Oxide-based memristors have been demonstrated as suitable options for memory components in neuromorphic systems. In such devices, the resistive switching characteristics are caused by the formation of conductive filaments (CFs) comprising oxygen vacancies. Thus, the electrical performance is primarily governed by the CF structure. Despite various approaches for regulating the oxygen vacancy distributions in oxide memristors, controlling the CF structure without modifying the device configuration related to material compatibility is still a challenge. This study demonstrates an effective strategy for localizing CF distributions in memristors by suppressing charge injection during the formation of conducting paths. As the injected charge quantity is reduced in the electroforming process of the oxide memristor, the CF distributions become narrower, leading to more reproducible and stable resistive switching characteristics in the device. Based on these findings, a reliable hardware neural network comprising oxide memristors is constructed to recognize complex images. The developed memristor has been employed as a synaptic memory component in systems without degradation for a long time. This promising concept of oxide memristors acting as stable synaptic components holds great potential for developing practical neuromorphic systems and their expansion into artificial intelligent systems.
Keywords: conductive filament; electroforming process; hardware neural network; oxide memristor; synaptic memory component.