Yield Optimization using Hybrid Gaussian Process Regression and a Genetic Multi-Objective Approach
This work addresses computational efficiency and robustness in electromagnetic device design, representing an incremental improvement.
The authors tackled the problem of uncertainty quantification and minimization in electromagnetic device design by proposing a hybrid approach combining Monte Carlo analysis with Gaussian Process Regression, achieving superior performance over classic methods on a dielectrical waveguide benchmark.
Quantification and minimization of uncertainty is an important task in the design of electromagnetic devices, which comes with high computational effort. We propose a hybrid approach combining the reliability and accuracy of a Monte Carlo analysis with the efficiency of a surrogate model based on Gaussian Process Regression. We present two optimization approaches. An adaptive Newton-MC to reduce the impact of uncertainty and a genetic multi-objective approach to optimize performance and robustness at the same time. For a dielectrical waveguide, used as a benchmark problem, the proposed methods outperform classic approaches.