Owl‐Inspired Adaptive Scotopic Vision Enabled by van der Waals Optoelectronic Heterointegration

ABSTRACT Owl's hierarchical and context‐sensitive scotopic vision has promised major advances in optoelectronic synapse devices. However, the limited performance of visual artificial intelligence under ultra‐weak illumination remains a critical bottleneck for nighttime recognition in autonomous driving, where conventional sensors exhibit poor classification accuracy. Here, we developed a scene‐aware, three‐level adaptive vision system that integrates van der Waals photodetectors and optical synapses within a unified architecture. Diverse interfacial dynamics in vdW heterostructures enable reconfigurable optoelectronic characteristics, giving rise to abundant synaptic plasticity and good detection performance, including a responsivity of 10 4 A W −1 , and a specific detectivity of 10 11 Jones. On top of that, we construct a three‐level visual‐adaptive hardware system with reconfigurable learning efficiency by integrating a scene‐aware phototransistor circuit to monitor contextual environmental information, 6 × 6 optical synapse arrays for adaptive feature preprocessing, and artificial neural networks for image classification. The owl‐inspired platform achieves a classification accuracy of 84.8% in darkness and dynamically reconfigures its learning efficiency across illumination levels from 0 to 100 mW cm −2 . This work establishes a scene‐aware paradigm for bioinspired night‐vision hardware, bridging neural‐computation principles with 2D optoelectronics to advance intelligent perception in autonomous systems.