HfO2-based memristor-CMOS hybrid implementation of artificial neuron model
Yinxing Zhang, Ziliang Fang, Xiaobing Yan
Abstract
Memristors with threshold switching behavior are increasingly used in the study of neuromorphic computing, which are frequently used to simulate synaptic functions due to their high integration and simple structure. However, building a neuron circuit to simulate the characteristics of biological neurons is still a challenge. In this work, we demonstrate a leaky integrate-and-fire model of neurons, which is presented by a memristor-CMOS hybrid circuit based on a threshold device of a TiN/HfO2/InGaZnO4/Si structure. Moreover, we achieve multiple neural functions based on the neuron model, including leaky integration, threshold-driven fire, and strength-modulated spike frequency characteristics. This work shows that HfO2-based threshold devices can realize the basic functions of spiking neurons and have great potential in artificial neural networks.