Advances in Radio Science
Advances in Radio Science
ARS
Articles | Volume 21
Articles | Volume 21
https://doi.org/10.5194/ars-21-37-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/ars-21-37-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 21
01 Dec 2023
 | 01 Dec 2023

Anomaly Detection with Decision Trees for AI Assisted Evaluation of Signal Integrity on PCB Transmission Lines

Emre Ecik, Werner John, Julian Withöft, and Jürgen Götze

Viewed

Total article views: 176 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
137 35 4 176 4 4
  • HTML: 137
  • PDF: 35
  • XML: 4
  • Total: 176
  • BibTeX: 4
  • EndNote: 4
Views and downloads (calculated since 01 Dec 2023)
Cumulative views and downloads (calculated since 01 Dec 2023)

Viewed (geographical distribution)

Total article views: 174 (including HTML, PDF, and XML) Thereof 174 with geography defined and 0 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Latest update: 10 May 2024
Download
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.
Read more