Dual-Function Sb₂S₃/HfO₂ Memristor for Reservoir Computing and Neural Network Learning via Decoupled Short- and Long-Term Memory

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 decoupling and 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 (PPF/PPD), tunable excitatory postsynaptic currents (EPSCs), and highly linear long-term potentiation/depression (LTP/LTD). Subsequently, utilizing the LTM characteristics of the device, a non-volatile synaptic array is built to implement a fully connected neural network, achieving 94.5% accuracy. Meanwhile, a Physical Reservoir Computing (RC) 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 units toward multifunctional intelligent processors.