ANN Performance for the Prediction of High-Speed Digital Interconnects over Multiple PCBs
| Autor: | Christian Morten Schierholz, Cheng Yang, Christian Schuster, Katharina Scharff |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Artificial neural network
Training data sets 020206 networking & telecommunications 02 engineering and technology 03 medical and health sciences Printed circuit board 0302 clinical medicine Backplane Frequency domain Hardware_INTEGRATEDCIRCUITS 0202 electrical engineering electronic engineering information engineering Electronic engineering Scattering parameters Signal integrity 030217 neurology & neurosurgery Daughterboard |
| Zdroj: | 2020 IEEE 29th Conference on Electrical Performance of Electronic Packaging and Systems (EPEPS). |
| DOI: | 10.1109/epeps48591.2020.9231490 |
| Popis: | In this paper the performance and the accuracy of artificial neural networks for the prediction of high-speed digital interconnects up to 100 GHz on printed circuit boards are analyzed and evaluated. The prediction accuracy is evaluated both for scattering parameters in frequency domain as well as weighted power sums thereof. The interconnects considered all contain a backplane connected to a daughtercard, showing two via arrays each. Several parameter variations of the basic setup lead to a wide range of possible transmission and crosstalk parameters. Training data sets are obtained using physics-based via modeling up to 100 GHz. Approximately 7000 data sets were made available in total for this study. Neural networks are able to predict the overall link behavior. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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