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Tree-Based Boosting for Efficient Estimation of S-Parameters for Package Electrical Analysis
Date
2024-01-01
Author
Özese, Doǧanay
Baydoǧan, Mustafa Gökçe
Durgun, Ahmet Cemal
Aygün, Kemal
Metadata
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This work is licensed under a
Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License
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Cite This
We propose a gradient boosted tree surrogate model for S-parameter prediction in high frequency structures with limited training data. Compared to data-hungry neural networks, our approach achieves reasonable accuracy and trains significantly faster.
Subject Keywords
gradient boosted trees
,
second level interconnect
,
surrogate model
URI
https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85212693371&origin=inward
https://hdl.handle.net/11511/113097
DOI
https://doi.org/10.1109/epeps61853.2024.10753981
Conference Name
33rd IEEE Conference on Electrical Performance of Electronic Packaging and Systems, EPEPS 2024
Collections
Department of Electrical and Electronics Engineering, Conference / Seminar
Citation Formats
IEEE
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BibTeX
D. Özese, M. G. Baydoǧan, A. C. Durgun, and K. Aygün, “Tree-Based Boosting for Efficient Estimation of S-Parameters for Package Electrical Analysis,” presented at the 33rd IEEE Conference on Electrical Performance of Electronic Packaging and Systems, EPEPS 2024, Toronto, Kanada, 2024, Accessed: 00, 2025. [Online]. Available: https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85212693371&origin=inward.
