Automated Anomaly Detection & Predictive Maintenance in Semiconductor Fabrication Using Multi-Modal Data Fusion and Graph Neural Networks

**Abstract:** This paper proposes a novel framework for real-time anomaly detection and predictive maintenance in semiconductor fabrication processes leveraging multi-modal data fusion and Graph Neural Networks (GNNs). Current methodologies often rely on disparate datasets and lack the ability to dynamically adapt to process variations. Our approach integrates data from process sensors, equipment logs, and vision inspection systems into a unified representation via a multi-layered evaluation pipeline culminating in a hyper-scored reliability metric. This allows for proactive intervention, minimizing downtime and improving overall yield. We demonstrate the efficacy of this framework through simulations mimicking a critical etching process, achieving a 25% reduction in false positives compared to traditional statistical process control methods and a 10% improvement in predictive accuracy for equipment failure events. The framework is immediately commercializable within the semiconductor manufacturing industry, offering a significant return on investment through reduced operational costs and enhanced production efficiency.