Circuit Modeling and Analysis of Memristor Crossbar Array-Based SNN With Spike-Mapping STDP Implementation

This article presents an efficient method for modeling and analysis of memristor crossbar array circuit-based neuromorphic computing hardware. A novel volatile memristor-based artificial neuron circuit is proposed, realizing real-time generation of spike sequences, where the amplitude, width, and reserve time of the spikes are all adjustable. Based on proposed neuron circuits and nonvolatile memristor-based artificial synapses, a spike timing-dependent plasticity (STDP) circuit implementation with a simple spike-mapping method is proposed and used for a memristor crossbar array-based spiking neural network (SNN) circuit. A circuit model of a crossbar array combined with a partial equivalent element circuit (PEEC) and volume filament (VFI) is used to calculate the circuit capacitance, inductance, and resistance. It is demonstrated by a transient simulation of a crossbar array circuit that the proposed SNN method can perform image classification tasks, and the recognition accuracy decreases with the frequency increase. From the perspective of affecting the spike sequences and STDP rules, damage caused by device stochasticity, parasitic effects, and skin effect on SNN recognition performance is further analyzed and evaluated.