Dual-Mode Nanoporous SiO₂ Memristors with Coexisting Volatile and Nonvolatile Dynamics for Reservoir Computing

Energy-efficient and adaptive neuromorphic hardware requires material platforms that can intrinsically integrate transient neural dynamics with stable long-term memory within a single device architecture. Here, a cross-point nanoporous SiO₂ memristor that unifies volatile and nonvolatile switching behaviors within a single material platform is reported. The engineered nanoporous framework provides well-defined ion migration pathways, enabling controlled modulation of conductive filaments and reversible transitions between short-term plasticity (STP) and long-term plasticity (LTP) through simple compliance-current tuning. Leveraging this dual-mode functionality, the volatile dynamics of the nanoporous SiO₂ memristors are employed directly as a physical reservoir, while the nonvolatile conductance states serve as synaptic weights in the readout layer. Using a conductance-aware training scheme, reservoir computing (RC) is demonstrated on the same device platform, achieving 93.7% accuracy in MNIST handwritten-digit recognition. Beyond standard benchmark datasets, the system further enables ECG temporal biosignal classification, reaching over 88% accuracy in distinguishing normal and abnormal heartbeat patterns. These results establish a single-material, CMOS-compatible neuromorphic platform capable of integrating dynamic processing with persistent memory, offering a scalable and low-power pathway toward compact intelligent edge computing hardware.