Efficient Stochastic Collocation Based Variability Analysis Using Model-Order Reduction Techniques

Predicting the effect of the variability of design parameters on the performance of high-speed integrated circuits is crucial to a successful design. The conventional Monte Carlo technique is computationally expensive due to the large number of simulations and a slow convergence rate. To address the above difficulties, a novel method is presented in this thesis for time-domain stochastic analysis of large active/passive circuits with multiple stochastic parameters. The new approach reduces the computational cost of variability analysis by using the Stochastic Collocation technique. The Sparse Grid algorithm is applied to limit the growth of the computational cost with an increase in the number of stochastic parameters. In addition, the proposed method is based on the Model Order Reduction algorithms coupled with the Numerical Inverse Laplace Transform approach.

Name thesis:degree_name

Master of Applied Science (M.App.Sc.)

Level thesis:degree_level

Master's

Discipline thesis:degree_discipline

Engineering, Electrical and Computer

Grantor dc:publisher

Carleton University

Year dc:date.issued

2016

Author dc:creator

• Guo, Kai

Statement dc:rights

• Copyright © 2016 the author(s). Theses may be used for non-commercial research, educational, or related academic purposes only. Such uses include personal study, research, scholarship, and teaching. Theses may only be shared by linking to Carleton University Institutional Repository and no part may be used without proper attribution to the author. No part may be used for commercial purposes directly or indirectly via a for-profit platform; no adaptation or derivative works are permitted without consent from the copyright owner.

Language dc:language.iso

en

DOI

10.22215/etd/2016-11708

OAI identifier oai:identifier

oai:carleton.scholaris.ca:20.500.14718/39139