Rigorous Mode-Matching Method of Circular to Off-Center Rectangular Side-Coupled Waveguide Junctions for Filter Applications

Com Dev Ltd., Cambridge
IEEE Transactions on Microwave Theory and Techniques (Impact Factor: 2.24). 12/2007; 55(11):2365 - 2373. DOI: 10.1109/TMTT.2007.908662
Source: IEEE Xplore


An accurate and efficient method for analyzing circular to multiple off-center rectangular side-coupled waveguide T-junctions is developed based on a rigorous mode-matching technique. The method is very general and not limited by symmetry or other dimensional constraints to the junction. A new way to match fields on the curved interface between subregions is described in detail. The computer code developed based on this theory is highly efficient. The numerical results match with the results obtained by other methods including experimental ones. The method is used to design a side-coupled circular waveguide dual-mode filter.

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Available from: Ming Yu, Oct 12, 2014
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    • "The general geometry of the single-hole sidewall coupler is a rectangular-to-circular T-junction. It is a basic microwave structure that can be used in applications such as an ortho-mode transducer (OMT) [13], polarizer, and microwave filter [14]. "
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    • "(b) is constructed by replacing the output impedance inverter with the -parameters of the output coupling iris, as shown in Fig. 4(b). Fig. 6(a) shows another example, a side-coupled circular waveguide dual-mode filter [19], [20]. "
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    • "This allows us to develop very accurate submodel. Each substructure is simulated using EM simulator based on mode-matching method, as described in [42] "
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    ABSTRACT: Neural networks are useful for developing fast and accurate parametric model of electromagnetic (EM) structures. However, existing neural-network techniques are not suitable for developing models that have many input variables because data generation and model training become too expensive. In this paper, we propose an efficient neural-network method for EM behavior modeling of microwave filters that have many input variables. The decomposition approach is used to simplify the overall high-dimensional neural-network modeling problem into a set of low-dimensional sub-neural-network problems. By incorporating the knowledge of filter decomposition with neural-network decomposition, we formulate a set of neural-network submodels to learn filter subproblems. A new method to combine the submodels with a filter empirical/equivalent model is developed. An additional neural-network mapping model is formulated with the neural-network submodels and empirical/equivalent model to produce the final overall filter model. An H -plane waveguide filter model and a side-coupled circular waveguide dual-mode filter model are developed using the proposed method. The result shows that with a limited amount of data, the proposed method can produce a much more accurate high-dimensional model compared to the conventional neural-network method and the resulting model is much faster than an EM model.
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