Reliable Resistive Switching and Multifunctional Synaptic Behavior in ZnO/NiO Nanocomposite Based Memristors for Neuromorphic Computing

Neuromorphic devices with extremely low energy consumption are greatly demanded for brain-like computing and artificial intelligence (AI). In this work, the ZnO–NiO nanocomposite as an active layer used to create artificial synaptic memristor devices with memory functions, including high ON/OFF ratios, stable and filamentary resistive switching behavior, long-term/short-term plasticity (LTP/STP), and learning-experience response. These qualities closely resemble biological learning and memory activities. Controlled production and rupture of Ag filaments result in resistive switching with a switching ratio of ∼103, making them ideal for nonvolatile memory demands. Before electroforming, the progressive conductance modulation of a Ag/ZnO/NiO/Pt/Ti/SiO2 memristor may be observed, and the working mechanism described by the subsequent development and contraction of Ag filaments induced by a redox reaction. Furthermore, the nanocomposite memristors demonstrated an exponential decay curve with a 2.26 μs decay time constant and an artificial neural network (ANN) with outstanding identification accuracy of 90.7% for handwritten digits. This work suggests that the proposed memristors (with a stable and mutifuntional responses) might enable efficient neuromorphic designs.

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