Organic neuromorphic vision devices with multilevel memory for palmprint identification

Neuromorphic visual devices have emerged as a critical strategy to address the limitation of the von Neumann bottleneck. However, the role of interfacial molecular engineering-specifically the modulation of polar groups in polymer gate dielectrics-in shaping the performance of neuromorphic vision systems remains insufficiently explored. Herein, we report polarity-engineered hafnium oxide (HfO₂)-based phototransistors that synergistically achieve ultrahigh photodetection sensitivity and multilevel nonvolatile memory. By strategically tuning polar functional groups in polymer gate dielectrics [polyphenylene ether (PPO) and poly(4-vinylphenol) (PVP)] combined with HfO₂, we demonstrate an enhancement in photoresponsivity compared to traditional low-polarity dielectrics, alongside realistic emulation of synaptic plasticity. The optimized devices exhibit exceptional comprehensive performance, including an ON/OFF ratio exceeding 10⁵, cycling endurance over 700 program/erase (P/E) cycles, retention time greater than 3 × 10⁴ s, and 256 distinct conductance states (8-bit resolution), thus setting a new benchmark for multilevel memory capacity in memory devices. When integrated with classical machine learning algorithms, these phototransistors efficiently extract discriminative optoelectronic features from CASIA-palmprint database images, enabling reliable biometric authentication with accuracy above 98%. This work establishes fundamental molecular design principles for neuromorphic electronics and presents an energy-efficient paradigm for vision systems that unify sensing, memory, and in situ processing, paving the way for next-generation intelligent devices.