Bio:
James Moyne is an Associate Research Scientist at the University of Michigan, where he received his Ph.D. degree. He is also a Consultant for Standards and Technology to the Applied Global Services group at Applied Materials working in microelectronics and pharma industries. Dr. Moyne has experience in a wide range of prediction technologies including predictive maintenance, model-based process control, virtual metrology, and yield prediction. He also has experience with smart manufacturing topics such as digital twin, and analytics. James is the author of refereed publications and holds patents in each of the above areas. He currently co-chairs the Factory Integration Thrust of the International Roadmap for Devices and Systems (IRDS) as well as the SEMI standards Information and Control Committee.
Predictive Maintenance (PdM): A Tutorial
Abstract:
PdM is the technology of utilizing process and equipment state information to predict when a tool or tool component might need maintenance, and then utilizing this information to improve maintenance procedures. PdM can be effective at reducing unscheduled downtime or relaxing maintenance schedules. An effective PdM solution not only predicts an event such as failure, but also provide a time-to-fail horizon, an indication of confidence in the prediction, and a prediction range. Effective and practical PdM solutions can be realized using digital twin technology, and by following a reusable off-line and on-line process in the solution lifecycle.