A Surrogate Modeling Space Definition Method for Efficient Filter Yield Optimization

Surrogate models are widely used in filter yield optimization methods to improve efficiency, which can be divided into online and offline. State-of-the-art offline surrogate model-based filter yield optimization methods are shown to be effective for filter cases with more than ten sensitive design variables. In these methods, a keystone is the appropriate definition of the space for building the surrogate model, deciding success/failure, or at least the efficiency of the yield optimization. However, there is a lack of systematic methods to achieve it. To address this challenge, a new method, called pattern search optimization-based surrogate modeling space definition method (PSOMSD), is proposed. The performance of PSOMSD is demonstrated by a real-world filter case with 14 sensitive design variables. Analysis shows the appropriateness of the defined surrogate modeling space and advantages compared to empirical methods.