Voltage Pulse Driven VO2 Volatile Resistive Transition Devices as Leaky Integrate-and-Fire Artificial Neurons

Zhen Xu,Ayrton A Bernussi,Zhaoyang Fan

doi:10.3390/electronics11040516

Abstract

In a hardware-based neuromorphic computation system, using emerging nonvolatile memory devices as artificial synapses, which have an inelastic memory characteristic, has attracted considerable interest. In contrast, the elastic artificial neurons have received much less attention. An ideal material system that is suitable for mimicking biological neurons is the one with volatile (or mono-stable) resistive change property. Vanadium dioxide (VO2) is a well-known material that exhibits an abrupt and volatile insulator-to-metal transition property. In this work, we experimentally demonstrate that pulse-driven two-terminal VO2 devices behave in a leaky integrate-and-fire (LIF) manner, and they elastically relax back to their initial value after firing, thus, mimicking the behavior of biological neurons. The VO2 device with a channel length of 20 µm can be driven to fire by a single long-duration pulse (>83 µs) or multiple short-duration pulses. We further model the VO2 devices as resistive networks based on their granular domain structure, with resistivities corresponding to the insulator or metallic states. Simulation results confirm that the volatile resistive transition under voltage pulse driving is caused by the formation of a metallic filament in an avalanche-like process, while this volatile metallic filament will relax back to the insulating state at the end of driving pulses. The simulation offers a microscopic view of the dynamic and abrupt filament formation process to explain the experimentally observed LIF behavior. These results suggest that VO2 insulator–metal transition could be exploited for artificial neurons.

Highlights

Accepted: 6 February 2022Artificial neural networks (ANNs) are the backbone of machine learning and have been attracting considerable interest [1,2]
XRD measurement (Figure 1b) the monocrystalline structure of the VO2 film, with monoclinic (020) as the growth plane confirms the monocrystalline structure of the VO2 film, with monoclinic (020) as the growth on the c-cut sapphire substrate
The experimental data shows that two-terminal VO2 devices, thanks to their abrupt and volatile insulator-to-metal transition, exhibit the basic leaky integrate-and-fire (LIF) spiking neuron characteristic with elasticity

Summary

Introduction

Accepted: 6 February 2022Artificial neural networks (ANNs) are the backbone of machine learning and have been attracting considerable interest [1,2]. They are commonly implemented through machine learning software, running on silicon-based hardware, and such an approach has been proved to be successful, evidenced by the rapid development of deep learning applications [3,4] This approach faces the challenge of limited computation capability, and its low energy efficiency will constrain the maximum number of neurons that could be simulated to a level less than the human brain, by several orders of magnitudes [5]. In this regard, biological neurons mimic neuromorphic systems, in which artificial neurons and artificial synapses are directly implemented by dedicated devices and have intrinsic superiority [6,7]. Different devices have been proposed to mimic the distributed processing function of individual neurons, and for the distributed memory function of synapses between the Published: 9 February 2022

Results

Discussion

Conclusion