Machine Learning Based Data Validation for Signal Integrity and Power Integrity Using Supervised Time Series Classification

• Published in: IEEE transactions on electromagnetic compatibility Vol. 66; no. 6; pp. 2150 - 2158
• Main Authors: Hassab, Youcef, Hillebrecht, Til, Lurz, Fabian, Schuster, Christian
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.12.2024; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Classification; Computational electromagnetics; Data validation; Electromagnetic compatibility; electromagnetic compatibility (EMC); Feature selective validation; Human performance; Labeling; Machine learning; power integrity; Scattering parameters; Signal integrity; Simulation; Time series; Time series analysis; time series classification (TSC); Visualization
• ISSN: 0018-9375; 1558-187X
• DOI: 10.1109/TEMC.2024.3474917

A novel approach for the validation of data in signal integrity and power integrity using machine learning is proposed. This approach presents an alternative to the feature selective validation method outlined in the IEEE Standard 1597.1 for the validation of computational electromagnetics, computer modeling and simulations. The proposed approach focuses on replicating the human visual assessment by using data collected and labeled by expert engineers to train time series classification networks that predict the degree of agreement between two curves. The trained networks are then used for the systematic and automated validation of 1-D datasets. The performance and suitability of this approach for systematic data validation is evaluated and discussed. The trained network surpasses the single human subjects in predicting the expert opinion with an accuracy higher than 70%.