A Flexible Neural Network-Based Tool for Package Second Level Interconnect Modeling
EPEPS 2023 - IEEE 32nd Conference on Electrical Performance of Electronic Packaging and Systems, Page: 1-3
2018
- 1Captures
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Metrics Details
- Captures1
- Readers1
Conference Paper Description
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.
Bibliographic Details
Institute of Electrical and Electronics Engineers (IEEE)
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