Polynomial-based surrogate models for uncertainty quantification of passive electronic systems

In this thesis, the state of the art of polynomial-based surrogate models for uncertainty quantification is reviewed. Different aspects of the surrogate models, including order selection, choice of polynomial basis, quadrature rules and regression-based coefficient obtain process, compression rate of principle component analysis and its relation to the order truncation, as well as the sampling of the design space, are discussed and compared. A new algorithm for sampling and a common denominator rational polynomial chaos formulation are proposed. Numerical results on an analytical distributed circuit model, a differential via structure, and a fan-out package are provided to demonstrate the applicability of the polynomial-based methods to work with passive electronic systems and structures. The proposed sampling method improved the L_\infty norm of the error compared to the simulation. And the common denominator formulation provides speed-up to the rational polynomial chaos. And finally, a conclusion and directions for future work are presented.

Name thesis:degree_name

M.S.

Level thesis:degree_level

Thesis

Discipline thesis:degree_discipline

Electrical & Computer Engr

Grantor

University of Illinois at Urbana-Champaign

Year dc:date

2022

Author dc:creator

• Shangguan, Xingjian

Contributors dc:contributor

• Chen, Xu

• Polynomial Chaos
• Rational Polynomial Chaos
• Uncertainty Quantification
• Surrogate Model
• Electronics Packaging
• Principal Component Analysis.

Statement dc:rights

• Copyright 2022 Xingjian Shangguan

Language dc:language

en, eng

Handle dc:identifier

https://hdl.handle.net/2142/117839

OAI record oai:identifier

https://www.ideals.illinois.edu/oai-pmh?verb=GetRecord&metadataPrefix=etdms&identifier=oai%3Awww.ideals.illinois.edu%3A2142%2F117839