Mathematical optimization and machine learning to support PCB topology identification

Cahani, Ilda; Stiemer, Marcus

doi:10.5194/ars-21-25-2023

Mathematical optimization and machine learning to support PCB topology identification

Publication date

2023-12-01

Document type

Konferenzbeitrag

Author

Cahani, Ilda

Stiemer, Marcus

Organisational unit

Theoretische Elektrotechnik

DOI

10.5194/ars-21-25-2023

URI

https://openhsu.ub.hsu-hh.de/handle/10.24405/18601

Conference

Kleinheubach Conference 2023 ; Miltenberg, Germany ; September 26-28, 2023

Project

progressivKI

Publisher

Copernicus Publications

Series or journal

Advances in Radio Science : Kleinheubacher Berichte

ISSN

1684-9965

Periodical volume

21

First page

25

Last page

35

Peer-reviewed

✅

Part of the university bibliography

✅

Funding(s)

Publikationsfonds der HSU/UniBw H

Language

English

Abstract

In this paper, we study an identification problem for schematics with different concurring topologies. A framework is proposed, that is both supported by mathematical optimization and machine learning algorithms. Through the use of Python libraries, such as scikit-rf, which allows for the emulation of network analyzer measurements, and a physical microstrip line simulation on PCBs, data for training and testing the framework are provided. In addition to an individual treatment of the concurring topologies and subsequent comparison, a method is introduced to tackle the identification of the optimum topology directly via a standard optimization or machine learning setup: An encoder-decoder sequence is trained with schematics of different topologies, to generate a flattened representation of the rated graph representation of the considered schematics. Still containing the relevant topology information in encoded (i.e., flattened) form, the so obtained latent space representations of schematics can be used for standard optimization of machine learning processes. Using now the encoder to map schematics on latent variables or the decoder to reconstruct schematics from their latent space representation, various machine learning and optimization setups can be applied to treat the given identification task. The proposed framework is presented and validated for a small model problem comprising different circuit topologies.

Description

This work is distributed under the Creative Commons Attribution 4.0 License (http://creativecommons.org/licenses/by/4.0/).

Version

Published version

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