Oxygen vacancies modulated VO2 for neurons and Spiking Neural Network construction

TL;DR

This paper presents oxygen vacancies modulated VO2-x neurons that operate at lower voltages and higher speeds, enabling efficient and practical neuromorphic computing systems with improved image recognition accuracy.

Contribution

The study introduces oxygen vacancies modulation in VO2 films to create low-power, high-speed neuronal devices for Spiking Neural Networks, advancing neuromorphic hardware development.

Findings

VO2-x neurons operate under lower voltage with higher processing speed.

VO2-x based BP-SNNs achieve high accuracy on MNIST dataset.

Defect engineering enhances VO2-based neuromorphic device performance.

Abstract

Artificial neuronal devices are the basic building blocks for neuromorphic computing systems, which have been motivated by realistic brain emulation. Aiming for these applications, various device concepts have been proposed to mimic the neuronal dynamics and functions. While till now, the artificial neuron devices with high efficiency, high stability and low power consumption are still far from practical application. Due to the special insulator-metal phase transition, Vanadium Dioxide (VO2) has been considered as an idea candidate for neuronal device fabrication. However, its intrinsic insulating state requires the VO2 neuronal device to be driven under large bias voltage, resulting in high power consumption and low frequency. Thus in the current study, we have addressed this challenge by preparing oxygen vacancies modulated VO2 film(VO2-x) and fabricating the VO2-x neuronal devices for Spiking Neural Networks (SNNs) construction. Results indicate the neuron devices can be operated under lower voltage with improved processing speed. The proposed VO2-x based back-propagation SNNs (BP-SNNs) system, trained with the MNIST dataset, demonstrates excellent accuracy in image recognition. Our study not only demonstrates the VO2-x based neurons and SNN system for practical application, but also offers an effective way to optimize the future neuromorphic computing systems by defect engineering strategy.