Scalable AI-Driven Anomaly Detection and Predictive Maintenance for FMM Etching Process Drift

**Abstract:** This paper introduces a novel framework for real-time anomaly detection and predictive maintenance within the fine metal mask (FMM) etching process, a critical step in OLED panel manufacturing. Traditional quality control methods rely on manual inspection and statistical process control, proving inefficient and reactive. Our solution, a multi-modal AI system incorporating Semantic & Structural Decomposition, Neural Network analysis, and Physics-informed Machine Learning (PIML) dynamically identifies deviations from nominal etching performance, enabling proactive intervention and minimizing yield loss. This system promises a 15-25% reduction in FMM scrap rates, estimated to represent a $500M - $1B market opportunity within the global OLED display industry, through increased process stability and reduced downtime. Our approach leverages readily available sensor data and existing etching models, maximizing immediate commercial viability.