Two-dimensional optoelectronic neuromorphic hardware for visual in-sensor computing: Mechanisms, integration, and event-driven perception

Visual in-sensor computing (ISC) provides a promising route for reducing redundant data transfer and enabling low-power visual intelligence at the sensor front end. Two-dimensional (2D) material-enabled optoelectronic neuromorphic devices are well suited for this paradigm because their light-matter interaction, tunable interfaces, defect states, ionic dynamics, and anisotropic responses can couple optical sensing with conductance modulation and memory. However, the field is still dominated by diverse material systems, isolated device demonstrations, and application-specific reports. A clear framework is needed to connect material properties, photoinduced mechanisms, device architectures, array integration, and visual ISC functions. In this review, recent progress in 2D optoelectronic neuromorphic devices is reorganized from this cross-scale perspective. The discussion highlights how 2D and quasi-2D material platforms enable light-induced synaptic plasticity, how different device architectures translate these mechanisms into programmable conductance states, and how array-level systems support visual preprocessing, dynamic perception, multidimensional encoding, and pattern recognition. Particular attention is given to the transition from single-device proof-of-concept studies to array-level and task-level visual ISC. Key challenges are further analyzed, including material nonuniformity, device drift, array variability, readout noise, limited benchmarking, and insufficient real-scene validation. This review aims to clarify the development path of 2D optoelectronic neuromorphic hardware toward scalable, reliable, and energy-efficient visual ISC systems.