Ultra‐Low Operating Voltage Memristors Based on Plating/Stripping Reactions

作者：Lingbo Yao, Zhurui Wang, Yanyu Sun, Xiaowei Chi, Yu Liu · 发表于：Advanced Science · 年份：2025 · DOI：10.1002/advs.202510370 · 被引用次数：3 · 研究领域：Advanced Memory and Neural Computing、Ferroelectric and Negative Capacitance Devices、Neuroscience and Neural Engineering

Current memristor technologies remain limited by instability, high operating voltage, and low switching ratio, primarily due to stochastic filament formation and defect migration. Here, a fundamentally different electrochemical mechanism is proposed through the development of a plating/stripping memristor (PSM) featuring stable, low-voltage, and bio-inspired conductance switching. Constructed with Zn/Cu electrodes and a deep eutectic gel electrolyte (DEGE), the PSM accurately emulates spike-rate-dependent plasticity and long-term synaptic dynamics. The DEGE matrix offers a corrosion-resistant, dendrite-free, and ionically homogeneous environment, facilitating gradual and programmable conductance evolution. Remarkably, the Zn/DEGE/Cu PSM exhibits switching behavior with a low-resistance state centered at 15.3 µV and dual high-resistance states at -10.0 mV and +11.1 mV, governed by electrochemical equilibrium, highlighting its sub-millivolt-level operation and energy-efficient switching characteristics. Furthermore, the Zn/DEGE/Cu PSMs are integrated into a reservoir computing framework using 4-bit pulse-encoded conductance states. When applied to pattern recognition tasks, the DEGE-based PSM system demonstrates a reliable classification accuracy of 89.3%, driven by device-derived temporal dynamics. Overall, this study establishes a new materials and mechanistic foundation for energy-efficient neuromorphic computing, bridging electrochemical reactions with biologically plausibl...