Yap Suk-Min, Wang I-Ting, Wu Ming-Hung, Hou Tuo-Hung
Department of Electrical Engineering and Institute of Electronics, National Yang Ming Chiao Tung University, Hsinchu, Taiwan.
Front Neurosci. 2022 Apr 13;16:868671. doi: 10.3389/fnins.2022.868671. eCollection 2022.
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.
在本研究中，我们构建了一个电压-时间变换模型（V-t模型），用于预测和模拟基于阈值开关选择器的神经元（TS神经元）的尖峰行为。V-t模型结合了物理成核理论和电阻-电容（RC）等效电路，成功地描绘了TS选择器的历史依赖阈值电压，这在TS神经元中尚未被建模。此外，基于我们的模型，我们分析了目前报道的TS器件，包括硫系阈值开关（OTS）、绝缘体-金属转变和银（Ag）基选择器，并比较了预测神经元的行为。结果表明，OTS神经元最具潜力，可能实现最高达GHz的尖峰频率、最低的工作电压和面积开销。所提出的V-t模型为用于神经形态计算应用的TS神经元的未来发展提供了一条工程途径。
