Scalable macromodelling methodology for the efficient design of microwave filters

Abstract

The complexity of the design of state-of-the art microwave filters increases steadily over the years. General design techniques available in literature yield relatively good initial designs, but electromagnetic (EM) optimization is often needed to meet the desired specifications. Although interesting optimization strategies exist, they depend on computationally expensive EM simulations. This makes the optimization process time consuming. Moreover, brute force optimization does not provide physical insights in the design and it is only applicable to one set of specifications. If the specifications change, the design and optimization process must be redone. We propose to use a scalable macromodel-based design approach to overcome this. Scalable macromodels can be generated in an efficient and automated way.So far the inclusion of scalable macromodels in the design cycle of microwave filters 1 has not been studied and discussed. In this paper, we show that scalable macromodels can be included in the design cycle of microwave filters and re-used in multiple design scenarios at a low computational cost. We give guidelines to properly generate and use scalable macromodels in a filter design context. We illustrate the approach on a stateof-the-art design example: a 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 an efficient and accurate alternative to a computationally expensive EM simulator-based design flow.