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.

Information is acquired by humans in dynamically changing environments, with vision acting as the pathway for 80% of information intake. As the core of the visual system, the adaptive mechanism enables humans to flexibly respond to complex and variable lighting conditions. In recent years, biological adaptive mechanisms such as light/dark and chromatic adaptation have been the focus of neuromorphic devices inspired by the visual pathway, intending to overcome static perception limitations and build artificial vision systems capable of dynamic environmental adaptation. This paper systematically reviews the material systems and biomimetic mechanisms of visual adaptive neuromorphic devices. The optoelectronic and memory performances of devices are discussed to provide references for achieving efficient adaptive functions. In addition, application scenarios of artificial vision based on visual adaptive neuromorphic devices are summarized, and their prospects and challenges are deeply explored to offer insights for developing high-performance biomimetic vision-adaptive devices.

Hafnium oxide (HfO₂)-based ferroelectric memory devices have emerged as promising CMOS-compatible nonvolatile memories, yet ferroelectric synaptic devices still suffer from polarization-state read-disturb issues and abrupt polarization variations in neuromorphic computing applications. Here, we demonstrate a ferroelectric synaptic device with highly linear and continuously tunable multilevel polarization states as well as improved resistance to read-disturb issues through electrically driven oxygen-vacancy migration in Hf_0.5Zr_0.5O₂ (HZO) devices. Using an oxygen-active indium tin oxide (ITO) electrode, electric-field cycling induces interfacial oxygen-vacancy redistribution, leading to vacancy compensation and interface stabilization. Spectroscopic and nanoscale analyses reveal field-driven redox processes, including Ti³⁺ to Ti⁴⁺ transformation and three-dimensional oxygen-vacancy migration across the interface. This dynamic defect regulation enhances remanent polarization (P_r), increases breakdown strength, and enables robust endurance. Importantly, controlled vacancy migration allows continuous P_r modulation, supporting multilevel analog synaptic behavior with high uniformity and reversibility. Implemented in a 1T1C architecture, the device exhibits a large and linear memory window with excellent retention. These results establish electrically driven oxygen vacancy engineering as an effective post-fabrication strategy for high-reliability ferroelectric synaptic devices.