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Erschienen in:

19.08.2023

Modeling of inversion layer capacitance of III-V double gate MOSFETs using a neural network-based regression technique

verfasst von: Subir Kumar Maity, Soumya Pandit

Erschienen in: Journal of Computational Electronics | Ausgabe 5/2023

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Abstract

This work presents a data-driven regression model of inversion layer capacitance of double gate III-V channel MOSFETs implemented using an artificial neural network. The training dataset is generated using a Schrodinger-Poisson solver for different channel thicknesses, carrier effective masses, oxide thickness, barrier height, and a wide range of gate bias voltages. The neural network predicted capacitance value is compared with Schrodinger-Poisson solver data and a physics-based analytical model result. The model effectively captures the variation in channel thickness, barrier height, carrier effective mass, and oxide thickness. Furthermore, extensive error analysis has been performed to demonstrate the correctness and degree of accuracy of the predicted result.

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Metadaten
Titel
Modeling of inversion layer capacitance of III-V double gate MOSFETs using a neural network-based regression technique
verfasst von
Subir Kumar Maity
Soumya Pandit
Publikationsdatum
19.08.2023
Verlag
Springer US
Erschienen in
Journal of Computational Electronics / Ausgabe 5/2023
Print ISSN: 1569-8025
Elektronische ISSN: 1572-8137
DOI
https://doi.org/10.1007/s10825-023-02089-7