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 (HfO 2 )-based phototransistors that synergistically achieve ultrahigh photodetection sensitivity (photoresponsivity >10 4 A/W) and multilevel nonvolatile memory. By strategically tuning polar functional groups in polymer gate dielectrics [polyphenylene ether and poly(4-vinylphenol)] combined with HfO 2 , we demonstrate a tenfold enhancement in photoresponsivity compared to traditional low-polarity dielectrics, alongside realistic emulation of synaptic plasticity. The optimized devices exhibit exceptional comprehensive performance including ON/OFF ratio exceeding 10 5 , cycling endurance over 700 program/erase (P/E) cycles, retention time greater than 3×10 4 s, and 256 distinct conductance states (8-bit resolution)-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.