Dual‐Mode Nanoporous SiO ₂ Memristors with Coexisting Volatile and Nonvolatile Dynamics for Reservoir Computing

ABSTRACT 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 SiO 2 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 SiO 2 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.