Electrospinning‐Driven IGZO Nanofiber Phototransistors Bridging Adaptive Processing and Recognition Learning

ABSTRACT The development of neuromorphic electronics with visual perception and adaptive capability is highly desirable for advancing artificial vision systems. Herein, we have demonstrated a dual‐plasticity adaptive phototransistor based on IGZO nanofibers that exhibits the perception and dynamic adaptation behavior of rod and cone cells to varying light environments. Benefiting from the coexistence of oxygen vacancies and trap states in nanofibers, the separation and injection of photogenerated carriers are significantly improved, thereby enabling the light‐intensity‐dependent dynamic adaptability and the gate‐modulated photosensitivity with a narrower adaptation timescale than the bio‐systems (< 2 min). Moreover, the phototransistor array replicates both photopic and scotopic adaptation, and achieves adaptive contrast enhancement for the overexposed images. Finally, the system enables real‐time neuromorphic encoding and recognition of digital signals under varying adaptive processes, and the pattern recognition accuracy is significantly improved from 10% to 95.8%. These results not only demonstrate a facile route for bridging visual sensing, adaptive processing, and neuromorphic computing in a single transistor device but also lay the groundwork for future applications in machine vision and next‐generation neuromorphic sensory systems.