Enhanced analog switching and neuromorphic performance of ZnO-based memristors with indium tin oxide electrodes for high-accuracy pattern recognition.

This article focuses on the investigation of analog switching and neuromorphic characteristics in zinc oxide (ZnO)-based memristors by varying the anodic top electrode (TE) materials—indium tin oxide (ITO), titanium (Ti), and tantalum (Ta). The study finds that memristors with ITO electrodes exhibit superior dual volatile and nonvolatile switching behaviors, multistate switching capabilities, and stable resistive switching compared to Ti and Ta electrodes, which suffer from reset failures due to cation migration. High-resolution transmission electron microscopy confirmed the polycrystalline structure of the ZnO layer, and the current transport mechanism was dominated by Schottky emission with a modulated barrier height. The ITO/ZnO/ITO/Si memristor successfully mimicked synaptic functions such as long-term potentiation and depression, achieving a 90.84% accuracy in pattern recognition using a convolutional neural network on the Modified National Institute of Standards and Technology (MNIST) dataset, highlighting its potential for advanced neuromorphic computing and high-performance electronic applications.