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March 22, 2026Advanced Science5 citationsOpen Access

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

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YZYulin ZhenWZWei ZengZZZherui Zhao

Key Points

  • The aim is to explore the potential of graphene quantum dots in neuromorphic devices and their impact on computing performance.
  • Summarizes preparation strategies for graphene-based quantum dots.
  • Details structural regulation and functionalization methods.
  • Examines the core functions in synaptic mechanisms such as charge capture and ion migration.
  • Reviews recent advancements in non-volatile memories and artificial synapses.
  • Graphene quantum dots show great potential in building ultra-low-power neuromorphic devices.
  • Demonstrated enhanced functionalities in charge capture and optoelectronic cooperation.
  • Identified challenges related to material controllability and device integration.

Abstract

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.

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Cite This Study

Zhen et al. (2026) studied this question.

synapsesocial.com/papers/69bf899af665edcd009e95ebhttps://doi.org/10.1002/advs.202600042
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