Organic neuromorphic vision devices with multilevel memory for palmprint identification

Abstract

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.