Device modeling and numerical analysis of VO2-based oscillatory neuristors

Abstract

Vanadium dioxide (VO2) holds great promise as a building block for neuromorphic computing due to its reversible phase transition at moderate stimulations and rapid switching speed. When integrated with appropriate circuit components, VO2 devices can exhibit both oscillatory, neuron-like excitation, and inhibitory functionality. Here, a self-consistent electrothermal model, based on random-resistor networks, is developed to replicate these unique characteristics, providing insights into the microscopic domain dynamics and filament formation during operation. The influence of various factors, such as capacitance, resistance, bias voltage, and ambient temperature, on the performance of VO2 neurons is systematically analyzed. These results are compared with those of an equivalent circuit model. These two approaches show strong agreement, offering complementary perspectives for the development of neuromorphic computing circuits.