Advancing Condition-Based Maintenance in the Semiconductor Industry: Innovations, Challenges and Future Directions for Predictive Maintenance

This study focuses on the criticality of failure detection and condition-based maintenance (CBM) within the semiconductor industry, employing Fault Detection and Classification (FDC) systems and Machine Learning (ML) techniques for equipment log analysis to anticipate equipment conditions and timely maintenance. Initiatives emphasize the cultivation of data engineering experts, enhancing depth in data analytics and equipment monitoring. Moreover, the imperative to advance the field lies in the development of innovative sensor technologies, a task that necessitates close collaboration with equipment manufacturers. This strategic partnership is indispensable for augmenting the precision and breadth of data acquisition. It ultimately enables more sophisticated analytics, thereby facilitating the creation of advanced predictive failure models through enhanced data capture and analysis. This paper illustrates the semiconductor sector’s competitive adoption of diverse strategies and technologies for maintenance innovation, aiming to bolster industry productivity, equipment reliability, and sustainability. Such endeavors are pivotal for outlining the future trajectory of manufacturing and ensuring sustainable growth within the industry.