Scalable macromodelling methodology for the efficient design of microwave filters

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. In this paper, we show that these scalable macromodels can be included in the design cycle of a microwave filter and re-used in multiple design scenarios at a low computational cost. We illustrate the approach on a state-of-the-art design example: a microstrip dual-band bandpass filter with closely spaced pass bands.