A Flexible Neural Network-Based Tool for Package Second Level Interconnect Modeling

Date

2023-12-28

Author

Karatoprak, Furkan
Saçın, Ekin Su
Özese, Doğanay
Durgun, Ahmet Cemal
Baydoğan, Mustafa Gökçe
Aygün, Kemal
Memioğlu, Tolga

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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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This paper introduces a neural network (NN)-based practical design tool for quick assessment of package second level interconnects (SLIs) at the earlier design stages. The study addresses the well-known computational cost problem of data generation and training processes of NN implementation by proposing a flexible model approach, where the SLI geometry is divided into several building blocks, for which a separate NN model was trained. The NNs take geometrical parameters as inputs and return the complex S-parameter matrices as outputs. The electrical performance of the entire SLI geometry is obtained by cascading the S-paramaters of the building blocks.

URI

https://ieeexplore.ieee.org/abstract/document/10314939
https://hdl.handle.net/11511/112481

Conference Name

EEE 32nd Conference on Electrical Performance of Electronic Packaging and Systems (EPEPS)

Collections

Department of Electrical and Electronics Engineering, Conference / Seminar

Citation Formats

F. Karatoprak et al., “A Flexible Neural Network-Based Tool for Package Second Level Interconnect Modeling,” presented at the EEE 32nd Conference on Electrical Performance of Electronic Packaging and Systems (EPEPS), California, Amerika Birleşik Devletleri, 2023, Accessed: 00, 2024. [Online]. Available: https://ieeexplore.ieee.org/abstract/document/10314939.