Voltage-Time Transformation Model for Threshold Switching Spiking Neuron Based on Nucleation Theory

Area of Science:

• Neuromorphic Engineering
• Materials Science
• Computational Neuroscience

Background:

• Threshold-switching selector-based neurons (TS neurons) are crucial for neuromorphic computing.
• Existing models do not fully capture the history-dependent threshold voltage of TS selectors.
• Understanding and simulating TS neuron behavior is essential for advancing artificial intelligence hardware.

Purpose of the Study:

• To develop a novel voltage-time transformation model (V-t Model) for predicting and simulating TS neuron spiking behavior.
• To incorporate physical nucleation theory and resistor-capacitor (RC) equivalent circuits into a unified model.
• To analyze and compare different TS selector devices within the V-t Model framework.

Main Methods:

• Construction of the V-t Model by integrating physical nucleation theory with RC equivalent circuit.
• Simulation of TS neuron spiking behavior using the developed V-t Model.
• Analysis of various TS selector devices, including ovonic threshold switching (OTS), insulator-metal transition, and silver-based selectors.

Main Results:

• The V-t Model successfully depicts the history-dependent threshold voltage of TS selectors, a previously unmodeled aspect.
• Comparison of predicted neuron behaviors indicates that OTS neurons offer the highest potential spike frequency (GHz) and lowest operating voltage.
• The model provides a quantitative comparison of different TS neuron types, highlighting their suitability for specific applications.

Conclusions:

• The proposed V-t Model offers an effective engineering pathway for developing advanced TS neurons.
• Ovonic threshold switching (OTS) neurons are identified as the most promising candidates for high-performance neuromorphic computing applications.
• The V-t Model facilitates the design and optimization of TS neurons, paving the way for future neuromorphic hardware development.