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

With the rapid rise in demand for artificial intelligence and brain-inspired computing, traditional von Neumann architecture is gradually approaching physical limitations in terms of computing power density, energy efficiency, and real-time performance. Graphene quantum dots (GQDs) and graphene oxide quantum dots (GOQDs), as zero-dimensional carbon-based materials with quantum confinement effects and tunable band structures, have shown significant potential in building next-generation ultra-low-power, large-scale integrated neuromorphic devices. This review systematically summarizes the main preparation strategies of graphene-based QDs and their structural regulation and functionalization methods. It focuses on the core functions of graphene-based QDs in synaptic working mechanisms such as charge capture, ion migration, and optoelectronic cooperation, as well as their latest progress in non-volatile memories, electrical and optoelectronic artificial synapses, and neuromorphic systems. Finally, this review article summarizes the current key challenges from the perspectives of material controllability, mechanism interpretability, device structural engineering, and system-level heterogeneous integration, and proposes future research directions to provide reference for the development of next-generation high-efficiency, scalable brain-inspired computing hardware.

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Publication Details

Journal
Advanced Science
Published
2026-03-20
DOI
https://doi.org/10.1002/advs.202600042
Primary Topic
Advanced Memory and Neural Computing
Type
article
Field-Weighted Citation Impact
0.00
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article

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

Yijun Zhou, Yulin Zhen, Zherui Zhao, Su‐Ting Han et al.
Advanced Science
Advanced Memory and Neural Computing
article

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

Yijun Zhou, Yulin Zhen, Zherui Zhao, Su‐Ting Han, Guanglong Ding, JiYu Zhao, Ziyan Huang, Zhuo Chen, Wei Zeng, Kui Zhou
article en

Abstract

With the rapid rise in demand for artificial intelligence and brain-inspired computing, traditional von Neumann architecture is gradually approaching physical limitations in terms of computing power density, energy efficiency, and real-time performance. Graphene quantum dots (GQDs) and graphene oxide quantum dots (GOQDs), as zero-dimensional carbon-based materials with quantum confinement effects and tunable band structures, have shown significant potential in building next-generation ultra-low-power, large-scale integrated neuromorphic devices. This review systematically summarizes the main preparation strategies of graphene-based QDs and their structural regulation and functionalization methods. It focuses on the core functions of graphene-based QDs in synaptic working mechanisms such as charge capture, ion migration, and optoelectronic cooperation, as well as their latest progress in non-volatile memories, electrical and optoelectronic artificial synapses, and neuromorphic systems. Finally, this review article summarizes the current key challenges from the perspectives of material controllability, mechanism interpretability, device structural engineering, and system-level heterogeneous integration, and proposes future research directions to provide reference for the development of next-generation high-efficiency, scalable brain-inspired computing hardware.

Advanced Science
Hong Kong Polytechnic University (HK), Shenzhen University (CN), Dalian University of Technology (CN), Dalian University (CN), Institute for Advanced Study (DE)
Affordable and clean energy
Openalex Percentile: Top 14%
Advanced Memory and Neural Computing
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