In this study, we constructed a voltage-time transformation model (V-t Model) to predict and simulate the spiking behavior of threshold-switching selector-based neurons (TS neurons). The V-t Model combines the physical nucleation theory and the resistor-capacitor (RC) equivalent circuit and successfully depicts the history-dependent threshold voltage of TS selectors, which has not yet been modeled in TS neurons. Moreover, based on our model, we analyzed the currently reported TS devices, including ovonic threshold switching (OTS), insulator-metal transition, and silver- (Ag-) based selectors, and compared the behaviors of the predicted neurons. The results suggest that the OTS neuron is the most promising and potentially achieves the highest spike frequency of GHz and the lowest operating voltage and area overhead. The proposed V-t Model provides an engineering pathway toward the future development of TS neurons for neuromorphic computing applications.
Keywords: history-dependent; neuromorphic computing; nucleation theory; spiking neuron; threshold switching selector.
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