Predictive Quality Degradation Assessment in Semiconductor Wafer Fabrication Using Multivariate Statistical Process Control and Bayesian Optimization

**Abstract:** Semiconductor wafer fabrication processes are inherently complex and susceptible to subtle variations leading to quality degradation and yield loss. Traditional Statistical Process Control (SPC) methods often react to deviations after they occur, hindering proactive quality management. This paper presents a novel approach combining Multivariate Statistical Process Control (MSPC) with Bayesian Optimization (BO) to predict and mitigate quality degradation *before* it impacts yield. We utilize a dynamic, multi-step retrospective analysis methodology leveraging historical process data, advanced feature engineering, and a recurrent neural network (RNN) framework to predict wafer quality attributes (e.g., resistivity, breakdown voltage) with high accuracy. BO is then employed to optimize key process parameters in a simulated environment, demonstrating a significant reduction in projected defect rates and improved overall process stability. This system, dubbed "Proactive Quality Navigator" (PQN), allows for predictive quality control, minimizing waste and maximizing yield in challenging semiconductor manufacturing environments.