Robust reconfigurable filter design using analytic variability quantification techniques

Arthur Nieuwoudt, Jamil Kawa, Yehia Massoud

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Scopus citations


In this paper, we develop a variability-aware design methodology for reconfigurable filters used in multi-standard wireless systems. To model the impact of statistical circuit component variations on the predicted manufacturing yield, we implement several different analytic variability quantification techniques based on a double-sided implementation of the first and second order reliability methods (FORM and SORM), which provide several orders of magnitude improvement in computational complexity over statistical sampling methods. Leveraging these efficient analytic variability quantification techniques, we employ an optimization approach using Sequential Quadratic Programming to simultaneously determine the fixed and tunable/switchable circuit element values in an arbitrary-order canonical filter to improve the overall robustness of the filter design when statistical variations are present. The results indicate that reconfigurable filters and impedance matching networks designed using the proposed methodology meet the specified performance requirements with a 26% average absolute yield improvement over circuits designed using deterministic techniques.
Original languageEnglish (US)
Title of host publicationIEEE/ACM International Conference on Computer-Aided Design, Digest of Technical Papers, ICCAD
Number of pages6
StatePublished - Dec 26 2008
Externally publishedYes


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