KCIST Kolloquium - Data Analytics and Process Control in Semiconductor Manufacturing
When Big Data Gets Physical

  • Venue:

    wbk Institut für Produktionstechnik am Fasanengarten, Geb. 50.36, Raum 010 (Oder via Teams: Hier klicken, um teilzunehmen)

  • Date:

    06 June 2023, 16:00 Uhr 

  • Speaker:

    Dragan Djurdjanovic obtained his B.S. in Mechanical Eng. and B.S. 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. and Ph.D. in Mechanical Eng. in 2002 from the Univ. of Michigan, Ann Arbor. Dr. Djurdjanovic explores methodologies for transforming data into useful information and further into operational decisions, with applications in advanced manufacturing, automotive engineering and biomedical systems. He served as the Director of the NSF Industry-University Cooperative Research Center on Intelligent Maintenance Systems at the University of Texas at Austin from 2013 until 2020, and currently serves as the Associate Director of the NSF Engineering Research Center on Nanomanufacturing Systems for Mobile Computing and Mobile Energy Technologies at the University of Texas at Austin. Dr. Djurdjanovic is the recipient of several prizes and recognitions, including the 2018 August-Wilhelm Scheer Visiting Professorship from Technical University of Munich and 2006 Outstanding Young Manufacturing Engineer Award from the Society of Manufacturing Engineers.

  • Abstract: 

    The seminar will offer an overview of methodologies which enable mining of large amounts of data in semiconductor manufacturing based on the methods which fuse first-principle physics with concepts of Machine Learning (ML) and Artificial Intelligence (AI). A palette of examples from various segments of semiconductor manufacturing will be shown, illustrating how combining physics and ML/AI methods in different ways enables solutions in data curation, metrology, process control and operational optimization. The seminar will end with a discussion on the future needs of and opportunities for research in the realm of data analytics in semiconductor manufacturing.