Robust reconfigurable filter design using analytic variability quantification techniques

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.