Metaplasticity and Reservoir Computing in Bio-Realistic Artificial Synapses with Embedded Localized Au-Nanoparticle-Based Memristors

This research reports on the control of short-term and long-term memory in transition metal oxides embedded with localized gold nanoparticles (Au-NPs). The HfTiO_x/TiSiO_x switching layer, after the orderly and uniform insertion of Au-NPs, demonstrates uniform cycle-to-cycle DC switching with an ON–OFF ratio >10. Stable low-resistance states (LRS) and high-resistance state (HRS) are maintained up to 10⁴ s with multilevel memory characteristics due to the control of oxygen vacancy concentrations. The localized Au-NPs enhance the local electric field near the HfTiO_x/Au-NP interface, forming controlled conductive filaments, while the high concentration of oxygen vacancies creates a permanent conduction path inside TiSiO_x after the electroforming process. The ITO/HfTiO_x/Au-NP/TiSiO_x/TaN memristor exhibits stable, controllable gradual bipolar switching and mimics several biological memory functions, including pulse-width-dependent plasticity, spike-timing-dependent plasticity, pulse-frequency-dependent plasticity, and experience-dependent plasticity. Additionally, a performance of 50k SET/RESET cycles without any significant degradation is achieved and the facilitation of long-term potentiation/depression are demonstrated. With the help of controlled oxygen vacancy generation on the surface of Au-NP inside the HfTiO_x/TiSiO_x switching layer, the memristor can emulate metaplasticity. Evaluation of a reservoir computing system utilizing the volatile switching of the memristor shows efficient processing of temporal data information which is essential for neuromorphic systems.