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Filter designers for RF/microwave requirements have long been challenged with meeting an often-conflicting set of performance demands. Often a final RF/microwave filter design is the result of a tedious, iterative process. Fortunately, computer-aided-engineering (CAE) software tools continue to improve and a new semi-automated filter design and layout procedure may help ease the journey to that final filter.

The procedure involves the integrated use of three commercial software tools and separately addresses filter synthesis, working with component models, and performing accurate circuit and electromagnetic (EM) analysis on the design. To demonstrate the effectiveness of the new procedure, a bandpass filter was synthesized from the models, with measured results impressively close to the predictions in a single design pass.

Filter synthesis is rarely easy or automatic, even for an experienced filter designer. Starting with just basic filter types—such as bandpass, bandstop, highpass, and lowpass filters—and then trying to achieve a particular set of performance parameters for one of these filter types, can turn into an almost endless iterative design process, with some designers always tempted by the challenge of achieving slightly more rejection or slightly less loss.

As some designers learn, often the simplest and most straightforward design approach builds upon a classic filter architecture. Still, classical filter topologies can suffer from any number of drawbacks that must be tackled, such as an excessive number of inductors and the need to account for circuit element losses, pad parasitics, lead parasitics, substrate effects, and interconnect effects. Failure to take any of these into account during the design process can lead to poor performance in the fabricated filter and failure to meet the target performance requirements.

Fortunately, modern CAE software tools continue to improve, creating filter designs in which computer predictions come consistently close to the measured results when those designs are finally fabricated in microstrip, stripline, or other high-frequency circuit technology. For example, such tools as Filter Solutions fron Nuhertz Technology, coupled with component and device models such as the Global Models from Modelithics, can greatly assist filter designers in need of shaving time and effort from the traditional iterative filter design process.

Filter Solutions, as the name implies, contains design functions and routines aimed at helping experienced and novice filter designers alike. The software also makes it possible to semi-automate the design process and drastically reduce the time required to take a filter from an initial design to a practical layout and fabricated component or circuit.

What makes this design software even more effective is the use of advanced measurement-based equivalent circuit Global Models from Modelithics, which accurately account for the parasitic effects of the components and circuit elements representing the filter design. By means of collaborative efforts between the two companies, they have even demonstrated excellent wideband results in the design, fabrication, and testing of five different lumped-element filters.

In using a combination of software-based tools such as the Filter Solutions software and the Modelithics models, the design process starts by establishing and entering design requirements into the filter design software, such as filter center frequency, passband bandwidth, and stopband attenuation. A designer must select a filter type (such as bandpass, bandstop, etc.), topology, and other design options; the software will suggest a filter design based on ideal components.

Circuit-element models

Circuit-element models

A desired substrate material and the Modelithics models are then selected and the design schematic diagram and layout will be exported to Microwave Office from AWR for further analysis. The AWR software will predict the impact of parasitic effects that might have been missed earlier in the design process (which are included in the Modelithics models) and plot the expected results (Fig. 1).

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