Articles | Volume 21
https://doi.org/10.5194/ars-21-25-2023
https://doi.org/10.5194/ars-21-25-2023
01 Dec 2023
 | 01 Dec 2023

Mathematical optimization and machine learning to support PCB topology identification

Ilda Cahani and Marcus Stiemer

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Short summary
A framework to assist to the modeling, classification, and identification of PCB designs involving different topological approaches is proposed based on the analysis of scattering parameters. The underlying idea is to break down the relevant information hidden in the network topology to a flattened latent space via an encoder-decoder structure. Various machine learning and optimization methods are proposed using the trained encoder or decoder as upstream or downstream component.
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