Predictive Maintenance in Semiconductor Manufacturing - AI Machine Learning Application for Downtime Reduction

Semiconductor manufacturing is a precision-driven and capital-intensive process where unplanned downtime can lead to significant production losses. Predictive maintenance (PdM) offers a proactive solution by anticipating equipment failures using data-driven techniques. This paper presents a comprehensive analysis of machine learning (ML)based approaches for PdM in semiconductor fabrication, drawing insights from current industrial practices and recent scholarly contributions. We review anomaly detection, condition monitoring, fault fingerprinting, and system-level fault diagnosis frameworks. The comparative performance of supervised, unsupervised, and hybrid ML models is analyzed, and challenges specific to semiconductor settings are addressed. This study offers strategic recommendations for model deployment and outlines future directions for intelligent maintenance systems in Industry 4.0 environments.