# Thermally Activated Negative Differential Resistance VO x Memristor with Switchable Rate and Leaky Integrate-and-Fire Spiking Dynamics

## TL;DR

A VOx-based memristor can function as both a spiking encoder and a leaky integrate-and-fire neuron, enabling compact and efficient neuromorphic hardware.

## Contribution

A VOx-based memristor is demonstrated to intrinsically support dual-mode spiking and LIF dynamics through thermal phase transitions.

## Key findings

- The device achieves a maximum spiking frequency of 570 kHz and energy consumption as low as 4.7 nJ per spike.
- The VOx memristor exhibits snap-back negative differential resistance due to a thermally driven insulator-to-metal transition.
- Integrated into a passive circuit, the device generates high-frequency spike trains and tunable LIF responses.

## Abstract

Spiking neural networks (SNNs) require neuron devices
that are
both compact and capable of supporting continuous-time and event-based
dynamics. Here, we demonstrate a VO
x
-based
threshold switching memristor (TSM) that intrinsically enables dual-mode
operation, functioning as both a spiking encoder and a leaky integrate-and-fire
(LIF) neuron. While such dual behavior is theoretically possible in
volatile memristors, it has only been experimentally demonstrated
in limited cases. It is achieved intrinsically in a single VO
x
-based device, arising from a thermally driven
insulator-to-metal transition (IMT) within the VO
x
layer, confirmed by temperature-dependent XRD and correlated
with snap-back negative differential resistance (NDR) observed in
electrical measurements. Integrated into a passive circuit, the device
generates high-frequency spike trains under analog input and tunable
LIF responses under pulsed stimulation. The device achieves a maximum
spiking frequency of 570 kHz, a time-to-first-spike (TTFS) of 1.6
μs, and energy consumption as low as 4.7 nJ per spike. These
results highlight the strong coupling between structural phase dynamics
and neuronal functions, positioning the VO
x
TSM as a promising platform for scalable neuromorphic hardware with
biologically realistic spiking capabilities.