Abstract
In this work, a nano-crystalline (NC) ZnO-based memristor was fabricated to investigate the short-term memory characteristics for reservoir computing systems. The crystalline structure of the ZnO film was confirmed through transmission electron microscopy (TEM) and X-ray diffraction pattern (XRD), while X-ray photoelectron spectroscopy (XPS) confirmed the chemical and bonding states of each element. The NC-ZnO-based memristor exhibited remarkable endurance, enduring more than 200 DC cycles, and had a high to low resistance (RH/RL) ratio of 102. Furthermore, it displayed long data retention of 104 s and consistent resistive switching (RS) with restricted variation in the set and reset voltage, showing its excellent performance characteristics. By controlling the pulse amplitude and the time interval between pulses, it was possible to effectively replicate the key features of short-term synaptic plasticity, including potentiation, depression, and paired-pulse depression, through conductance modulation. An artificial neural network (ANN) simulation achieved a pattern recognition accuracy of approximately 90.1% for a 28 × 28-pixel image after 100 training epochs. Based on this extensive study, NC-ZnO-based memristor exhibits immense potential as a crucial element in constructing high-performance neuromorphic computing systems.
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