A model of TaOx threshold switching memristor for neuromorphic computing

Abstract

In this article, we analyzed the experimental data based on the TaOx memristor and found that the threshold switching (TS) characteristics are related to temperature, and its logarithmic I–V curve is in good agreement with the space charge limiting current conduction mechanism. We use this mechanism to establish a TS physical model and then use the physical model to build an LTspice model. The model data are fitted with the experimental data, which is basically consistent. Next, using the TS memristor to simulate a leaky integrate-and-fire neuron circuit, the basic dynamics are realized. By changing the external temperature of the memristor, the output frequency of the neuron will be more intense as the temperature increases. Finally, an artificial spiking neural network (SNN) was built based on this neuron circuit for MNIST recognition task. In this SNN, the input signals fused both voltage amplitude and temperature to achieve neuromorphic multimodal preprocessing and enhance the recognition accuracy. These results demonstrated the reliability of the model, which enhanced the flexibility for exploring the application of TaOx-based TS memristors.