Multi-Objective Bayesian Target Interval Optimization for Semiconductor Process Parameters

Semiconductor manufacturing is becoming increasingly complex, making optimization of process parameters both challenging and costly. In Physical Vapor Deposition (PVD) processes, high-dimensional nonlinear relationships between input parameters and output performance exacerbate these difficulties, particularly under conditions of limited data and multi-objective constraints. To tackle these challenges, we propose a Bayesian Optimization (BO) framework integrating Sparse Multi-task Gaussian Process (SMTGP) and Probability-guided Interval Search Mechanism (PRISM) for process parameter optimization. By incorporating a Multi-input Multi-output (MIMO) Predictor with Soft Physical Constraints, the framework effectively combines physical priors with learning-based modeling, enhancing predictive accuracy and reliability in low-data scenarios. Experimental evaluations across various scenarios demonstrate that the proposed method outperforms Random Search (RS) and classical BO in both efficiency and reliability.