Modeling, Parameters and Synaptic Plasticity Analysis of Lateral‐Ionic‐Gated Graphene Synaptic FETs
Abstract: Exploiting simulation modeling of graphene synaptic field‐effect transistors is extremely important for helping researchers to construct carbon‐based neuromorphic computing systems. Here, lateral‐ionic‐gated graphene synaptic FETs with different gate lengths are fabricated, and they are modeled by using basic physic models combined with the ions migration‐diffusion model and graphene material model. The feasibility and accuracy of the proposed modeling are validated by showing an excellent agreement between simulations and experimental results. The slicing technique of the modeling is proposed to analyze the influence of ionic concentration and diffusion coefficient on the ions movement to reveal their working mechanism. The effect of key parameters about gate length, ionic concentration, and diffusion coefficient on synaptic behavior such as short‐term plasticity, and long‐term plasticity is simulated and discussed. In addition, three kinds of spike‐timing‐dependent plasticity are obtained by the device modeling. This research opens up promising avenues for the development of artificial synapse modeling and paths to new opportunities for the construction of carbon‐based neuromorphic networks.
- Location
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Deutsche Nationalbibliothek Frankfurt am Main
- Extent
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Online-Ressource
- Language
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Englisch
- Bibliographic citation
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Modeling, Parameters and Synaptic Plasticity Analysis of Lateral‐Ionic‐Gated Graphene Synaptic FETs ; day:17 ; month:06 ; year:2024 ; extent:9
Advanced electronic materials ; (17.06.2024) (gesamt 9)
- Creator
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He, Xiaoying
Cao, Bowen
Xu, Minghao
Wang, Kun
Rao, Lan
- DOI
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10.1002/aelm.202400047
- URN
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urn:nbn:de:101:1-2406181425132.246171805656
- Rights
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Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Last update
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14.08.2025, 11:00 AM CEST
Data provider
Deutsche Nationalbibliothek. If you have any questions about the object, please contact the data provider.
Associated
- He, Xiaoying
- Cao, Bowen
- Xu, Minghao
- Wang, Kun
- Rao, Lan
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