[paper] Compact Model of Graphene FETs

Nikolaos Mavredakis, Anibal Pacheco-Sanchez, Oihana Txoperena,

Elias Torres, and David Jiménez

A Scalable Compact Model for the Static Drain Current of Graphene FETs

IEEE TED, Vol. 71, No. 1, January 2024

DOI:  10.1109/TED.2023.3330713

1 Departament d’Enginyeria Electrònica, Escola d’Enginyeria, UAB, 08193 Bellaterra, Spain
2 Graphenea Semiconductor SLU, 20009 San Sebastián, Spain.

Abstract: 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 the 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.

Fig: Graphenea GFET schematic cross-section not drawn to scale. Graphene under metal contacts is not shown.The drain current has explicit derivation in respect to Qgr, where Qt and Qp(n) are the transport sheet and p(n)-type charges, respectively; Vc is the chemical potential, h is the reduced Planck constant, uf is the Fermi velocity, e is the electron charge, and k is a coefficient. Qt and, thus, ID can be calculated according to Vc polarity at source (Vcs) and drain (Vcd), respectively. Hence, at n-type region where Vcs, Vcd > 0 and Qp = 0

Acknowledgements: This work was supported in part by the European Union’s Horizon 2020 Research and Innovation Program GrapheneCore3 under Grant 881603; in part by the Ministerio de Ciencia, Innovación y Universidades under Grant RTI2018-097876-B-C21 (MCIU/AEI/ FEDER, UE), Grant FJC2020-046213-I, and Grant PID2021-127840NBI00 (MCIN/AEI/FEDER, UE); in part by the European Union Regional Development Fund within the Framework of the ERDF Operational Program of Catalonia 2014–2020 with the Support of the Department de Recerca i Universitat, with a grant of 50% of Total Cost Eligible; and in part by the GraphCAT Project under Grant 001-P-001702.