Predictive Maintenance in Semiconductor Manufacturing: Leveraging IoT Sensor Data for Equipment Reliability

Predictive maintenance (PdM) has emerged as a transformative approach in semiconductor manufacturing, leveraging IoT sensor data to enhance equipment reliability and optimize production processes. This review explores the implementation of predictive maintenance strategies in semiconductor manufacturing, focusing on the role of IoT sensor networks, advanced analytics techniques, and integration with Manufacturing Execution Systems (MES). IoT sensor networks enable comprehensive real-time data collection on equipment performance and environmental conditions, thereby providing a foundation for predictive maintenance. Time series forecasting algorithms, such as ARIMA, exponential smoothing, and machine learning-based approaches, are employed to anticipate potential equipment failures. Anomaly detection techniques, including statistical methods and machine-learning algorithms, are used to identify unusual patterns or behaviors that are indicative of impending issues. The integration of predictive maintenance insights with MES allows for real-time decision making, process optimization, and improved overall equipment effectiveness. However, challenges persist in terms of data quality, scalability, and cybersecurity, requiring ongoing research and industry collaboration. Early adopters reported significant reductions in unplanned downtime, optimized maintenance schedules, and improved product quality. As the semiconductor industry continues to evolve, predictive maintenance is expected to play a crucial role in maintaining competitiveness and meeting the growing demand. Further research, standardization efforts, and development of best practices are essential to fully realize the potential of predictive maintenance in semiconductor manufacturing.