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.
In this work, a compact model of the diffusive memristor is proposed from the perspective of the transition of electronic transmission mechanisms induced by the dynamics of filament. First, a new physical model is established based on tunneling mechanisms that are used to fit the experimental data, and the results indicate it is versatile for various diffusive memristors. In addition, the threshold voltage (Vth) of the diffusive memristor negatively correlates with the ratio of ionic migration and diffusion coefficient (ui/Ds), and the hold voltage (Vh) positively correlates with the ratio of ionic diffusion and migration coefficient (Ds/ui), which is beneficial for selecting material to achieve target electrical properties. Furthermore, the different parameters that influence the simulated switching curve were explored. The result indicated that the desired electrical characteristic can obtain by obtained by adjusting these parameters. A compact electrical module model is then built and tested in LTspice to carry out bio-neuron and bio-synaptic performance completely. These simulations demonstrated that the model is reliable for exploring diffusive memristor applications.
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