A computationally efficient hybrid approach based on artificial neural networks and the wavelet transform for quantum simulations of graphene nanoribbon FETs

Numerical modeling of graphene nanoribbon field-effect transistors (GNRFETs) using quantum-mechanical approaches is often associated with a heavy computational burden, indicating the urgent need for new methods to resolve this issue. A computationally efficient hybrid approach for quantum simulation...

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Bibliographic Details
Published in:Journal of computational electronics 2019-09, Vol.18 (3), p.813-825
Main Authors: Tamersit, Khalil, Djeffal, Fayçal
Format: Article
Language:English
Subjects:
Accuracy
Artificial neural networks
Charge density
Computational efficiency
Electrical Engineering
Engineering
Field effect transistors
Flexibility
Flight simulators
Graphene
Green's functions
Mathematical and Computational Engineering
Mathematical and Computational Physics
Mathematical models
Mechanical Engineering
Methods
Nanoelectronics
Nanoribbons
Nanotechnology devices
Neural networks
Numerical models
Onsite
Optical and Electronic Materials
Poisson equation
Semiconductor devices
Simulation
Theoretical
Transistors
Wavelet transforms
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Summary:Numerical modeling of graphene nanoribbon field-effect transistors (GNRFETs) using quantum-mechanical approaches is often associated with a heavy computational burden, indicating the urgent need for new methods to resolve this issue. A computationally efficient hybrid approach for quantum simulations of ballistic GNRFETs is developed herein. The proposed simulation method is based on solving the two-dimensional (2D) Poisson equation using both wavelet-based adaptive meshing and wavelet-based matrix compression techniques, self-consistently, with well-trained neural network models that imitate the mode-space (MS) nonequilibrium Green’s function (NEGF) solver in generating the charge density. The results obtained by applying the hybrid approach show good agreement with NEGF simulations. Numerical experiments reveal that the developed simulation method can offer a speed-up of about one order of magnitude over the conventional MS NEGF approach. The encouraging results obtained in this work indicate that the developed numerical model is particularly appropriate for incorporation into nanoelectronic device simulators to investigate future GNRFET-based circuits.
ISSN:1569-8025
1572-8137
DOI:10.1007/s10825-019-01350-2