Pietrenko-Dabrowska Anna, Koziel Slawomir, Golunski Lukasz
Faculty of Electronics, Telecommunications and Informatics, Gdansk University of Technology, 80-233, Gdańsk, Poland.
Engineering Optimization & Modeling Center, Reykjavik University, 102, Reykjavík, Iceland.
Sci Rep. 2022 Sep 7;12(1):15185. doi: 10.1038/s41598-022-19411-1.
Quantifying the effects of fabrication tolerances and uncertainties of other types is fundamental to improve antenna design immunity to limited accuracy of manufacturing procedures and technological spread of material parameters. This is of paramount importance especially for antenna design in the industrial context. Degradation of electrical and field properties due to geometry parameter deviations often manifests itself as, e.g., center frequency shifts or compromised impedance matching. Improving antenna performance at the presence of uncertainties is typically realized through maximization of the fabrication yield. This is normally carried out at the accuracy level of full-wave electromagnetic (EM) analysis, which incurs considerable computational expenses. The involvement of surrogate modeling techniques is the most common approach to alleviating these difficulties, yet conventional modeling methods suffer to a great extent form the curse of dimensionality. This work proposes a technique for low-cost yield optimization of antenna structures. It capitalizes on meticulous definition of the domain of the metamodel constructed for statistical analysis purposes. The domain is spanned by a limited number of essential directions being the most influential in terms of affecting antenna responses in the frequency bands of interest. These directions are determined through an automated decision-making process based on the assessment of the circuit response variability. Our approach permits maintaining small domain volume, which translates into low cost of surrogate model setup, while providing sufficient room for yield improvement. The presented method is validated using three antenna structures and favorably compared to several surrogate-assisted benchmark methods. EM-driven Monte Carlo simulation is also conducted to verify reliability of the yield optimization process.
量化制造公差和其他类型不确定性的影响，对于提高天线设计对制造工艺有限精度和材料参数技术扩散的抗扰性至关重要。这在工业环境中的天线设计中尤为重要。由于几何参数偏差导致的电气和场特性退化通常表现为，例如，中心频率偏移或阻抗匹配受损。在存在不确定性的情况下提高天线性能通常通过最大化制造良率来实现。这通常在全波电磁（EM）分析的精度水平上进行，这会产生相当大的计算成本。替代建模技术的应用是缓解这些困难的最常见方法，然而传统建模方法在很大程度上受到维度诅咒的困扰。这项工作提出了一种用于天线结构低成本良率优化的技术。它利用为统计分析目的构建的元模型域的精确定义。该域由有限数量的基本方向构成，这些方向在影响感兴趣频段的天线响应方面最具影响力。这些方向通过基于电路响应变异性评估的自动决策过程来确定。我们的方法允许保持较小的域体积，这转化为替代模型设置的低成本，同时为良率提高提供足够的空间。使用三种天线结构对所提出的方法进行了验证，并与几种替代辅助基准方法进行了有利比较。还进行了基于EM的蒙特卡罗模拟，以验证良率优化过程的可靠性。
