IC Modeling via Machine Learning Regressions: A Data-Driven Approach to SPICE Integration
- DOI
- 10.1109/tcpmt.2025.3584470
- Published
- 2025-09
- Container
- IEEE Transactions on Components, Packaging and Manufacturing Technology
- Publisher
- Institute of Electrical and Electronics Engineers (IEEE)
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.1109/tcpmt.2025.3584470,
title = {IC Modeling via Machine Learning Regressions: A Data-Driven Approach to SPICE Integration},
author = {Marco Atlante and Riccardo Trinchero and Igor S. Stievano and Mihai Telescu and Noël Tanguy},
year = {2025},
journal = {IEEE Transactions on Components, Packaging and Manufacturing Technology},
doi = {10.1109/tcpmt.2025.3584470},
url = {https://doi.org/10.1109/tcpmt.2025.3584470}
}RIS
TY - JOUR TI - IC Modeling via Machine Learning Regressions: A Data-Driven Approach to SPICE Integration AU - Marco Atlante AU - Riccardo Trinchero AU - Igor S. Stievano AU - Mihai Telescu AU - Noël Tanguy PY - 2025 JO - IEEE Transactions on Components, Packaging and Manufacturing Technology DO - 10.1109/tcpmt.2025.3584470 UR - https://doi.org/10.1109/tcpmt.2025.3584470 ER -
APA
Atlante, M., Trinchero, R., Stievano, I. S., Telescu, M., & Tanguy, N. (2025). IC Modeling via Machine Learning Regressions: A Data-Driven Approach to SPICE Integration. IEEE Transactions on Components, Packaging and Manufacturing Technology. https://doi.org/10.1109/tcpmt.2025.3584470
Source records
- crossref · retrieved 2026-09-26T22:45:41.886Z