Automated Anomaly Detection and Predictive Maintenance in High-Throughput Semiconductor Manufacturing using Graph Neural Networks and Hyper-Score Analysis

**Abstract:** Semiconductor manufacturing processes are intricate and sensitive, requiring meticulous monitoring and proactive maintenance to ensure yield and efficiency. This paper introduces a novel framework for automated anomaly detection and predictive maintenance utilizing Graph Neural Networks (GNNs) augmented with a Hyper-Score analysis system. We leverage a heterogeneous network representing equipment interdependencies, process parameters, and historical performance data to identify deviations from normal operation with exceptional accuracy, and predict potential failures allowing for preemptive intervention. The testbed deploys optimized algorithms for rapid processing of data streams, significantly reducing downtime and improving overall production efficiency. Our approach precisely caters to the demands of real-time industrial control while emphasizing rigorous validity checks through detailed algorithm calibration and probabilistic validation.