Voltage–Time Transformation Model for Threshold Switching Spiking Neuron Based on Nucleation Theory - Yap, Suk-Min; Wang, I-Ting; Wu, Ming-Hung; Hou, Tuo-Hung (Frontiers in Neuroscience, 2022)
In this paper, we construct 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 equivalent circuit and successfully depicts the history-dependent threshold voltage of TS selectors, which has not yet been modeled in TS neurons before. Moreover, based on our model, we analyze the currently reported TS devices including ovonic threshold switching, insulator-metal-transition, and Ag-based selectors, and compare the predicted neuron behaviors. The results suggest the ovonic threshold-switching neuron be the most promising and potentially achieves the highest spiking frequency of GHz and the lowest operating voltage and area overhead. The proposed V-t Model provides an engineering pathway towards the future development of TS neurons for neuromorphic computing applications.