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

Area of Science:

• Materials Science
• Neuroscience
• Computer Engineering

Background:

• Memristors are crucial for hardware neural networks due to their integration, nonlinearity, and plasticity.
• Emulating biological synapses and neurons with memristors is an active research area.
• A key challenge is creating low-power, robust artificial neurons without additional electronic components.

Purpose of the Study:

• To demonstrate a single two-dimensional (2D) MXene (V2C)-based memristor capable of emulating a leaky integrate-and-fire (LIF) neuron.
• To achieve artificial neuron functionality without auxiliary circuits.
• To explore the potential of MXene memristors in neuromorphic computing.

Main Methods:

• Fabrication of a V2C-based threshold switching (TS) memristor.
• Investigation of the Ag diffusion-based filamentary mechanism responsible for neuron emulation.
• Characterization of the memristor's ability to perform neural functions.

Main Results:

• A single V2C memristor successfully emulated a LIF neuron without extra components.
• The device exhibited key neural functions: leaky integration, threshold-driven firing, and self-relaxation.
• Linear strength-modulated spike frequency characteristics were achieved.

Conclusions:

• Three-atom-type MXene memristors, specifically V2C, offer an efficient pathway for constructing hardware neuromorphic computing systems.
• This work presents a significant advancement in developing compact and effective artificial neurons.
• The V2C memristor demonstrates potential for low-power, high-performance brain-inspired computing applications.