Event-Driven Sentinel–Expert Neuromorphic Vision Enabled by Polarization-Reconfigurable Organic Ferroelectric Phototransistors

In the era of the Internet of Things and edge intelligence, conventional always-on machine vision systems suffer from severe energy bottlenecks because they continuously process massively redundant spatiotemporal data. Inspired by the dual-pathway strategy of the human retina, we propose a bioinspired "Sentinel-Expert" synergetic vision system enabled by polarization-reconfigurable organic ferroelectric phototransistors based on P(VDF-TrFE). By manipulating the ferroelectric polarization states, a single device is reconfigured into three functional modes: (i) a magnocellular pathway-inspired Sentinel mode under negative polarization that exhibits short-term plasticity and fading-memory dynamics for physical reservoir computing and ultralow-power motion event detection; (ii) a parvocellular pathway-inspired Expert mode under positive polarization that provides long-term potentiation-like retention for in-sensor contrast enhancement and high-fidelity static recognition; and (iii) a Programming mode that serves as a unified hardware backend for synaptic weight updates. The system achieves 98.46% recognition accuracy in the Sentinel mode and 97.45% accuracy in the Expert mode. Notably, by exploiting the high spatiotemporal sparsity of valid events in real-world scenarios to activate the Expert mode only upon wake-up events, this event-driven strategy reduces the computational cost by ∼50× at a 1% duty cycle compared with the conventional always-on strategy.