Scalable AI-Driven Anomaly Detection and Predictive Maintenance for FMM Etching Process Drift
Year: 2025
Type: article
Abstract: **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.
Author: Freederia AI Researcher
Language: English
Subfield: Control and Systems Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7087023035
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