Modified Knowledge-Based Neural Networks Using Control Variates for the Fast Uncertainty Quantification of On-Chip MWCNT Interconnects

Dimple, Km; Guglani, Surila; DasGupta, Anirvan; Sharma, Rohit; Roy, Sourajeet; Kaushik, Brajesh Kumar

doi:10.1109/temc.2023.3279695

Cited by 5 publications

(1 citation statement)

References 24 publications

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“…Many of the first successful approaches were based on polynomial chaos expansion (for an overview of this family of algorithms see [3]). These were followed by other machine learning inspired solutions falling mainly into the category of kernel machine regression (e.g., [4], [5], [6], [7], [8], [9]) and artificial neural networks (ANNs) (e.g., [10], [11], [12], [13], [14]). This article focuses on kernel machine regression techniques, such as: the support vector machine (SVM) regression [15], the least-squares support vector machine (LS-SVM) [16] regression, and the more recent vector-valued kernel ridge regression (KRR) [6], [7], [17], which have been proven particularly effective for various microelectronics and radio-frequency applications [4], [5], [8].…”

Section: Introductionmentioning

confidence: 99%

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

Telescu,

Trinchero,

Soleimani

et al. 2023

IEEE Trans. Electromagn. Compat.

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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.

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