Traditional optical communication networks often face challenges such as mode incompatibility, crosstalk, and complex interconnection when integrating diverse fiber types, including multimode fibers (MMFs) and ring-core fibers (RCFs). With the growing demand for transmission capacity and flexibility in data centers and high-speed communication systems, achieving efficient interconnection and dynamic switching between fibers of different structures has become a key bottleneck in optical communication technology.

Wireless communication has long been essential for transmitting mission-critical information across both military and civil domains. From ancient fire signals, smoke beacons, ship flags, and semaphore telegraph messages to modern satellite networks, wireless communication technologies have continuously evolved to overcome limitations in propagation range, transmission latency, and spectral efficiency. The growing demand for high-volume data transmission and the increasing congestion of the radio frequency spectrum have accelerated the development of free-space optical (FSO) communication technologies. FSO communication has the potential to provide high-speed, high-throughput, and spectrum-unregulated channels that can serve as the backbone of future integrated terrestrial, airborne, and space networks. However, despite their advantages, the performance of FSO systems remains critically sensitive to background noise—most notably solar radiation—as well as atmospheric fluctuations, which collectively compromise their operational robustness. This susceptibility is exacerbated in satellite-to-ground links wherein intensity modulation techniques are widely adopted, as signal strength is directly exposed to fluctuations in background light.

Wearable biosensors integrating flexible substrates with biosensing components demonstrate remarkable advantages including lightweight construction, stretchability, and biocompatibility, enabling conformal skin attachment or textile integration for continuous, noninvasive physiological monitoring. Sweat, containing abundant biomarkers such as glucose, lactate, uric acid, and electrolytes, holds significant potential for disease diagnosis, health management, and sports medicine applications. However, conventional sweat analysis techniques, such as electrochemical methods (susceptible to signal interference and electrode passivation) and colorimetric approaches (limited sensitivity and stability), exhibit inherent limitations. Surface-enhanced Raman scattering (SERS) technology has emerged as an optimal solution owing to its ultra-high sensitivity, multiplexing capability, and compatibility with aqueous samples. While traditional rigid SERS substrates lack mechanical flexibility for wearable applications, flexible SERS platforms combine stretchability, skin conformability, and biocompatibility, allowing seamless integration with flexible electronics for dynamic monitoring. Despite recent progress in flexible SERS-based sweat sensors, precise multiplexed quantification remains challenging due to spectral overlap and dependence on bulky instrumentation.

  Programmable photonic integrated circuits (PICs) achieve precise control over optical transmission paths through the dynamic tuning of unit structures. This capability enables software-defined, intelligent routing of optical signals, thereby supporting real-time analog signal processing. Thanks to this unique advantage, programmable PICs demonstrate broad application prospects in fields such as wavelength routing, optical neural networks, and microwave photonics. Therefore, with increasingly complex and diverse application scenarios, the trend toward large-scale integration of programmable PICs has become inevitable. This brings a critical challenge: how to achieve global optimal configuration of hundreds to thousands of control units to meet the demands of multifunctional signal processing. Traditional optimization algorithms (such as the Dijkstra algorithm, genetic algorithms, etc.) often struggle with effective convergence due to their exponentially increasing computational complexity. Therefore, it is imperative to develop intelligent computing models that align with the structural characteristics of PICs, enabling significant enhancements in system reconfiguration capabilities and accelerating the practical deployment of large-scale programmable PICs.