To be competitive and satisfy the rising demand of semiconductor devices, the most important challenge fabrication plants must overcome is how to minimize the production cost and maximize yield. Therefore, the possibility of equipment breakage and line shutdowns leads to a hurdle with intolerable revenue loss. Semiconductor fabrication has one of the most complicated manufacturing processes, however, in their current status, asset maintenance is often performed in a reactive manner. Conducting either wafer-based or time-based maintenance schedules based on the asset manufacturer’s recommendation leads to the equipment being under-maintained or over-maintained and not running at its peak performance. Meanwhile, other industries are rapidly moving toward full utilization of predictive maintenance (PdM) with consideration of essential impact factors such as machine and environmental conditions combined with anomaly detection based on historical data. In this paper, it is discussed how the semiconductor sector can harness the power of PdM techniques exercised in other fields such as utility, aerospace, healthcare, electronics, and consumer packaged goods (CPG) to lower its operating cost, increase asset efficiency, improve asset uptime, and improve sustainability efforts. Moreover, it is discussed how unique anomaly detection algorithms can enable companies to have a holistic view of all of their assets by using sensor data and historical behavioral patterns, which helps reduce dependencies on machinery that is equipped with select sensors.
