Metaplasticity and Reservoir Computing in Bio‐Realistic Artificial Synapses with Embedded Localized Au‐Nanoparticle‐Based Memristors
Chandreswar Mahata, Gimun Kim, Hyojin So, Muhammad Ismail, Chih‐Chieh Hsu, Sungjoon Kim, Sungjun Kim, Sungjun Kim, Sungjun Kim
Abstract
Abstract 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 10 4 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.