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Drift-Diffusion Versus Monte Carlo Simulated ON-Current Variability in Nanowire FETs

Daniel Nagy, Guillermo Indalecio, Antonio J. Garcia-Loureiro, Gabriel Espineira, Muhammad Elmessary Orcid Logo, Karol Kalna Orcid Logo, Natalia Seoane

IEEE Access, Volume: 7, Pages: 12790 - 12797

Swansea University Authors: Daniel Nagy, Muhammad Elmessary Orcid Logo, Karol Kalna Orcid Logo

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DOI (Published version): 10.1109/access.2019.2892592

Abstract

Variability of semiconductor devices is seriously limiting their performance at nanoscale. The impact of variability can be accurately and effectively predicted by computer-aided simulations in order to aid future device designs. Quantum corrected (QC) drift-diffusion (DD) simulations are usually em...

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Published in: IEEE Access
ISSN: 2169-3536
Published: Institute of Electrical and Electronics Engineers (IEEE) 2019
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URI: https://cronfa.swan.ac.uk/Record/cronfa48909
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spelling 2020-10-19T14:46:53.4263118 v2 48909 2019-02-19 Drift-Diffusion Versus Monte Carlo Simulated ON-Current Variability in Nanowire FETs f6efefac27b3523cc876c78741c44643 Daniel Nagy Daniel Nagy true false 4be2e9acb658a7cabbc80ea75b6dfea8 0000-0001-9732-9010 Muhammad Elmessary Muhammad Elmessary true false 1329a42020e44fdd13de2f20d5143253 0000-0002-6333-9189 Karol Kalna Karol Kalna true false 2019-02-19 FGSEN Variability of semiconductor devices is seriously limiting their performance at nanoscale. The impact of variability can be accurately and effectively predicted by computer-aided simulations in order to aid future device designs. Quantum corrected (QC) drift-diffusion (DD) simulations are usually employed to estimate the variability of state-of-the-art non-planar devices but require meticulous calibration. More accurate simulation methods, such as QC Monte Carlo (MC), are considered time consuming and elaborate. Therefore, we predict TiN metal gate work-function granularity (MGG) and line edge roughness (LER) induced variability on a 10-nm gate length gate-all-around Si nanowire FET and perform a rigorous comparison of the QC DD and MC results. In case of the MGG, we have found that the QC DD predicted variability can have a difference of up to 20% in comparison with the QC MC predicted one. In case of the LER, we demonstrate that the QC DD can overestimate the QC MC simulation produced variability by a significant error of up to 56%. This error between the simulation methods will vary with the root mean square (RMS) height and maximum source/drain n -type doping. Our results indicate that the aforementioned QC DD simulation technique yields inaccurate results for the ON-current variability. Journal Article IEEE Access 7 12790 12797 Institute of Electrical and Electronics Engineers (IEEE) 2169-3536 14 1 2019 2019-01-14 10.1109/access.2019.2892592 COLLEGE NANME Science and Engineering - Faculty COLLEGE CODE FGSEN Swansea University 2020-10-19T14:46:53.4263118 2019-02-19T14:40:10.7933530 Faculty of Science and Engineering School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Electronic and Electrical Engineering Daniel Nagy 1 Guillermo Indalecio 2 Antonio J. Garcia-Loureiro 3 Gabriel Espineira 4 Muhammad Elmessary 0000-0001-9732-9010 5 Karol Kalna 0000-0002-6333-9189 6 Natalia Seoane 7 0048909-19022019144228.pdf nagy2019.pdf 2019-02-19T14:42:28.7670000 Output 5010965 application/pdf Version of Record true 2019-02-19T00:00:00.0000000 true eng
title Drift-Diffusion Versus Monte Carlo Simulated ON-Current Variability in Nanowire FETs
spellingShingle Drift-Diffusion Versus Monte Carlo Simulated ON-Current Variability in Nanowire FETs
Daniel Nagy
Muhammad Elmessary
Karol Kalna
title_short Drift-Diffusion Versus Monte Carlo Simulated ON-Current Variability in Nanowire FETs
title_full Drift-Diffusion Versus Monte Carlo Simulated ON-Current Variability in Nanowire FETs
title_fullStr Drift-Diffusion Versus Monte Carlo Simulated ON-Current Variability in Nanowire FETs
title_full_unstemmed Drift-Diffusion Versus Monte Carlo Simulated ON-Current Variability in Nanowire FETs
title_sort Drift-Diffusion Versus Monte Carlo Simulated ON-Current Variability in Nanowire FETs
author_id_str_mv f6efefac27b3523cc876c78741c44643
4be2e9acb658a7cabbc80ea75b6dfea8
1329a42020e44fdd13de2f20d5143253
author_id_fullname_str_mv f6efefac27b3523cc876c78741c44643_***_Daniel Nagy
4be2e9acb658a7cabbc80ea75b6dfea8_***_Muhammad Elmessary
1329a42020e44fdd13de2f20d5143253_***_Karol Kalna
author Daniel Nagy
Muhammad Elmessary
Karol Kalna
author2 Daniel Nagy
Guillermo Indalecio
Antonio J. Garcia-Loureiro
Gabriel Espineira
Muhammad Elmessary
Karol Kalna
Natalia Seoane
format Journal article
container_title IEEE Access
container_volume 7
container_start_page 12790
publishDate 2019
institution Swansea University
issn 2169-3536
doi_str_mv 10.1109/access.2019.2892592
publisher Institute of Electrical and Electronics Engineers (IEEE)
college_str Faculty of Science and Engineering
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hierarchy_top_id facultyofscienceandengineering
hierarchy_top_title Faculty of Science and Engineering
hierarchy_parent_id facultyofscienceandengineering
hierarchy_parent_title Faculty of Science and Engineering
department_str School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Electronic and Electrical Engineering{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Electronic and Electrical Engineering
document_store_str 1
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description Variability of semiconductor devices is seriously limiting their performance at nanoscale. The impact of variability can be accurately and effectively predicted by computer-aided simulations in order to aid future device designs. Quantum corrected (QC) drift-diffusion (DD) simulations are usually employed to estimate the variability of state-of-the-art non-planar devices but require meticulous calibration. More accurate simulation methods, such as QC Monte Carlo (MC), are considered time consuming and elaborate. Therefore, we predict TiN metal gate work-function granularity (MGG) and line edge roughness (LER) induced variability on a 10-nm gate length gate-all-around Si nanowire FET and perform a rigorous comparison of the QC DD and MC results. In case of the MGG, we have found that the QC DD predicted variability can have a difference of up to 20% in comparison with the QC MC predicted one. In case of the LER, we demonstrate that the QC DD can overestimate the QC MC simulation produced variability by a significant error of up to 56%. This error between the simulation methods will vary with the root mean square (RMS) height and maximum source/drain n -type doping. Our results indicate that the aforementioned QC DD simulation technique yields inaccurate results for the ON-current variability.
published_date 2019-01-14T03:59:36Z
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