Insight into the Charging and Relaxation Dynamics of Diffusive Memristors in Integration-and-fire Neuron Applications

Abstract

Artificial neural networks (ANNs) have been studied to mimic biological neurons because of the limitations of conventional computing. Among various ANNs, the spike neural network (SNN) is advantageous owing to its energy efficiency. To demonstrate the effectiveness of the SNN, circuits of integrate-and-fire (IF), leaky IF (LIF), and Hodgkin-Huxley model have been studied using various methods. These circuits contain an external capacitor to mimic membrane behavior. In this study, it is expected that the LIF circuits can be simplified by adopting a diffusive memristor made of Pt/Ag-doped HfOSUBx/SUB/Pt. Volatile threshold switching was observed and modeled by performing electrical measurements. Their capacitive properties and relaxation behavior were interpreted by the internal capacitor and dissolution of the conducting filament. Pulse trains were adjusted to confirm the possibility of implementing an LIF neuron without an external capacitor.