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 Using Digital Twins
CASE STUDY: Li-Ion Battery EOL Prediction
Abstract:
A Lithium-Ion battery end-Of-Life predictor illustrates the benefits of using digital twin (DT) technology to mirror the process states in predicting battery capacity. The solution uses two levels of DTs; the level 1 DT has been successfully developed to predict battery capacity; the level 2 DT has been developed to predict regeneration amplitude. Future work will focus on extending the level 2 DT to model battery use behavior in order to predict battery end-of-life.