Amphibian-inspired neuromorphic dynamic vision systems based on ferroelectric field-effect transistor

The dynamic vision sensor captures visual information as discrete events, enabling high-speed imaging with reduced data redundancy, but is limited by lack of color sensitivity, a speed-noise trade-off, and inefficient data transfer. Here we show an amphibian-inspired dynamic vision system (ADVS) based on ferroelectric field-effect transistors that emulates the hierarchical functions of amphibian retinas, including spectral perception, spatial preprocessing, and event-driven neural encoding. The ferroelectric transistors exhibit broadband photosensitivity (365-637 nm) and bidirectional photoresponses, enabling multichannel spectral recognition from ultraviolet to visible light. Device arrays further reproduce center-surround receptive-field-like processing, enhancing spatial contrast while suppressing background noise under weak illumination. Owing to the steep switching characteristics (SS_min = 53.8 mV dec^-1) of the transistors, the system also supports microsecond-scale event-driven spiking responses. Combined with bioinspired hierarchical preprocessing framework and event-driven convolutional neural network, the ADVS achieves 96.5% accuracy in dynamic facial expression recognition and real-time multi-agent trajectory prediction with <5% error.