Machine Learning-Enabled Fast Prediction of GGNMOS Performance and Inverse Design for Electrostatic Discharge Applications

Machine Learning-Enabled Fast Prediction of GGNMOS Performance and Inverse Design for Electrostatic Discharge Applications

Electrostatic discharge (ESD) protection is generally required in integrated circuit (IC) chips. The grounded-gate n-channel metal-oxide-semiconductor (GGNMOS) is a popular device for ESD protection in circuit design. However, the design optimization of GGNMOS for every circuit is carried out using...

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Bibliographic Details
Main Authors: Zihan Wang, Ruichen Chen, Shengyao Lu, Ian Then, Di Niu, Xihua Wang
Format: Article
Language:English
Published: IEEE 2025-01-01
Series:IEEE Access
Subjects:
ESD protection
GGNMOS
machine learning
Sentaurus TCAD
Online Access:https://ieeexplore.ieee.org/document/11115110/
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https://ieeexplore.ieee.org/document/11115110/

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