Exploring conductance modulation and implementation of convolutional neural network in Pt/ZnO/Al2O3/TaN memristors for brain-inspired computing

Abstract

Oxide-based memristors have emerged as a promising electronic device for high-density memory and neuromorphic applications. In our study, we explored the tunable analog switching and biological synaptic functions of a Pt/ZnO/Al2O3/TaN memristive device. Using transmission electron microscopy (TEM) and x-ray photoelectron spectroscopy (XPS), we confirmed the presence of a TaOxNy interface layer at the anode contact, believed to play a critical role in resistance transitions. The memristive device showed excellent performance, including a stable and reproducible analog switching memory with a low operating voltage (μ=̶2.0/+1.7V), good cycling endurance (2 × 102), a high on/off ratio (>103), and retention up to 104 s at 85 °C. Additionally, multi-state resistances were achieved by varying the reset voltage, enabling the creation of neuromorphic synapses and high-density memories. Direct-current mode set and reset transitions showed multi-state resistance changes similar to potentiation and depression behaviors in biological synapses. Further simulations, including long-term potentiation (LTP) and long-term depression (LTD), paired pulse facilitation (PPF), and convolutional neural network (CNN) simulations for handwritten digits, showed an accuracy of 86.5%. These results indicate that the memristive device is highly suitable for use in high-density memory and brain-inspired computer systems.