Parallel Space-Mapping Based Yield-Driven em Optimization Incorporating Trust Region Algorithm and Polynomial Chaos Expansion

Space mapping (SM) methodology has been recognized as a powerful tool for accelerating electromagnetic (EM)-based yield optimization. This paper proposes a novel parallel space-mapping based yield-driven EM optimization technique incorporating trust region algorithm and polynomial chaos expansion (PCE). In this technique, a novel trust region algorithm is proposed to increase the robustness of the SM surrogate in each iteration during yield optimization. The proposed algorithm updates the trust radius of each design parameter based on the effectiveness of minimizing the $l_{1}$ objective function using the surrogate, thereby increasing the robustness of the SM surrogate. Moreover, for the first time, parallel computation method is incorporated into SM-based yield-driven design to accelerate the overall yield optimization process of microwave structures. The use of parallel computation allows the surrogate developed in the proposed technique to be valid in a larger neighborhood than that in standard SM, consequently increasing the speed of finding the optimal yield solution in SM-based yield-driven design. Lastly, the PCE approach is incorporated into the proposed technique to further speed up yield verification on the fine model. Compared with the standard SM-based yield optimization technique with sequential computation, the propose

Rapid Multi-Band Patch Antenna Yield Estimation using Polynomial Chaos-Kriging

Yield estimation of antenna systems is important to check their robustness with respect to the uncertain sources. Since the Monte Carlo sampling-based real physics simulation model evaluations are computationally intensive, this work proposes the polynomial chaos-Kriging (PC-Kriging) metamodeling technique for fast yield estimation. PC-Kriging integrates the polynomial chaos expansion (PCE) as the trend function of Kriging metamodel since the PCE is good at capturing the function tendency and Kriging is good at matching the observations at training points. The PC-Kriging is demonstrated with an analytical case and a multi-band patch antenna case and compared with direct PCE and Kriging metamodels. In the analytical case, PC-Kriging reduces the computational cost by around 42% compared with PCE and over 94% compared with Kriging. In the antenna case, PC-Kriging reduces the computational cost by over 60% compared with Kriging and over 90% compared with PCE. In both cases, the savings are obtained without compromising the accuracy