Investigation of the Modulated Threshold Memristor for Tunable Artificial Neuron

Abstract

Threshold switching (TS) memristor with a simple structure and high biomimetic offers a more promising way to implement an efficient artificial neuron than traditional methods. To accommodate the complex environments in practical applications, previous memristor-based neurons typically incorporate auxiliary circuits to ensure tunability within circuits. However, this addition not only heightens the design complexity but also reduces the efficiency. In this work, we investigate the conduction process under different thresholds in an NbOx-based memristor and further demonstrate its potential merits in human face recognition. The negative threshold voltage of the device can be linearly modulated by positive stimuli. The conduction mechanisms under different threshold states are systematically investigated by experiments and theoretical analysis, showing that the defects concentration controlled by the electrical field is attributed to the threshold modulation. The revealed mechanism is instructive for device optimization, offering an oxygen-related fabrication method. Based on such a device, we construct a tunable spiking neuron whose threshold can be modulated by only one preoperation on the neuron without other burdensome units. By modulating the threshold based on the light intensities—a lower threshold for the bright condition and a higher threshold for the dark condition—the temporal features of the neuron outputs can be maintained at a normal condition to ensure the correct recognition under different environmental luminance. The function of the proposed tunable neuron is further evaluated in a network for human face recognition. The network finally reaches a 93.25% accuracy with tunable threshold neurons, significantly surpassing the 71.87% with fixed-threshold neurons.