Robust and Efficient Uncertainty Quantification and Validation of RFIC Isolation

Modern communication and identification products impose demanding constraints on reliability of components. Due to this statistical constraints more and more enter optimization formulations of electronic products. Yield constraints often require efficient sampling techniques to obtain uncertainty qu...

Full description

Saved in:
Bibliographic Details
Published in:Radioengineering 2014-04, Vol.23 (1), p.308-318
Main Authors: A. Di Bucchianico, J. ter Maten, R. Pulch, R. Janssen, J. Niehof, M. Hanssen, S. Kapora
Format: Article
Language:English
Subjects:
failure
floor-plan modelling
importance sampling
isolation grounding
Monte Carlo
parameterized model order reduction
reliability
RFIC isolation
sensitivity
stochastic collocation
stochastic galerkin
tail probabilities
uncertainty quantification
variation aware
yield estimation
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Modern communication and identification products impose demanding constraints on reliability of components. Due to this statistical constraints more and more enter optimization formulations of electronic products. Yield constraints often require efficient sampling techniques to obtain uncertainty quantification also at the tails of the distributions. These sampling techniques should outperform standard Monte Carlo techniques, since these latter ones are normally not efficient enough to deal with tail probabilities. One such a technique, Importance Sampling, has successfully been applied to optimize Static Random Access Memories (SRAMs) while guaranteeing very small failure probabilities, even going beyond 6-sigma variations of parameters involved. Apart from this, emerging uncertainty quantifications techniques offer expansions of the solution that serve as a response surface facility when doing statistics and optimization. To efficiently derive the coefficients in the expansions one either has to solve a large number of problems or a huge combined problem. Here parameterized Model Order Reduction (MOR) techniques can be used to reduce the work load. To also reduce the amount of parameters we identify those that only affect the variance in a minor way. These parameters can simply be set to a fixed value. The remaining parameters can be viewed as dominant. Preservation of the variation also allows to make statements about the approximation accuracy obtained by the parameter-reduced problem. This is illustrated on an RLC circuit. Additionally, the MOR technique used should not affect the variance significantly. Finally we consider a methodology for reliable RFIC isolation using floor-plan modeling and isolation grounding. Simulations show good comparison with measurements.
ISSN:1210-2512