Modeling framework linking material characterization to reliability prediction

L. Larcher; V. Milo; A. Palmieri; P. La Torraca; A. Padovani; F. Nardi; M. Pesic

doi:10.1109/EDTM55494.2023.10937707

Modeling framework linking material characterization to reliability prediction

L. Larcher
, V. Milo
, A. Palmieri
, P. La Torraca
, A. Padovani
, F. Nardi
, M. Pesic

University of Modena and Reggio Emilia

Research output: Chapter in Book/Report/Conference proceedings › Chapter › peer-review

Abstract

In this paper, we present a novel multiscale material-device simulation platform linking material/defect properties to device performances and reliability. This modeling platform supports technology development acceleration exploiting tools such as advanced characterization, sensitivity analysis, and material/device codesign and optimization. The prediction accuracy, fundamental for device reliability phenomena, is extensively addressed including also the statistics.

Original language	English
Title of host publication	7th IEEE Electron Devices Technology and Manufacturing Conference
Subtitle of host publication	Strengthen the Global Semiconductor Research Collaboration After the Covid-19 Pandemic, EDTM 2023
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9798350332520
DOIs	https://doi.org/10.1109/EDTM55494.2023.10937707
Publication status	Published - 2023
Externally published	Yes
Event	7th IEEE Electron Devices Technology and Manufacturing Conference, EDTM 2023 - Seoul, Korea, Republic of Duration: 7 Mar 2023 → 10 Mar 2023

Publication series

Name	7th IEEE Electron Devices Technology and Manufacturing Conference: Strengthen the Global Semiconductor Research Collaboration After the Covid-19 Pandemic, EDTM 2023

Conference

Conference	7th IEEE Electron Devices Technology and Manufacturing Conference, EDTM 2023
Country/Territory	Korea, Republic of
City	Seoul
Period	7/03/23 → 10/03/23

Keywords

kinetic MonteCarlo (kMC)
multiscale modeling
NBTI
precursor defects
TDDB

Access to Document

10.1109/EDTM55494.2023.10937707

Cite this

Larcher, L., Milo, V., Palmieri, A., La Torraca, P., Padovani, A., Nardi, F., & Pesic, M. (2023). Modeling framework linking material characterization to reliability prediction. In 7th IEEE Electron Devices Technology and Manufacturing Conference: Strengthen the Global Semiconductor Research Collaboration After the Covid-19 Pandemic, EDTM 2023 (7th IEEE Electron Devices Technology and Manufacturing Conference: Strengthen the Global Semiconductor Research Collaboration After the Covid-19 Pandemic, EDTM 2023). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/EDTM55494.2023.10937707

Larcher, L. ; Milo, V. ; Palmieri, A. et al. / Modeling framework linking material characterization to reliability prediction. 7th IEEE Electron Devices Technology and Manufacturing Conference: Strengthen the Global Semiconductor Research Collaboration After the Covid-19 Pandemic, EDTM 2023. Institute of Electrical and Electronics Engineers Inc., 2023. (7th IEEE Electron Devices Technology and Manufacturing Conference: Strengthen the Global Semiconductor Research Collaboration After the Covid-19 Pandemic, EDTM 2023).

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title = "Modeling framework linking material characterization to reliability prediction",

abstract = "In this paper, we present a novel multiscale material-device simulation platform linking material/defect properties to device performances and reliability. This modeling platform supports technology development acceleration exploiting tools such as advanced characterization, sensitivity analysis, and material/device codesign and optimization. The prediction accuracy, fundamental for device reliability phenomena, is extensively addressed including also the statistics.",

keywords = "kinetic MonteCarlo (kMC), multiscale modeling, NBTI, precursor defects, TDDB",

author = "L. Larcher and V. Milo and A. Palmieri and \{La Torraca\}, P. and A. Padovani and F. Nardi and M. Pesic",

note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 7th IEEE Electron Devices Technology and Manufacturing Conference, EDTM 2023 ; Conference date: 07-03-2023 Through 10-03-2023",

year = "2023",

doi = "10.1109/EDTM55494.2023.10937707",

language = "English",

series = "7th IEEE Electron Devices Technology and Manufacturing Conference: Strengthen the Global Semiconductor Research Collaboration After the Covid-19 Pandemic, EDTM 2023",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

booktitle = "7th IEEE Electron Devices Technology and Manufacturing Conference",

address = "United States",

}

Larcher, L, Milo, V, Palmieri, A, La Torraca, P, Padovani, A, Nardi, F & Pesic, M 2023, Modeling framework linking material characterization to reliability prediction. in 7th IEEE Electron Devices Technology and Manufacturing Conference: Strengthen the Global Semiconductor Research Collaboration After the Covid-19 Pandemic, EDTM 2023. 7th IEEE Electron Devices Technology and Manufacturing Conference: Strengthen the Global Semiconductor Research Collaboration After the Covid-19 Pandemic, EDTM 2023, Institute of Electrical and Electronics Engineers Inc., 7th IEEE Electron Devices Technology and Manufacturing Conference, EDTM 2023, Seoul, Korea, Republic of, 7/03/23. https://doi.org/10.1109/EDTM55494.2023.10937707

Modeling framework linking material characterization to reliability prediction. / Larcher, L.; Milo, V.; Palmieri, A. et al.
7th IEEE Electron Devices Technology and Manufacturing Conference: Strengthen the Global Semiconductor Research Collaboration After the Covid-19 Pandemic, EDTM 2023. Institute of Electrical and Electronics Engineers Inc., 2023. (7th IEEE Electron Devices Technology and Manufacturing Conference: Strengthen the Global Semiconductor Research Collaboration After the Covid-19 Pandemic, EDTM 2023).

Research output: Chapter in Book/Report/Conference proceedings › Chapter › peer-review

TY - CHAP

T1 - Modeling framework linking material characterization to reliability prediction

AU - Larcher, L.

AU - Milo, V.

AU - Palmieri, A.

AU - La Torraca, P.

AU - Padovani, A.

AU - Nardi, F.

AU - Pesic, M.

PY - 2023

Y1 - 2023

N2 - In this paper, we present a novel multiscale material-device simulation platform linking material/defect properties to device performances and reliability. This modeling platform supports technology development acceleration exploiting tools such as advanced characterization, sensitivity analysis, and material/device codesign and optimization. The prediction accuracy, fundamental for device reliability phenomena, is extensively addressed including also the statistics.

AB - In this paper, we present a novel multiscale material-device simulation platform linking material/defect properties to device performances and reliability. This modeling platform supports technology development acceleration exploiting tools such as advanced characterization, sensitivity analysis, and material/device codesign and optimization. The prediction accuracy, fundamental for device reliability phenomena, is extensively addressed including also the statistics.

KW - kinetic MonteCarlo (kMC)

KW - multiscale modeling

KW - NBTI

KW - precursor defects

KW - TDDB

UR - https://www.scopus.com/pages/publications/105002319503

U2 - 10.1109/EDTM55494.2023.10937707

DO - 10.1109/EDTM55494.2023.10937707

M3 - Chapter

AN - SCOPUS:105002319503

T3 - 7th IEEE Electron Devices Technology and Manufacturing Conference: Strengthen the Global Semiconductor Research Collaboration After the Covid-19 Pandemic, EDTM 2023

BT - 7th IEEE Electron Devices Technology and Manufacturing Conference

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 7th IEEE Electron Devices Technology and Manufacturing Conference, EDTM 2023

Y2 - 7 March 2023 through 10 March 2023

ER -

Larcher L, Milo V, Palmieri A, La Torraca P, Padovani A, Nardi F et al. Modeling framework linking material characterization to reliability prediction. In 7th IEEE Electron Devices Technology and Manufacturing Conference: Strengthen the Global Semiconductor Research Collaboration After the Covid-19 Pandemic, EDTM 2023. Institute of Electrical and Electronics Engineers Inc. 2023. (7th IEEE Electron Devices Technology and Manufacturing Conference: Strengthen the Global Semiconductor Research Collaboration After the Covid-19 Pandemic, EDTM 2023). doi: 10.1109/EDTM55494.2023.10937707

Modeling framework linking material characterization to reliability prediction

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Keywords

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