Implementation of an artificial neuron circuit model based on high speed AlN stacked threshold switching devices
Zhe Fan, Hong Fan, Chang He, Xiaobing Yan · Frontiers of Physics · 2025
There has been a notable surge of interest in neuromorphic network computation, particularly concerning both non-volatile and volatile threshold devices. In this research, we have developed a multi-layer thin film architecture consisting of Al/AlN/Ag/AlN/Pt, which functions as a threshold switching (TS) device characterized by rapid switching speeds of 50 ns and minimal leakage current. We have effectively demonstrated biological neuron-like behaviors, such as threshold-driven spikes, all-or-nothing spikes, intensity-modulated frequency response, and frequency-modulated frequency response, through the deployment of a leaky integrate-and-fire (LIF) artificial neuron circuit, which surpasses earlier neuronal models. The resistance switching mechanism of the device is likely due to the migration of nitrogen vacancies in conjunction with silver filaments. This threshold switching device shows significant potential for applications in next-generation artificial neural networks.