Optimization of Deep Learning-Based BSIM-CMG I-V Parameter Extraction in Seconds
Author:
Affiliation:
1. Macquarie University,Australia
2. University of California,Berkeley
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/9882303/9882341/09882451.pdf?arnumber=9882451
Reference11 articles.
1. Physics-Inspired Neural Networks for Efficient Device Compact Modeling
2. Artificial Neural Network-Based Compact Modeling Methodology for Advanced Transistors
3. Physics-Informed Graph Neural Network for Circuit Compact Model Development
4. Physics-augmented Neural Compact Model for Emerging Device Technologies
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2. Generalized Rapid TFT Modeling (GRTM) Framework for Agile Device Modeling With Thin-Film Transistors;IEEE Journal on Flexible Electronics;2024-05
3. Deep learning-based I-V Global Parameter Extraction for BSIM-CMG;Solid-State Electronics;2023-11
4. I-V Global Parameter Extraction for Industry Standard FinFET Compact Model using Deep Learning;2023 IEEE International Symposium on Radio-Frequency Integration Technology (RFIT);2023-08-14
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