Real-Time Anomaly Detection and Predictive Maintenance of EUV Pellicle Coating Uniformity via Spatiotemporal Hypervector Network Analysis

**Abstract:** This study introduces a novel methodology for real-time monitoring and predictive maintenance of EUV pellicle coating uniformity, a critical factor in lithographic process stability. We propose a Spatiotemporal Hypervector Network (ST-HVN) model leveraging high-resolution, continuous scanning ellipsometry (SE) data to detect subtle anomalies and predict coating degradation before it impacts production yield. The system integrates the Ingestion & Normalization Layer, Semantic Decomposition, Multi-layered Evaluation Pipeline, and Meta-Self-Evaluation Loop to provide a robust and adaptable analysis framework. This approach offers a 10x improvement over traditional statistical process control (SPC) methods in detecting early-stage defects and enables proactive interventions, minimizing downtime and maximizing pellicle lifespan. --- *This document is a part of the Freederia Research Archive. Explore our complete collection of advanced research at [en.freederia.com](https://en.freederia.com), or visit our main portal at [freederia.com](https://freederia.com) to learn more about our mission and other initiatives.*