Microscopic Modeling and Optimization of NbOx Mott Memristor for Artificial Neuron Applications

Mott memristors have been considered as a promising candidate to implement artificial neurons for neuromorphic computing thanks to their low-power consumption and superior scalability. However, the large variability and poor reliability hinder their large-scale applications. The complex working mechanism associated with the thermoelectric coupling in the correlated oxides such as niobium oxide (NbOx) has led to the lack of a physics-based model to guide device optimizations. In this work, we present a microscopic model of NbOx-based Mott memristor and investigate the evolution of atomic configuration via a real-time scale kinetic Monte-Carlo simulation involving multiple physical processes. We elucidate the relationship between the ${I}{-}{V}$ characteristics and the oxygen stoichiometry. We further reveal that the low-yield issue originates from the oxidation of NbO2 phase in air and the poor reliability correlates with the migration of oxygen vacancies. We hence propose to improve the device performance by introducing a Si3N4 passivation layer and N doping. The optimized devices exhibit excellent endurance of more than 108 cycles with significantly reduced variability and low operation voltage. Both oscillation neuron and leaky integrate and fire (LIF) neuron are experimentally implemented using the optimized Mott device, which could serve as a highly reliable artificial neuron with low variability and excellent endurance for large-scale neuromorphic computing systems.