On Space Mapping Techniques for Microwave Filter Tuning

Melgarejo, Juan Carlos; Ossorio, Javier; Cogollos, S.; Guglielmi, M.; Boria, Vicente E.; Bandler, J.W.

doi:10.1109/tmtt.2019.2944361

Cited by 41 publications

(20 citation statements)

References 26 publications

(41 reference statements)

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“…Models of the optimized structures have been implemented using a variety of numerical simulators, including commercially available EDA systems and internal CAD tools. More recently, measurement-based physical platforms have also been incorporated as "fine models" [70]- [73].…”

Section: Space Mapping Machine Learning and Bayesian Approachesmentioning

confidence: 99%

Advanced RF and Microwave Design Optimization: A Journey and a Vision of Future Trends

2021

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Section: Space Mapping Machine Learning and Bayesian Approachesmentioning

confidence: 99%

Advanced RF and Microwave Design Optimization: A Journey and a Vision of Future Trends

2021

Self Cite

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“…The overall requested time with this technique is orders of magnitude lower than that required by brute-force optimization with full-wave modeling of the filter. Several implementations of the space mapping techniques may be found in the literature, some of which specifically developed for filters optimization [89].…”

Section: E Refinement Of the Design With Full-wave Simulatorsmentioning

confidence: 99%

Emerging Trends in Techniques and Technology as Applied to Filter Design

et al. 2021

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In the last decade, the filter community has innovated both design techniques and the technology used for practical implementation. In design, the philosophy has become "if you can't avoid it, use it", a very practical engineering approach. Modes previously deemed spurious are intentionally used to create in-line networks incorporating real or imaginary transmission zeros and also reduce the number of components and thus further miniaturize; spurious responses are re-routed to increase the passband width or stopband width, frequency variation in couplings is used to create complex transfer functions, with all of these developments using what was previously avoided. Clever implementations of baluns into passive and active networks is resulting in a new generation of noise-immune filters for 5G and beyond. Finally, the use of a diakoptic approach to synthesis has appeared an evolving approach in which small blocks ("singlets", "doublets", etc.) are cascaded to implement larger networks, (reducing the need for very complex synthesis), with this new approach promising a large impact on the implementation of practical structures. Filter technology has migrated towards "observe it and then adapt it", pragmatically repurposing tools not specifically originally intended for the applications. Combinations of surface wave and bulk wave resonators with L-C networks are improving the loss characteristics of filters in the region below 2 GHz. Lightweight alloys and other materials designed for spacecraft are being used in filters intended for space, to provide temperature stability without the use of heavy alloys such as Invar. Fully-enclosed waveguide is being replaced in some cases by planar and quasiplanar structures propagating quasi-waveguide modes. This is generically referred to as SIW (Substrate Integrated Waveguide). Active filters trade noise figure for insertion loss but perhaps will offer advantage in terms of size and chip-level implementation. Finally, the era of reconfiguration might be approaching, as the basic networks are evolving, perhaps lacking only the appearance of lower-loss, higher-IP solid-state tuning elements.

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“…Other methods rely on variable-fidelity or variable-resolution techniques [14], [15], often combined with physics-based surrogate modeling procedures (e.g., space mapping [16], response correction [17], Bayesian model fusion [18], cokriging [19]). The latter are mostly applicable to local optimization purposes [20]. Surrogate-assisted methodologies involving data-driven (or approximation) models constitute a separate class of growing popularity.…”

Section: Introductionmentioning

confidence: 99%

Improved Modeling of Microwave Structures Using Performance-Driven Fully-Connected Regression Surrogate

et al. 2021

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Fast replacement models (or surrogates) have been widely applied in the recent years to accelerate simulation-driven design procedures in microwave engineering. The fundamental reason is a considerable-and often prohibitive-CPU cost of massive full-wave electromagnetic (EM) analyses related to solving common tasks such as parametric optimization or uncertainty quantification. The most popular class of surrogates are data-driven models, which are fast to evaluate, versatile, and easy to handle. Notwithstanding, the curse of dimensionality as well as the utility demands (e.g., so that the model covers sufficiently broad ranges of the system operating conditions), limit the applicability of conventional methods. A performance-driven modeling paradigm allows for mitigating these issue by focusing the surrogate setup process in a constrained domain encapsulating designs being of high quality w.r.t. the assumed figures of interest. The nested kriging framework capitalizing on this idea, renders the constrained surrogate using kriging interpolation, and has been shown to surpass traditional approaches. In pursuit of further accuracy improvements, this work incorporates the performance-driven concept into the fully-connected regression model (FRCM). The latter has been recently introduced in the context of frequency selective surfaces, and combined deep neural networks with Bayesian optimization, the latter employed to determine the network architecture and hyper-parameters. Using two examples of miniaturized microstrip couplers, our methodology is demonstrated to outperform both conventional modeling techniques and nested kriging, with reliable models constructed over multi-dimensional parameters spaces using just a few hundreds of samples.INDEX TERMS Dara-driven modeling; surrogate modeling; performance-driven surrogates; nested kriging; deep regression model; Bayesian optimization.

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On Space Mapping Techniques for Microwave Filter Tuning

Cited by 41 publications

References 26 publications

Advanced RF and Microwave Design Optimization: A Journey and a Vision of Future Trends

Advanced RF and Microwave Design Optimization: A Journey and a Vision of Future Trends

Emerging Trends in Techniques and Technology as Applied to Filter Design

Improved Modeling of Microwave Structures Using Performance-Driven Fully-Connected Regression Surrogate

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