Deep Learning-Based ASM-ESD Forward I-V Parameter Extraction

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.

Authors

Fredo Chavez (ORCID: https://orcid.org/0000-0002-5046-6832)
Sourabh Khandelwal (ORCID: https://orcid.org/0000-0001-8833-6462)

Institutions

Macquarie University (AU)

Publication Details

Published: 2025-04-23
DOI: https://doi.org/10.1109/isqed65160.2025.11014331
Citations: 2
Primary Topic: Electrostatic Discharge in Electronics
Type: article
Field-Weighted Citation Impact: 1.46

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article

Deep Learning-Based ASM-ESD Forward I-V Parameter Extraction

Fredo Chavez, Sourabh Khandelwal

Apr 23, 2025

2 citations

Electrostatic Discharge in Electronics

1.46

article

Deep Learning-Based ASM-ESD Forward I-V Parameter Extraction

Fredo Chavez, Sourabh Khandelwal

April 23, 2025article en

2 citations

Abstract

Macquarie University (AU)

Openalex Percentile: Top 17%

Electrostatic Discharge in Electronics

1.46

DOI Source OpenAlex

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Deep Learning-Based ASM-ESD Forward I-V Parameter Extraction — Fredo Chavez, Sourabh Khandelwal · (2025) | TGRS Research Map | TGRS