Advances and Perspectives in Graphene-Based Quantum Dots Enabled Neuromorphic Devices

Area of Science:

• Materials Science
• Nanotechnology
• Computer Engineering

Background:

• Traditional von Neumann architecture faces limitations in computing power density, energy efficiency, and real-time performance due to increasing demand for AI.
• Graphene quantum dots (GQDs) and graphene oxide quantum dots (GOQDs) are zero-dimensional carbon nanomaterials with unique quantum confinement and tunable band structures.
• These properties make GQDs and GOQDs highly suitable for developing next-generation ultra-low-power, large-scale integrated neuromorphic devices.

Purpose of the Study:

• To systematically review preparation strategies, structural regulation, and functionalization methods for graphene-based quantum dots.
• To focus on the core functions of graphene-based QDs in synaptic working mechanisms, including charge capture, ion migration, and optoelectronic cooperation.
• To summarize recent advancements in non-volatile memories, electrical and optoelectronic artificial synapses, and neuromorphic systems utilizing graphene-based QDs.

Main Methods:

• Systematic literature review of preparation strategies for graphene-based quantum dots.
• Analysis of structural regulation and functionalization techniques for GQDs and GOQDs.
• Review of research on the application of graphene-based QDs in neuromorphic computing components and systems.

Main Results:

• Graphene-based QDs exhibit significant potential for building next-generation neuromorphic devices.
• Key functions like charge capture, ion migration, and optoelectronic cooperation are explored in synaptic mechanisms.
• Progress in non-volatile memories, artificial synapses (electrical and optoelectronic), and complete neuromorphic systems is highlighted.

Conclusions:

• Graphene-based QDs are crucial for advancing brain-inspired computing hardware.
• Challenges remain in material controllability, mechanism interpretability, device engineering, and system integration.
• Future research should focus on addressing these challenges to develop high-efficiency, scalable brain-inspired computing solutions.