An Adaptive Antenna Sensitivity-to-Yield Analysis and Optimization Framework Using Active Learning-Based Gaussian Process Regression

Geometrical and material tolerances in fabrication can degrade electromagnetic performance and yield of antennas. This letter develops a sensitivity-to-yield (AStY) framework that links sensitivity analysis (SA) with tolerance optimization to enhance antenna yield under fabrication uncertainties. An SA method based on automatic relevance determination Gaussian process regression (ARD-GPR) is developed, where the isotropic Matérn kernel is equipped with dimension-specific length-scales to construct a principled sensitivity metric, enabling direct parameter ranking. Moreover, a variance-guided adaptive sampling with clustering is proposed to reduce simulation cost and ensure a standardized process applicable to different antenna designs. Compared with conventional Latin hypercube sampling (LHS), this sampling scheme reduces the number of numerical simulations by 30% and runtime by 39%. Numerical examples on U-slot and broadband Vivaldi antennas show yield improvements from 60.1% to 90.3% and from 76.4% to 92.2%, closely matching full tightening while requiring significantly less fabrication precision. The proposed AStY framework offers a practical route for cost-effective yield enhancement.