Statistical Estimation of Crosstalk through a Modified Stochastic Reduced Order Model Approach

Tao LIANG  Flavia GRASSI  Giordano SPADACINI  Sergio Amedeo PIGNARI  

Publication
IEICE TRANSACTIONS on Communications   Vol.E101-B   No.4   pp.1085-1093
Publication Date: 2018/04/01
Online ISSN: 1745-1345
DOI: 10.1587/transcom.2017EBP3140
Type of Manuscript: PAPER
Category: Electromagnetic Compatibility(EMC)
Keyword: 
uncertainty quantification,  stochastic reduced order model,  Gaussian quadrature,  crosstalk,  

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Summary: 
This work presents a hybrid formulation of the stochastic reduced order model (SROM) algorithm, which makes use of Gauss quadrature, a key ingredient of the stochastic collocation method, to avoid the cumbersome optimization process required by SROM for optimal extraction of the sample set. With respect to classic SROM algorithms, the proposed formulation allows a significant reduction in computation time and burden as well as a remarkable improvement in the accuracy and convergence rate in the estimation of statistical moments. The method is here applied to a specific case study, that is the prediction of crosstalk in a two-conductor wiring structure with electrical and geometrical parameters not perfectly known. Both univariate and multivariate analyses are carried out, with the final objective being to compare the performance of the two SROM formulations with respected to Monte Carlo simulations.