Dual-function Sb2S3/HfO2 memristor for reservoir computing and neural network learning via decoupled short- and long-term memory

Abstract

Conventional computing architectures typically rely on separate devices to achieve dynamic sensing and long-term storage, leading to low integration density, high energy consumption, and significant data movement bottlenecks. Here, a biomimetic dual-function memristor based on an Sb2S3/HfO2 heterostructure is proposed, in which synergistic regulation of ion migration and electronic transport enables the materials-assisted decoupling and coordinated integration of short-term memory (STM) and long-term memory (LTM) functions within a single device. The device successfully emulates various biological synaptic behaviors, including paired-pulse facilitation/depression, tunable excitatory postsynaptic currents (EPSCs), and highly linear long-term potentiation/depression. Subsequently, utilizing the LTM characteristics of the device, a nonvolatile synaptic array is built to implement a fully connected neural network, achieving 94.5% accuracy. Meanwhile, a physical reservoir computing system is constructed using the STM dynamics to directly encode and recognize spatiotemporal features in iris image sequences, achieving 98% accuracy. Through coordinated innovation in materials, devices, and architecture, this work advances memristors from single-function memory elements toward multifunctional, all-electrical intelligent processing units.