Machine learning approach for predicting the effect of statistical variability in Si junctionless nanowire transistors

Carrillo-Nunez, H., Dimitrova, N., Asenov, A. and Georgiev, V. (2019) Machine learning approach for predicting the effect of statistical variability in Si junctionless nanowire transistors. IEEE Electron Device Letters, 40(9), pp. 1366-1369. (doi: 10.1109/LED.2019.2931839)

191552.pdf - Accepted Version



This work investigates the possibility to replace numerical TCAD device simulations with a multi-layer neural network (NN). We explore if it is possible to train the NN with the required accuracy in order to predict device characteristics of thousands of transistors without executing TCAD simulations. In order to answer this question, here we present a hierarchical multi-scale simulation study of a silicon junctionless nanowire field-effect transistor (JL-NWT) with a gate length of 150 nm and diameter of an Si channel of 8 nm. All device simulations are based on the Drift-Diffusion (DD) formalism with activated density gradient (DG) quantum corrections. For the purpose of this work, we perform statistical numerical experiments of a set of 1380 automictically different JL-NWTs. Each device has a unique random distribution of discrete dopants (RDD) within the silicon body. From those statistical simulations, we extract important figures of merit (FoM), such as OFF-current (IOFF) and ONcurrent (ION), subthreshold slope (SS) and voltage threshold (VTH). Based on those statistical simulations, we train a multi-layer NN and we compare the obtained results with a general linear model (GLM). Our work shows the potential of using NN in the field of device modelling and simulation with a potential application to significantly reduce the computational cost.

Item Type:Articles
Glasgow Author(s) Enlighten ID:Dimitrova, Miss Nadezhda and Carrillo-Nunez, Dr Hamilton and Asenov, Professor Asen and Georgiev, Professor Vihar
Authors: Carrillo-Nunez, H., Dimitrova, N., Asenov, A., and Georgiev, V.
College/School:College of Science and Engineering > School of Engineering > Electronics and Nanoscale Engineering
Journal Name:IEEE Electron Device Letters
ISSN (Online):1558-0563
Published Online:29 July 2019
Copyright Holders:Copyright © 2019 IEEE
First Published:First published in IEEE Electron Device Letters 40(9): 1366-1369
Publisher Policy:Reproduced in accordance with the copyright policy of the publisher

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Project CodeAward NoProject NamePrincipal InvestigatorFunder's NameFunder RefLead Dept
730291Quantum Electronics Device Modelling (QUANTDEVMOD)Vihar GeorgievEngineering and Physical Sciences Research Council (EPSRC)EP/P009972/1ENG - ENGINEERING ELECTRONICS & NANO ENG
3023770Quantum Simulator for Entangled Electronics (QSEE)Vihar GeorgievEngineering and Physical Sciences Research Council (EPSRC)EP/S001131/1ENG - Electronics & Nanoscale Engineering