Efficient yield estimation of multiband patch antennas by polynomial chaos‐based Kriging

Leifsson, Leifur; Koziel, Slawomir

doi:10.1002/jnm.2722

Cited by 38 publications

(33 citation statements)

References 23 publications

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“…However, the overall cost of setting up the surrogates within all iterations of the sequential algorithm is low. Furthermore, these algorithms can be viewed as illustrations of the two conceptual approaches, whereas the literature offers a number of well refined techniques, typically following either of these paradigms (eg, [15][16][17][18][19][20][21]31 ). In this work, the aim is to develop a technique that maintains the simplicity of the one-shot approach while constructing the surrogate at a reasonable cost.…”

Section: Yield Optimization Using Performance-driven Surrogatesmentioning

confidence: 99%

“…Instead, the widely used approaches rely on fast surrogate models. [15][16][17][18][19][20][21] Similarly, as in the case of statistical analysis, the popular techniques include response surface approximations, 15 space mapping, 28 neural networks, 29 and polynomial chaos expansion (PCE). 30 However, due to a high cost of setting up the surrogate valid within broader ranges of the system parameters, otherwise necessary to conduct the optimization process, iterative methods seem to be more economical.…”

Section: Introductionmentioning

confidence: 99%

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Rapidtolerance‐awaredesign of miniaturized microwave passives by means ofconfined‐domainsurrogates

Pietrenko‐Dabrowska

2020

Int J Numerical Modelling

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The effects of uncertainties, primarily manufacturing tolerances but also incomplete information about operating conditions or material parameters, can be detrimental to the performance of microwave components. Quantification of such effects is essential to ensure a meaningful evaluation of the structure, in particular, its reliability under imperfect fabrication procedures. The improvement of the circuit robustness can be achieved by reducing sensitivity to geometry/material parameter deviations, which requires optimization of suitably chosen statistical performance metrics such as the yield. The prerequisite for the latter is statistical analysis. In the case of compact circuits, it is executed through full-wave electromagnetic (EM) analysis. The fundamental difficulty, that is, the high CPU cost, can be alleviated by the employment of fast surrogate models, which is the method of choice for the majority of contemporary approaches. Despite its advantages, a practical challenge of surrogate-assisted design is the initial computational overhead related to metamodel construction. As a workaround, this work proposes the employment of a recently introduced concept of constrained modelling, where the surrogate domain is confined only to contain the essential subsets of the parameter space. In the context of yield optimization, the domain needs to correspond to directions featuring maximum variability of the circuit responses (particularly the parts thereof that affect the yield value in the most significant way) with respect to its geometry parameters. The small volume of the domain spanned by such directions permits setting up an accurate model using a fraction of training data samples required by conventional methods. The proposed technique is demonstrated using a miniaturized microstrip rat-race coupler with its yield optimized at the cost of just a few dozen of EM simulations of the circuit. EM-based Monte Carlo simulations corroborate the reliability of the approach.

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Section: Yield Optimization Using Performance-driven Surrogatesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Rapidtolerance‐awaredesign of miniaturized microwave passives by means ofconfined‐domainsurrogates

Pietrenko‐Dabrowska

2020

Int J Numerical Modelling

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“…Furthermore, as approximation models are exclusively based on sampled high-fidelity model data, it is straightforward to apply them in different engineering disciplines. Among many available modeling methods, the following ones are particularly popular: polynomial regression [35], artificial neural networks [36], radial basis function interpolation [37], kriging [38], support-vector regression [39], [40], polynomial chaos expansion [41]- [43], and, recently, PC kriging [44]. Unfortunately, data-driven surrogates exhibit an important disadvantage, which is a rapid increase of the number of training data samples required to ensure usable accuracy of the model as a function of the number of independent parameters and their ranges (a socalled curse of dimensionality).…”

Section: Introductionmentioning

confidence: 99%

Design-Oriented Two-Stage Surrogate Modeling of Miniaturized Microstrip Circuits With Dimensionality Reduction

2020

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Contemporary microwave design heavily relies on full-wave electromagnetic (EM) simulation tools. This is especially the case for miniaturized devices where EM cross-coupling effects cannot be adequately accounted for using equivalent network models. Unfortunately, EM analysis incurs considerable computational expenses, which becomes a bottleneck whenever multiple evaluations are required. Common simulation-based design tasks include parametric optimization and uncertainty quantification. These can be accelerated using fast replacement models, among which the data-driven surrogates are the most popular. Notwithstanding, a construction of approximation models for microwave components is hindered by the dimensionality issues as well as high nonlinearity of system characteristics. A partial alleviation of the mentioned difficulties can be achieved with the recently reported performance-driven modeling methods, including the nested kriging framework. Therein, the computational benefits are obtained by appropriate confinement of the surrogate model domain, spanned by a set of pre-optimized reference designs, and by focusing on the parameter space region that contains high quality designs with respect to the considered performance figures. This paper presents a methodology that incorporates the concept of nested kriging and enhances it by explicit dimensionality reduction based on spectral decomposition of the reference design set. Extensive verification studies conducted for a compact rat-race coupler and a three-section impedance matching transformer demonstrate superiority of the presented approach over both the conventional techniques and the nested kriging in terms of modeling accuracy. Design utility of our surrogates is corroborated through application cases studies. INDEX TERMS Microwave design; compact circuits; surrogate modeling; domain confinement; principal component analysis; dimensionality reduction.

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“…Due to their stochastic nature, quantification requires performing statistical analysis [7]- [9]. Reducing the effects of tolerances normally entails stochastic design that aims at improving statistical performance measures, e.g., the yield [10], [11]. The second type of uncertainties are systematic (or epistemic) ones, related to the lack of knowledge of the operating conditions (temperature, radius of bending of a wearable antenna, etc.).…”

Section: Introductionmentioning

confidence: 99%

“…One of the practical issues related to the use of surrogates is a potentially high cost of setting up the model, especially for higher-dimensional parameter spaces. Some of the recent approaches are arguably more economical in that sense, e.g., PC kriging [10], where low-order polynomial traditionally employed as a trend function is replaced by the PCE surrogate. Other possibilities include reduction of the problem dimensionality (e.g., using principal component analysis [21]), incorporating variablefidelity simulations by means of space mapping [22], or cokriging [23], as well as combinations of various approaches such as surrogate modeling and model order reduction [24].…”

Section: Introductionmentioning

confidence: 99%

Expedited Yield Optimization of Narrow- and Multi-Band Antennas Using Performance-Driven Surrogates

2020

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Uncertainty quantification is an important aspect of engineering design, also pertaining to the development and performance evaluation of antenna systems. Manufacturing tolerances as well as other types of uncertainties, related to material parameters (e.g., substrate permittivity) or operating conditions (e.g., bending) may affect the antenna characteristics. In the case of narrow-or multi-band antennas, this usually leads to frequency shifts of the operating bands. Quantifying these effects is imperative to adequately assess the design quality, either in terms of the statistical moments of the performance parameters or the yield. Reducing the antenna sensitivity to parameter deviations is even more essential when increasing the probability of the system satisfying the prescribed requirements is of concern. The prerequisite of such procedures is statistical analysis, normally carried out at the level of full-wave electromagnetic (EM) analysis. While necessary to ensure reliability, it entails considerable computational expenses, often prohibitive. Following the recently fostered concept of constrained modeling, this paper proposes a simple technique for rapid surrogate-assisted yield optimization of narrow-and multi-band antennas. The keystone of the approach is an appropriate definition of the optimization domain. This is realized by considering a few pre-optimized designs that represent the directions of the major changes of the antenna resonant frequencies and operating bands. Due to a small volume of such a domain, an accurate replacement model can be established therein using a small number of training samples, and employed to improve the antenna yield. Verification results obtained for a ring-slot antenna, a dual-band and a triple-band uniplanar dipoles indicate that the optimization process can be accomplished at low cost of a few dozen of EM simulations: 62, 74 and 132 EM simulations, respectively. Result reliability is validated through comparisons with EM-based Monte Carlo simulations. INDEX TERMS Uncertainty quantification; tolerance-aware design; yield optimization; multi-band antennas; performance-driven modeling.

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Efficient yield estimation of multiband patch antennas by polynomial chaos‐based Kriging

Cited by 38 publications

References 23 publications

Rapidtolerance‐awaredesign of miniaturized microwave passives by means ofconfined‐domainsurrogates

Rapidtolerance‐awaredesign of miniaturized microwave passives by means ofconfined‐domainsurrogates

Design-Oriented Two-Stage Surrogate Modeling of Miniaturized Microstrip Circuits With Dimensionality Reduction

Expedited Yield Optimization of Narrow- and Multi-Band Antennas Using Performance-Driven Surrogates

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