Abstract
Artificial neuromorphic vision systems emulate the biological visual pathway by integrating sensing, storage, and information processing within a unified architecture. Featuring high speed, low power consumption, and superior temporal resolution, they demonstrate significant potential in fields such as autonomous driving, facial recognition, and intelligent perception. As the core building block, the optoelectronic synapse plays a decisive role in determining system performance, which is closely related to its material composition, structural design, and functional characteristics. This review systematically summarizes recent progress in optoelectronic synaptic materials, device architectures, and performance evaluation methodologies. Furthermore, it explores the working mechanisms and network architectures of optoelectronic synapse-based neuromorphic vision systems, highlighting their capability in image perception, information storage, and target recognition. Current challenges, including environmental stability, large-scale array fabrication, chip-level integration, and adaptability of visual functions to real-world scenarios, are discussed in depth. Finally, the review provides an outlook on future development trends toward stable, scalable, and highly integrated optoelectronic neural vision systems, underscoring their key importance in next-generation intelligent sensing and information-processing technologies.