Spike-timing-dependent plasticity and synaptic consolidation in Hfo₂ memristors for adaptive neuromorphic computing

Abstract In this work, we demonstrate the potential of HfO₂-based memristors as artificial synapses capable of reproducing biologically plausible spike-timing-dependent plasticity (STDP). W/HfO₂/Ti/TiN devices were fabricated and characterized, exhibiting reliable bipolar resistive switching, stable endurance, and reproducible resistance states across multiple cells and devices. The excitatory postsynaptic current (EPSC) response under sequential voltage pulses revealed gradual potentiation, depression, and saturation dynamics, closely resembling long-term potentiation, long-term depression, and synaptic consolidation in biological systems. Furthermore, the memristors successfully emulated higher-order learning rules, including triplet-STDP and frequency-dependent plasticity, while maintaining robust performance under biologically realistic noise conditions, exhibiting less than ±2% variation under voltage perturbations and ±2.5% under spike-timing jitter across 25 trials. A compact physical model captured the interplay between vacancy-driven filament dynamics and time-dependent weight modulation, yielding STDP curves consistent with experimental observations in neuroscience. These findings highlight HfO₂ memristors as promising candidates for neuromorphic computing, providing not only a faithful hardware realization of synaptic learning but also compatibility with large-scale, CMOS-integrated architectures for next-generation cognitive processors.