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

Abstract

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.

Yield-Constrained Optimization Design Using Polynomial Chaos for Microwave Filters

“…f obj (x, ξ ) is used for the objective function. The PC models of f yie (x, ξ ) and f obj (x, ξ ) are established using Legendre polynomials (7) and Hermite polynomials (8) as expressed in equations ( 12) and ( 13), respectively.…”

Generalized Gamma-Laguerre Polynomial Chaos to Model Random Bending of Wearable Antennas

“…Current-generation antenna systems suffer from significant variability, due to fabrication tolerances, and uncertainty, due to variable and adverse deployment conditions [1]- [4]. Important random changes in geometry and material properties are encountered both in high-end applications [5], due to operating frequencies beyond 6 GHz and corresponding small features, and in Internet-of-Things systems [6], given the cheap fabrication techniques and harsh operating environments.…”

Low-cost yield-driven design of antenna structures using response-variability essential directions and parameter space reduction

“…Needless to say, with antenna designs becoming increasingly more sophisticated, e.g., due to incorporation of metamaterials (e.g., in the form of metasurfaces) 40 – 43 , robust design is more important than ever before. If the specifications are of minimax-type (e.g., expressed through acceptable levels of antenna responses over target bandwidths 22 ), the merit function is typically the yield, maximized under the assumed probability distributions for parameter deviations. An alternative would be to maximize the input tolerance levels for which the design specifications can still be met (e.g., tolerance hypervolume maximization 23 ).…”