Scalable macromodelling methodology for the efficient design of microwave filters

Abstract

The complexity of the design of microwave filters increases steadily over the years. General design techniques available in literature yield relatively good initial designs, but electromagnetic (EM) optimisation is often needed to meet the specifications. Although interesting optimisation strategies exist, they depend on computationally expensive EM simulations. This makes the optimisation process time consuming. Moreover, brute force optimisation does not provide physical insights into the design and it is only applicable to one set of specifications. If the specifications change, the design and optimisation process must be redone. The authors propose a scalable macromodel-based design approach to overcome this. Scalable macromodels can be generated in an automated way. So far the inclusion of scalable macromodels in the design cycle of microwave filters has not been studied. In this study, it is shown that scalable macromodels can be included in the design cycle of microwave filters and re-used in multiple design scenarios at low computational cost. Guidelines to properly generate and use scalable macromodels in a filter design context are given. The approach is illustrated on a state-of-the-art microstrip dual-band bandpass filter with closely spaced pass bands and a complex geometrical structure. The results confirm that scalable macromodels are proper design tools and a valuable alternative to a computationally expensive EM simulator-based design flow.