A Memristor-Based Leaky Integrate-and-Fire Artificial Neuron With Tunable Performance

Abstract

Artificial neurons have received extensive attention as an important part of neuromorphic computing. Recently, tremendous efforts have been made on the memristor-based neurons, while the regulation of performance of such neurons and its underlining mechanism has been rarely studied. In this work, we propose an artificial neuron device based on Ag/TaO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><i>x</i></sub> /Si, which exhibits good threshold switching characteristics (on-off ratio above 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">5</sup> ) along with good device stability and cycling stability. The Leaky Integrate-and-Fire (LIF) neuron model is successfully simulated without additional circuitry, including leaky integrated firing and refractory periods. In addition, the effect of oxygen vacancy concentration on the performance of artificial neurons is investigated, and the results showed that an increase of oxygen vacancies can significantly reduce the threshold voltage of neuron activation, the holding voltage and the probability of refractory period. This work provides a simple and effective strategy for the development of artificial neurons with tunable properties.