Machine Learning Approach for Predicting the Effect of Statistical Variability in Si Junctionless Nanowire Transistors | IEEE Journals & Magazine | IEEE Xplore

Machine Learning Approach for Predicting the Effect of Statistical Variability in Si Junctionless Nanowire Transistors


Abstract:

This letter 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...Show More

Abstract:

This letter 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. Here, in order to answer this question, 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 letter, 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 ON-current (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). This shows the potential of using NN in the field of device modeling and simulation with a potential application to significantly reduce the computational cost.
Published in: IEEE Electron Device Letters ( Volume: 40, Issue: 9, September 2019)
Page(s): 1366 - 1369
Date of Publication: 29 July 2019

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I. Introduction

Silicon nanowires have a wide spectrum of promising applications, such as current field-effect transistors [1], [2], photovoltaics [3], energy conversion and storage [4] and qubits [5]. In our previous work, we have shown an extensive comparison between simulations and experimental results for JL-NWTs with -gated region and with a channel length of 150 nm [6]. In another study, we have discussed statistical simulation results based on an ensemble of 500 JL-NWTs, where each device is atomistically unique with random distribution of discreet dopants in the channels [7]. Results obtained from those previously reported works have allowed us to suggest an improvement of the device design, predict the device performance and to extract important Figures of Merit (FoM), such as OFF-current (IOFF) and ON-current (ION), subthreshold slope (SS) and voltage threshold (VTH).

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