Stochastic Time-Domain Mapping for Comprehensive Uncertainty Assessment in Eye Diagrams

• Published in: IEEE transactions on electromagnetic compatibility Vol. 65; no. 6; pp. 1930 - 1938
• Main Authors: Telescu, Mihai, Trinchero, Riccardo, Soleimani, Nastaran, Tanguy, Noel, Stievano, Igor Simone
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.12.2023; The Institute of Electrical and Electronics Engineers, Inc. (IEEE); Institute of Electrical and Electronics Engineers
• Subjects: Algorithms; Apertures; Engineering Sciences; Eye diagram; high-speed interconnects; Kernel; Kernel functions; kernel machine regressions; Machine learning; Micro and nanotechnologies; Microelectronics; Quality assessment; Regression; Shape; signal integrity; Smart materials; smart textiles; Stochastic processes; Support vector machines; Textiles; Tolerances; Training; Uncertainty; uncertainty quantification; wearable electronics; uncertainty quantification (UQ); signal integrity; kernel-machine regressions; smart textiles; eye diagram; high-speed interconnects; wearable electronics
• ISSN: 0018-9375; 1558-187X; 1558-187X
• DOI: 10.1109/TEMC.2023.3317974

The eye diagram is one of the most common tools used for quality assessment in high-speed links. This article proposes a method of predicting the shape of the inner eye for a link subject to uncertainties. The approach relies on machine learning regression and is tested on the very challenging example of flexible link for smart textiles. Several sources of uncertainties are taken into account related to both manufacturing tolerances and physical deformation. The resulting model is fast and accurate. It is also extremely versatile: rather than focusing on a specific metric derived from the eye diagram, its aim is to fully reconstruct the inner eye and enable designers to use it as they see fit. This article investigates the features and convergence of three alternative machine learning algorithms, including the single-output support vector machine regression, together with its least squares variant, and the vector-valued kernel ridge regression. The latter method is arguably the most promising, resulting in an accurate, fast and robust tool enabling a complete parametric stochastic map of the eye.