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.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Fischetti, M.: Scaling MOSFETs to the limit: a physicist’s perspective. J. Comput. Electron. 2, 73–79 (2003)
Frank, D., Dennard, R., Nowak, E., Solomon, P., Taur, Y., Wong, H.: Device scaling limits of \(Si\) MOSFETs and their application dependences. Proc. IEEE 89, 259–288 (2001)
Jungemann, C., Decker, S., Thoma, R., Eng, W.L., Goto, H.: Phase space multiple refresh: a versatile statistical enhancement method for Monte Carlo device simulation. In: 1996 International Conference on Simulation of Semiconductor Processes and Devices, SISPAD 1996 (IEEE Cat. No. 96TH8095), pp. 65–66 (1996)
Kim, J., Shin, H., Lee, C., Park, Y.J., Min, H.S.: A new weight redistribution technique for electron-electron scattering in the MC simulation. IEEE Trans. Electron Devices 51(9), 1448–1454 (2004)
Medina-Bailon, C., et al.: Multisubband ensemble Monte Carlo analysis of tunneling leakage mechanisms in ultrascaled FDSOI, DGSOI, and FinFET devices. IEEE Trans. Electron Devices 66(3), 1145–1152 (2019). https://doi.org/10.1109/TED.2019.2890985
Medina-Bailon, C., Padilla, J., Sampedro, C., Godoy, A., Donetti, L., Gámiz, F.: Source-to-drain tunneling analysis in FDSOI, DGSOI and FinFET devices by means of multi-subband ensemble Monte Carlo. IEEE Trans. Electron Devices 65(11), 4740–4746 (2018). https://doi.org/10.1109/TED.2018.2867721
Oriols, X., Fernàndez-Díaz, E., Alvarez, A., Alarcón, A.: An electron injection model for time-dependent simulators of nanoscale devices with electron confinement: application to the comparison of the intrinsic noise of 3D-, 2D- and 1D-ballistic transistors. Solid-State Electron. 51(2), 306–319 (2007)
QuantumATK version O-2018.06. Synopsys, inc. (2018). https://www.synopsys.com/silicon/quantumatk.html
Woolard, D.L., Tian, H., Littlejohn, M.A., Kim, K.W.: Efficient ohmic boundary conditions for the Monte Carlo simulation of electron transport. IEEE Trans. Electron Devices 41(4), 601–606 (1994)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
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
Download citation
DOI: https://doi.org/10.1007/978-3-030-41032-2_47
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-41031-5
Online ISBN: 978-3-030-41032-2
eBook Packages: Computer ScienceComputer Science (R0)