inter-modal crosstalk using a hybrid photonic--electronic framework that combines an integrated silicon photonic processor with an FPGA-based control backend. The photonic processor performs signal separation in the optical domain, while the FPGA extracts signal statistics and implements a blind source separation (BSS)-based feedback algorithm without requiring dedicated training sequences. A two-stage optimization strategy combines rapid crosstalk suppression with stable continuous tracking under dynamic channel variations. We demonstrate photonic blind source separation for signals up to 100 GBaud and FPGA-enabled continuous adaptation for two 64 GBaud data channels in a few-mode-fiber transmission system. The system maintains bit-error rates below $10^{-4}$ under dynamic crosstalk, with a per-update control latency of 4 ms. These results establish a practical route toward low-latency, hardware-efficient adaptive photonic front ends for dynamic high-speed IM/DD space-division-multiplexed links.

physics.optics↗

arXiv2024

A 103-TOPS/mm$^2$ Integrated Photonic Computing Engine Enabling Next-Generation Reservoir Computing

Reservoir computing (RC) is a leading machine learning algorithm for information processing due to its rich expressiveness. A new RC paradigm has recently emerged, showcasing superior performance and delivering more interpretable results with shorter training data sets and training times, representing the next generation of RC computing. This work presents the first realization of a high-speed next-generation RC system on an integrated photonic chip. Our experimental results demonstrate state-of-the-art forecasting and classification performances under various machine learning tasks and achieve the fastest speeds of 60 Gbaud and a computing density of 103 tera operations/second/mm$^2$ (TOPS/mm$^2$). The passive system, composed of a simple star coupler with on-chip delay lines, offers several advantages over traditional RC systems, including no speed limitations, compact footprint, extremely high fabrication error tolerance, fewer metaparameters, and greater interpretability. This work lays the foundation for ultrafast on-chip photonic RC, representing significant progress toward developing next-generation high-speed photonic computing and signal processing.

cs.ET↗