Precise control of humidity levels within semiconductor manufacturing environments is paramount to ensuring product quality and yield. Unsuitable conditions can induce various wafer-related defects, including corrosion, oxidation, and poor film adhesion, thereby increasing production costs and compromising equipment reliability. This paper presents an innovative artificial intelligence-based framework, Lifelong Boosting Learning (L2 Boost), for rapid and accurate environmental detection within manufacturing facilities. By utilising datasets correlating sensor data with wafer defect labels, we establish links between environmental conditions and defects. Our approach employs an L2 Boost strategy to analyse heterogeneous sensor data and identify patterns indicative of environment-induced anomalies. The proposed system enables near-real-time environmental monitoring by indirectly measuring process characteristics correlated with defects, providing an early warning mechanism for environmental control systems. Experimental results demonstrate that L2 Boost accurately identifies environment-related defects from sensor data, achieving a macro-averaged Precision of 0.9912, Recall of 0.9804, F1 of 0.9858, and an ROC-AUC of 0.9945. This research contributes to the development of intelligent environmental monitoring systems for semiconductor manufacturing, offering a cost-effective solution for maintaining optimal production conditions. Ultimately, this framework provides actionable insights and automated diagnostic capabilities that are highly useful for process engineers, facility managers, and quality control teams striving to optimize yield in smart manufacturing environments.
Yao et al. (2026) studied this question.