Advanced Parametric Modeling And Yield Optimization Approaches For Microwave Passive Components

Parametric modeling and yield optimization techniques play important roles in electromagnetic(EM)-based microwave component design.In the first part of this thesis,we propose a novel training approach for parametric modeling of microwave passive components with respect to changes in geometrical parameters using matrix Pad′e via Lanczos(MPVL)and EM sensitivities.In the proposed approach,the EM responses of passive components versus frequency are represented by pole-zero-gain transfer functions.The relationships between the poles/zeros/gain in the transfer function and geometrical variables are learnt by neural networks.A novel sensitivity-analysis-based pole/zero-matching algorithm is proposed to obtain the correct correspondences between the poles/zeros at different geometrical parameter values.Using the matched poles/zeros to train the neural networks allows us to have fast and reliable predictions for the poles/zeros subject to large geometrical variations,consequently increasing the accuracy of the overall model.In the second part of this thesis,we propose a novel surrogate-assisted yield-driven EM optimization approach combining parallel space-mapping(SM),trust region algorithm,and polynomial chaos expansion(PCE).In this approach,a novel trust region algorithm is proposed to increase the robustness of the SM surrogate in each iteration during yield optimization.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.The proposed technique can achieve a higher yield increase with shorter CPU time by reducing the number of SM iterations.As a further advancement,in the third part of this thesis,we propose a novel nonsurrogate-based approach to yield-driven EM optimization based on PCE.We first formulate a novel objective function for yield-driven EM optimization.By incorporating the PCE coefficients into the formulation,the objective function is analytically related to the nominal point.We then derive the sensitivity formulas of the PCE coefficients with respect to the nominal point,following which we derive the sensitivities of the objective function.These sensitivities are then used in gradient-based optimization algorithms to find the optimal yield solution iteratively.Using the proposed objective function,the number of EM simulations required to find the update direction and suitable step size for the change of the nominal point is reduced at each iteration of optimization.This allows the proposed technique to achieve similar yield increase using much fewer EM simulations or greater yield increase using similar number of EM simulations.All the proposed approaches are illustrated by microwave examples.