The semiconductor manufacturing industry is characterized by high-precision processes, complex equipment interdependencies, and substantial capital investment in fabrication systems. Equipment failures in such environments can lead to significant production losses, reduced yield, and increased operational costs. Traditional maintenance strategies, including reactive and preventive approaches, are often insufficient for addressing the dynamic and data-intensive nature of modern semiconductor fabrication processes. Consequently, there is a growing need for intelligent, data-driven maintenance solutions capable of anticipating equipment failures and optimizing maintenance interventions.
This study presents a comprehensive predictive maintenance framework for semiconductor fabrication equipment based on data-driven monitoring systems. The proposed approach integrates real-time sensor data acquisition, advanced signal processing, feature engineering, and hybrid predictive modeling techniques. A combination of time-series analysis and machine learning algorithms is employed to capture both temporal degradation patterns and nonlinear relationships in equipment behavior. The framework also incorporates remaining useful life estimation and decision-support mechanisms to enable proactive maintenance scheduling.
The model is evaluated using simulated multivariate datasets representative of semiconductor fabrication environments, including parameters such as temperature, vibration, pressure, and process load. The results demonstrate that the proposed hybrid model significantly outperforms traditional approaches, achieving high prediction accuracy, improved failure detection rates, and reduced false alarms. Furthermore, the framework enables early identification of equipment anomalies, providing sufficient lead time for maintenance intervention and minimizing unplanned downtime.
Operational analysis indicates substantial improvements in overall equipment effectiveness, along with notable reductions in maintenance costs and system downtime. These findings underscore the potential of predictive maintenance as a transformative strategy for enhancing reliability and efficiency in semiconductor manufacturing.
This study contributes to the advancement of smart manufacturing by providing a scalable and adaptable predictive maintenance framework tailored to high-precision industrial systems. The integration of data-driven monitoring and advanced analytics offers a robust foundation for the implementation of intelligent maintenance strategies in semiconductor fabrication facilities.