Runtime optimization and confidence evaluation for yield-focused root cause selection algorithms

Abstract In Surface Mount Technology (SMT) processes, high-dimensional datasets pose significant computational and interpretability challenges. This work presents an extended root cause selection pipeline within a Yield Management framework, which reduces the execution time by 66 %, selects more suitable root causes, and maintains the same predictive accuracy as traditional approaches. This is achieved by explicitly integrating engineering knowledge in the form of root and cause separation into the data preprocessing, thus enhancing the suitability of detected root causes and decreasing computational time. Additionally, a dedicated confidence evaluation based on engineering knowledge quantifies the reliability of identified causes, ensuring that domain expertise guides interpretation.