Leaky integrate-and-fire and oscillation neurons based on ZnO diffusive memristors for spiking neural networks

Abstract

Diffusive threshold switching (TS) memristors have emerged as a promising candidate for artificial neurons, effectively replicating neuronal functions and enabling spiking neural networks (SNNs) to emulate the low-power processing of biological brains. In this study, we present an artificial neuron based on a Pt/Ag/ZnO/Pt volatile memristor, which exhibits exceptional TS characteristics, including electro-forming-free operation, low voltage requirements (<0.2 V), high stability (2.25% variation over 1024 cycles), a high on/off ratio (10⁶), and inherent self-compliance. These Pt/Ag/ZnO/Pt diffusive memristors are employed to simultaneously emulate oscillation neurons and leaky integrate-and-fire (LIF) neurons, enabling precise modulation of oscillation and firing frequencies through pulse parameters while maintaining low energy consumption (1.442 nJ per spike). We further integrate the oscillation and LIF neurons as input and output neurons, respectively, in a two-layer SNN, achieving a high classification accuracy of 89.17% on MNIST-based voltage images. This work underscores the potential of ZnO diffusive memristors in emulating stable artificial neurons and highlights their promise for advanced neuromorphic computing applications using SNNs.