Introduction

In this article, the FEKO optimisation mask feature will be used to optimise the response of a microstrip filter. General familiarity with FEKO optimisation options is assumed. The optimisation mask feature allows for the comparison of an output data array obtained from one simulation result, with a predefined array, in the evaluation of the fitness of an optimisation step.

Start by considering the following generic microstrip line structure (this is a parametric generalization of a model available in [1]):

The objective is to optimise this structure, to automatically realize specified filtering characteristics (i.e. transfer functions). The optimisation variables will be the set {l21; l22; l23}. The feed lines have 50 Ohm characteristic impedance and are driven by microstrip ports.

The following optimisation features will be employed:

• Masks: the FEKO optimiser allows the specification of masks with respect to which a given output series of values can be optimized. In this case, the behaviour of the transfer coefficient (S_21) will be optimized as a function of frequency.

• Weighted, combined, optimization goals: the FEKO optimiser allows the user to combine multiple, specified goals, such that all are targeted simultaneously. The combination action allows weighting of the individual goals before combination. The combination procedure can be an averaging step, or a minimization/maximization action on the set of weighted goals. With regards to realizing filter characteristics, this will clearly be an important feature, as the transfer coefficient needs to be either maximized or minimized depending on the frequency band. 

Low-pass realization

Consider optimising the transfer coefficient in the band 4 – 6 GHz, with a low-pass characteristic. A set of 11 linearly spaced frequency points are defined. The linear interpolation of S_21 at these points is the function that will be optimised. Note that continuous, adaptive frequency interpolation cannot be used together with optimization masks, at present. The masks specified in FEKO are shown in Figure 2.

Note the following important points regarding mask definitions:

• Optimising with respect to any given mask implies that the difference between the output function (as defined above) and the function obtained by linearly interpolating the mask data, will be optimized.
• The number of mask data points does not need to correspond with the number of output points (as the optimiser will use a piece-wise linear fitting on the mask array to determine the values for comparison at the correct points). For instance, in the present case 11 output points are specified, but only 4 mask data points. However, the mask should always span the whole range under consideration. For example, in the present case it means that points at both 4 and 6 GHz must form part of the mask definitions.

The output function is required to be less than the first mask, and greater than the second. These two sub-goals are weighed by 10 and 1 (the cut-off characteristic is considered the more important of the two) and combined by way of set maximization to form the final goal. The optimization method (algorithm) is set to Automatic and the convergence accuracy set to High. The resulting optimization required 68 iterations to converge, with the optimum parameter set found as follows:

S-parameter results for the final structure, as calculated with adaptive frequency interpolation, are shown in Figure 3. Observe the low-pass behaviour as specified. Note that the masks are nearly, but not exactly satisfied. This is due to the use of a combined goal, which forces the optimiser to a compromise. Also, with optimisation one must realize that it might not always be possible to achieve a goal exactly within the specified parameter space.

Band-reject realization

Again consider optimising the transfer coefficient in the band 4 – 6 GHz, but now towards a band-reject characteristic. Again 11 linearly spaced frequency points are specified for the analysis. The masks specified in FEKO are shown in Figure 4.

As before, the output function is required to be less than the first mask, and greater than the second. In this case the two sub-goals are weighed by 100 and 1 and combined by way of set maximization to form the final goal. The optimization method is set to Automatic and the convergence accuracy set to High. The resulting optimization required 84 iterations to converge, with the optimum parameter set found as follows:

S-parameter results for the final structure, as calculated with adaptive frequency interpolation, are shown in Figure 5. Observe the band-reject behaviour as specified.

Conclusion

Here, S_21 as a function of frequency constituted the array to which the masks were applied, but other data sets may also be targeted, such as functions of angle (e.g. a radiation pattern) or functions of position (e.g. a set of near field points).

References