This paper aims to investigate the strategy for low-cost yield optimization of miniaturized microstrip couplers using variable-fidelity electromagnetic (EM) simulations.
Usefulness of data-driven models constructed from structure frequency responses formulated in the form of suitably defined characteristic points for statistical analysis is investigated. Reformulation of the characteristics leads to a less nonlinear functional landscape and reduces the number of training samples required for accurate modeling. Further reduction of the cost associated with construction of the data-driven model, is achieved using variable-fidelity methods. Numerical case study is provided demonstrating feasibility of the feature-based modeling for low cost statistical analysis and yield optimization.
It is possible, through reformulation of the structure frequency responses in the form of suitably defined feature points, to reduce the number of training samples required for its data-driven modeling. The approximation model can be used as an accurate evaluation engine for a low-cost Monte Carlo analysis. Yield optimization can be realized through minimization of yield within the data-driven model bounds and subsequent model re-set around the optimized design.
The investigated technique exceeds capabilities of conventional Monte Carlo-based approaches for statistical analysis in terms of computational cost without compromising its accuracy with respect to the conventional EM-based Monte Carlo.
The proposed tolerance-aware design approach proved useful for rapid yield optimization of compact microstrip couplers represented using EM-simulation models, which is extremely challenging when using conventional approaches due to tremendous number of EM evaluations required for statistical analysis.
