Seed function combination selection for chained function filters

Abstract

An algorithm is presented for seed function combination selection for chained function filters on the basis of pre-defined manufacturing limitations. To this end, and for the first time, combinatorial analysis methods have been applied to computer-aided filter design routines. A fundamental Monte-Carlo margin has been discovered that represents an upper-bound on the worst-case pass-band return-loss responses that cannot be exceeded. This can be exploited to extend the state-of-the art in tuningless high-performance filter implementations towards higher frequencies and smaller fractional bandwidths or, alternatively, to lower the accuracy and manufacturing cost for a given set of filter specifications.