Abstract
Novel numerical techniques are needed in advanced simulation tools in order to accurately describe the behavior of nanoelectronic devices. In this work, two different numerical techniques for statistical enhancement are included in a 2D Multi-Subband Ensemble Monte Carlo (MS-EMC) simulator. First, the consideration of the Fermi-Dirac statistics for the boundary conditions in the ohmic contacts instead of the Boltzmann ones provides a more accurate picture of the distribution function. Second, the energy-dependent weight model reduces the stochastic noise that the superparticles with very high energy introduce in the device performance. In this work, we study the impact of both numerical techniques in two of the potential candidates to extend the CMOS technology: the Fully-Depleted Silicon-On-Insulator (FDSOI) and the FinFET devices. We show that the choice of the Fermi-Dirac statistics has the same impact in both the FDSOI and the FinFET, whereas the energy-dependent weight model has more significance in the FDSOI than in the FinFET because the latter has better electrostatic integrity.
This work was supported by the U.K. EPSRC Projects No. EP/P009972/1 and EP/S001131/1.
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Medina-Bailon, C. et al. (2020). Techniques for Statistical Enhancement in a 2D Multi-subband Ensemble Monte Carlo Nanodevice Simulator. In: Lirkov, I., Margenov, S. (eds) Large-Scale Scientific Computing. LSSC 2019. Lecture Notes in Computer Science(), vol 11958. Springer, Cham. https://doi.org/10.1007/978-3-030-41032-2_47
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DOI: https://doi.org/10.1007/978-3-030-41032-2_47
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