Anomaly Detection with Decision Trees for AI Assisted Evaluation of Signal Integrity on PCB Transmission Lines
Emre Ecik
CORRESPONDING AUTHOR
Information Processing Lab, TU Dortmund University, 44227 Dortmund, Germany
Werner John
Pyramide2525/TU Dortmund University, 33100 Paderborn, Germany
Julian Withöft
Information Processing Lab, TU Dortmund University, 44227 Dortmund, Germany
Jürgen Götze
Information Processing Lab, TU Dortmund University, 44227 Dortmund, Germany
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Short summary
The implementation of an anomaly detection with a decision tree was investigated with respect to the evaluation of selected SI effects in PCB design. The tree based approach allows the designer to understand the proposals more easily (explainable AI). High prediction accuracies for two simple networks were achieved with the proposed method. The use of anomaly detection with a decision tree will be further developed in future work for various more complex SI applications in circuit design.
The implementation of an anomaly detection with a decision tree was investigated with respect to...