Reliable Resistive Switching and Multifunctional Synaptic Behavior in ZnO/NiO Nanocomposite Based Memristors for Neuromorphic Computing
Rajwali Khan, Fazal Raziq, Iftikhar Ahmad, Siddhartha Ghosh, Soorathep Kheawhom, Sambasivam Sangaraju
Abstract
High Resolution Image Download MS PowerPoint Slide 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 ∼10 3, making them ideal for nonvolatile memory demands. Before electroforming, the progressive conductance modulation of a Ag/ZnO/NiO/Pt/Ti/SiO 2 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.