Neuromorphic devices for intelligent visual perception

Abstract Neuromorphic visual perception, by emulating the efficient information processing mechanisms of biological vision systems and integrating innovations in materials and device architectures, offers novel solutions for artificial intelligence sensing. For instance, the incorporation of low-dimensional materials (e.g., quantum dots, carbon nanotubes, and two-dimensional materials) optimizes device optoelectronic properties, while the synergistic design of organic semiconductors and oxide materials balances flexibility with complementary metal-oxide-semiconductor (CMOS) compatibility. Representative neuromorphic devices such as memristors and neuromorphic transistors address traditional vision system bottlenecks via near-sensor and in-sensor architectures in data transmission latency and energy consumption, offering a new paradigm for highly integrated, energy-efficient real-time perception. However, critical challenges—including device non-uniformity caused by material interface defects, system instability induced by memristor conductance drift, and environmental adaptability under complex illumination—remain barriers to scalable applications. This review comprehensively examines neuromorphic visual perception devices from the perspectives of device structure, operational mechanisms, materials, and applications. It explores the pivotal roles of memristors, electrolyte-gated transistors, and other neuromorphic devices in optical signal perception and information processing, with a focus on their implementations in visual perception tasks and future prospects.