Dedicated and Reconfigurable Artificial Neurons and Synapses based on Two-Dimensional Materials for Efficient...

Area of Science:

• Materials Science
• Computer Science
• Neuroscience

Background:

• Neuromorphic computing offers solutions to storage-compute separation and scaling limitations.
• Artificial neurons and synapses are key components for biomimetic neuromorphic architectures.
• Two-dimensional (2D) materials possess unique properties suitable for neuromorphic devices.

Purpose of the Study:

• To review recent advances in artificial neuron and synapse devices based on 2D materials.
• To focus on biomimetic models, physical mechanisms, and performance metrics of these devices.
• To analyze limitations and chart a roadmap for future brain-like chips.

Main Methods:

• Review of recent literature on 2D material-based neuromorphic devices.
• Focus on biomimetic models and underlying physical mechanisms.
• Analysis of device performance metrics and integration strategies.

Main Results:

• 2D materials meet requirements for ultralow power consumption, dynamic plasticity, and multifunctional integration.
• Advances in dedicated artificial neuron and synapse devices based on 2D materials are highlighted.
• Systemic integration for neural perception, networks, and logical operations is summarized.

Conclusions:

• 2D materials are crucial for next-generation neuromorphic hardware.
• Further research is needed to overcome device and system-level limitations.
• A roadmap towards more efficient and multifunctional brain-like chips is presented.