Artificial Neurons Based on Ag/V2C/W Threshold Switching Memristors

Artificial synapses and neurons are two critical, fundamental bricks for constructing hardware neural networks. Owing to its high-density integration, outstanding nonlinearity, and modulated plasticity, memristors have attracted emerging attention on emulating biological synapses and neurons. However, fabricating a low-power and robust memristor-based artificial neuron without extra electrical components is still a challenge for brain-inspired systems. In this work, we demonstrate a single two-dimensional (2D) MXene(V2C)-based threshold switching (TS) memristor to emulate a leaky integrate-and-fire (LIF) neuron without auxiliary circuits, originating from the Ag diffusion-based filamentary mechanism. Moreover, our V2C-based artificial neurons faithfully achieve multiple neural functions including leaky integration, threshold-driven fire, self-relaxation, and linear strength-modulated spike frequency characteristics. This work demonstrates that three-atom-type MXene (e.g., V2C) memristors may provide an efficient method to construct the hardware neuromorphic computing systems.

Automated summary

Memristors have attracted attention for emulating biological synapses and neurons due to their high-density integration, outstanding nonlinearity, and modulated plasticity. This study demonstrates the successful emulation of multiple neural functions by using a two-dimensional MXene(V2C) memristor to simulate a leaky integrate-and-fire neuron, without the need for auxiliary circuits. This work suggests that three-atom-type MXene memristors may provide an efficient method for constructing hardware neuromorphic computing systems.