Modern semiconductor manufacturing tools are often complex systems of numerous interacting subsystems that operate in multiple physical domains and often follow highly nonlinear distributed dynamics. In such systems, traditional condition monitoring methods, which rely on a direct link between sensor readings and the underlying condition of the system, cannot be used.
Rather, one must acknowledge that the available sensor readings are only stochastically related to the condition of the monitored system, which therefore must be probabilistically inferred from the sensors. This talk will describe a recently proposed condition monitoring method, based on
characterizing the degradation process via a mixture of operation specific Hidden Markov Models (HMMs), with hidden states representing the unobservable degradation states of the monitored system, while its observable variables represent the available sensor readings. The new HMM based monitoring paradigm was applied to monitoring of several tools operating in major semiconductor fabs over multiple months, with orders of magnitude better performance over the traditional, purely signature-based approaches. The remainder of the talk will focus on describing how Markovian models of degradation of flexible manufacturing equipment, such as that utilized in modern semiconductor manufacturing, can be utilized to concurrently optimize the sequence of
production operations and schedule preventive maintenance for that machine. It will be shown that integrated decision-making in terms of product sequencing and maintenance operations carries significant potential benefits compared to the more traditional, fragmented decisionmaking,
The lecture will be ended with a brief summary of possible future research directions both in terms of process monitoring and in terms of operational decision-making in modern Manufacturing.
Dragan Djurdjanovic obtained his B.S. in Mechanical Eng. and in Applied Mathematics in 1997 from the Univ. of Nis, Serbia, his M.S. in Mechanical Eng. from the Nanyang Technological Univ., Singapore in 1999, and his M.S. in Electrical Eng. (Systems) and Ph.D. in Mechanical Eng. in 2002 from the Univ. of Michigan, Ann Arbor. His research interests include modeling, monitoring and control of complex systems, with applications spanning from advanced manufacturing to human body data analytics. He co-authored 56 published or accepted journal publications, 4 book chapters and more than 50 conference publications. He is a Fellow of the International Society for Asset Management and the Director of the University of Texas Industry-University Cooperative Research Center on Intelligent Maintenance Systems. Dr. Djurdjanovic was a Research Affiliate of the International Academy for Production Research (CIRP) from 2008-2014 and is the recipient of several prizes and awards, including the 2006 Outstanding Young Manufacturing Engineer Award from the Society of Manufacturing Engineers (SME),
2005 Teaching Incentive Award from the Dept. of Mechanical Eng. of the Univ. of Michigan and Nomination for the Distinguished Ph.D. Thesis from the Dept. of Mechanical Eng., Univ. of Michigan in 2003.