Resistive switching and synaptic characteristics of Hf-doped ZnO sandwiched between HfO2-based memristors for neuromorphic computing

Abstract

With the rise of neuromorphic computing, memristors have attracted much attention as a device with potential synaptic properties. In this paper, we investigated the embedding of Hf-doped ZnO (Hf:ZnO) layers with different doping concentrations in HfO2-based memristors to design HfO2/(0 %-10 %)Hf:ZnO/HfO2 tri-layer structured memristors for resistive switching (RS) and synaptic characteristics. Utilizing first-principles calculations, we integrated assessments of both oxygen vacancy migration capability and material conductivity to evaluate the rationale behind our design. The experimental results show that the HfO2/(5 %)Hf:ZnO/HfO2 tri-layer structured memristor presents the best RS performance, with a storage window of 103. We analyze the impact of insertion layers with varying doping concentrations on the RS performance of the devices and elucidate the RS mechanism. In addition, we investigated the synaptic properties of HfO2/(5 %)Hf:ZnO/HfO2 tri-layer structured memristor, and effectively simulated synaptic plasticity akin to the synaptic behavior found in biological neurons. Finally, we also constructed a memristor-based Spiking Neural Network for recognizing the Mixed National Institute of Standards and Technology handwritten digit dataset, achieving a recognition accuracy of 82 %. This comprehensive study shows that the insertion of (5 %)Hf:ZnO into HfO2-based memristors holds significant promise for upcoming applications in the field of neuromorphic computing.