Deep learning-based ASM-ESD forward I-V parameter extraction

Fredo Chavez; Sourabh Khandelwal

doi:10.1109/ISQED65160.2025.11014331

Deep learning-based ASM-ESD forward I-V parameter extraction

Fredo Chavez, Sourabh Khandelwal

School of Engineering

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

2 Citations (Scopus)

Abstract

The parameter extraction process for the advanced SPICE model for electrostatic discharge diodes (ASM-ESD) requires expert knowledge and can take several minutes to hours to finish. This paper presents a fast and accurate deep learning (DL) based methodology for extracting the ASM-ESD Forward I-V parameters to model the transmission line pulse (TLP)-IV of a device.

Original language	English
Title of host publication	Proceedings of the Twenty Sixth International Symposium on Quality Electronic Design ISQED 2025
Place of Publication	Piscataway, NJ
Publisher	Institute of Electrical and Electronics Engineers (IEEE)
Pages	302
Number of pages	1
ISBN (Electronic)	9798331509422
ISBN (Print)	9798331509439
DOIs	https://doi.org/10.1109/ISQED65160.2025.11014331
Publication status	Published - 2025
Event	26th International Symposium on Quality Electronic Design, ISQED 2025 - Hybrid, San Francisco, United States Duration: 23 Apr 2025 → 25 Apr 2025

Publication series

Name
ISSN (Print)	1948-3287
ISSN (Electronic)	1948-3295

Conference

Conference	26th International Symposium on Quality Electronic Design, ISQED 2025
Country/Territory	United States
City	Hybrid, San Francisco
Period	23/04/25 → 25/04/25

Access to Document

10.1109/ISQED65160.2025.11014331

Cite this

@inproceedings{ca136b2b7774474fbb4f4f75edb26de5,

title = "Deep learning-based ASM-ESD forward I-V parameter extraction",

abstract = "The parameter extraction process for the advanced SPICE model for electrostatic discharge diodes (ASM-ESD) requires expert knowledge and can take several minutes to hours to finish. This paper presents a fast and accurate deep learning (DL) based methodology for extracting the ASM-ESD Forward I-V parameters to model the transmission line pulse (TLP)-IV of a device.",

author = "Fredo Chavez and Sourabh Khandelwal",

year = "2025",

doi = "10.1109/ISQED65160.2025.11014331",

language = "English",

isbn = "9798331509439",

publisher = "Institute of Electrical and Electronics Engineers (IEEE)",

pages = "302",

booktitle = "Proceedings of the Twenty Sixth International Symposium on Quality Electronic Design ISQED 2025",

address = "United States",

note = "26th International Symposium on Quality Electronic Design, ISQED 2025 ; Conference date: 23-04-2025 Through 25-04-2025",

}

Chavez, F & Khandelwal, S 2025, Deep learning-based ASM-ESD forward I-V parameter extraction. in Proceedings of the Twenty Sixth International Symposium on Quality Electronic Design ISQED 2025. Institute of Electrical and Electronics Engineers (IEEE), Piscataway, NJ, pp. 302, 26th International Symposium on Quality Electronic Design, ISQED 2025, Hybrid, San Francisco, United States, 23/04/25. https://doi.org/10.1109/ISQED65160.2025.11014331

Deep learning-based ASM-ESD forward I-V parameter extraction. / Chavez, Fredo ; Khandelwal, Sourabh.
Proceedings of the Twenty Sixth International Symposium on Quality Electronic Design ISQED 2025. Piscataway, NJ: Institute of Electrical and Electronics Engineers (IEEE), 2025. p. 302.

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

TY - GEN

T1 - Deep learning-based ASM-ESD forward I-V parameter extraction

AU - Chavez, Fredo

AU - Khandelwal, Sourabh

PY - 2025

Y1 - 2025

N2 - The parameter extraction process for the advanced SPICE model for electrostatic discharge diodes (ASM-ESD) requires expert knowledge and can take several minutes to hours to finish. This paper presents a fast and accurate deep learning (DL) based methodology for extracting the ASM-ESD Forward I-V parameters to model the transmission line pulse (TLP)-IV of a device.

AB - The parameter extraction process for the advanced SPICE model for electrostatic discharge diodes (ASM-ESD) requires expert knowledge and can take several minutes to hours to finish. This paper presents a fast and accurate deep learning (DL) based methodology for extracting the ASM-ESD Forward I-V parameters to model the transmission line pulse (TLP)-IV of a device.

UR - https://www.scopus.com/pages/publications/105007552714

U2 - 10.1109/ISQED65160.2025.11014331

DO - 10.1109/ISQED65160.2025.11014331

M3 - Conference proceeding contribution

AN - SCOPUS:105007552714

SN - 9798331509439

SP - 302

BT - Proceedings of the Twenty Sixth International Symposium on Quality Electronic Design ISQED 2025

PB - Institute of Electrical and Electronics Engineers (IEEE)

CY - Piscataway, NJ

T2 - 26th International Symposium on Quality Electronic Design, ISQED 2025

Y2 - 23 April 2025 through 25 April 2025

ER -

Deep learning-based ASM-ESD forward I-V parameter extraction

Abstract

Publication series

Conference

Access to Document

Other files and links

Fingerprint

Cite this