A Scalable Compact Model for the Static Drain Current of Graphene FETs
Mavredakis, Nikolaos (Universitat Autònoma de Barcelona. Departament d'Enginyeria Electrònica)
Pacheco-Sanchez, Anibal (Universitat Autònoma de Barcelona. Departament d'Enginyeria Electrònica)
Txoperena, Oihana (Graphenea Semiconductor SLU)
Torres, Elias (Graphenea Semiconductor SLU)
Jiménez, David (Universitat Autònoma de Barcelona. Departament d'Enginyeria Electrònica)

Data:	2024
Resum:	The main target of this article is to propose for the first time a physics-based continuous and symmetric compact model that accurately captures I-V experimental dependencies induced by geometrical scaling effects for graphene field-effect transistor (GFET) technologies. Such a scalable model is an indispensable ingredient for the boost of large-scale GFET applications, as it has been already proved in solid industry-based CMOS technologies. Dependencies of the physical model parameters on channel dimensions are thoroughly investigated, and semi-empirical expressions are derived, which precisely characterize such behaviors for an industry-based GFET technology, as well as for others developed in research laboratory. This work aims at the establishment of the first industry standard GFET compact model that can be integrated in circuit simulation tools and, hence, can contribute to the update of GFET technology from the research level to massive industry production.
Ajuts:	European Commission 881603 Agencia Estatal de Investigación RTI2018-097876-B-C21 Agencia Estatal de Investigación FJC2020-046213-I Agencia Estatal de Investigación PID2021-127840NB-I00
Nota:	Altres ajuts: GraphCAT project reference: 001-P-001702
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Llengua:	Anglès
Document:	Article ; recerca ; Versió acceptada per publicar
Matèria:	Circuit design ; Compact model ; Contact resistance ; Flat-band voltage ; Geometrical scaling ; Graphene field-effect transistor (GFET) ; Mobility ; Residual charge
Publicat a:	IEEE Transactions on Electron Devices, Vol. 71, Issue 1 (January 2024) , p. 853-859, ISSN 0018-9383

DOI: 10.1109/TED.2023.3330713

Postprint 8 p, 1.4 MB

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