Analysis of Parameter Variability in Integrated Devices by Partial Least Squares Regression

Mourad Larbi (1) , Riccardo Trinchero (2) , Flavio G Canavero (2) , Philippe Besnier , Madhavan Swaminathan (1)

1 ECE GeorgiaTech - School of Electrical and Computer Engineering - Georgia Insitute of Technology
2 Polito - Politecnico di Torino = Polytechnic of Turin

This paper focuses on the application of the partial least squares (PLS) regression to the uncertainty quantification of the responses of complex stochastic systems. It considers the development of a surrogate model using a limited set of training samples in order to estimate statistical quantities of the system output with relatively low computational cost compared to the standard brute force Monte Carlo (MC) simulation. The performance and the strength of the proposed modeling scheme is investigated for an integrated voltage regulator (IVR) with 8 random variables. The results highlight the ability of the PLS regression to deals with complex nonlinear problems with very few principal components, also providing important insights about the input variables.

Mots clés: Index Terms-Machine learning; uncertainty quantification; surrogate model; PLS regression; sensitivity analysis; integrated voltage regulator (IVR)

Domaines: Electronique; Electromagnétisme

Mourad Larbi, Riccardo Trinchero, Flavio G Canavero, Philippe Besnier, Madhavan Swaminathan. Analysis of Parameter Variability in Integrated Devices by Partial Least Squares Regression. IEEE 24th Workshop on Signal and Power Integrity (SPI), May 2020, Köln, Germany. ⟨hal-03007096⟩