Prof. Mark J. Kushner

University of Michigan, USA
Mark J. Kushner is the William P. Allis Distinguished University Professor in the Electrical Engineering and Computer Science Department at the University of Michigan, USA. He received the Ph.D. in Applied Physics from the California Institute of Technology, and served on the technical staffs of Sandia National Laboratory, Lawrence Livermore National Laboratory and Spectra Technology before joining the University of Illinois at Urbana-Champaign in 1986 where he was the Founder Professor of Engineering and served in many administrative roles. Prof. Kushner was the Dean of Engineering and the James and Katherine Melsa Professor at Iowa State University before joining the University of Michigan in 2008 as founding director of the Michigan Institute for Plasma Science and Engineering. Prof. Kushner's research areas are low temperature plasmas, their fundamental properties and technological applications, in which he has extensively published.  He has served on and chaired several US National Academies policy advising studies, including the 2020 Decadal Report on Plasma Science, and several US Department of Energy (DOE) and Department of Defense advisory panels.  He is director of the DOE Low Temperature Plasma Science Center and is a member of the US National Academy of Engineering.  

Talk title: Progress towards the digital twin for plasma microelectronics fabrication
The rapid advancement in microelectronics fabrication and device capability – Moore's Law – is being challenged by those devices approaching atomic dimensions.  Increasing device performance is now relying on more complexity and new materials as limits are being reached in the ability to shrink devices. The transition to 3-dimensional devices (e.g., 3D-NAND memory) reflects the trend to improve performance by stacking devices as opposed to shrinking.  With the majority of manufacturing steps involving plasmas (etching, deposition, cleaning, implantation) this added complexity is stressing both developing new plasma processes and the economics.  An industry wide effort to address these challenges is the digital twin – conceptually, a computational representation of the entire fabrication process, from delivery of blank wafers to packaging of final devices.  Digital twins for plasma processing would address equipment scale production of fluxes of reactive species (radical, ions, electrons, photons) delivered to the wafer and the nanoscale evolution of features in response to those fluxes; and so be tools used in process development.  Digital twins would also track the state of the reactor predicting, for example, erosion of components or coatings on plasma facing surfaces, and recommending when preventative maintenance is necessary.  In this talk, the status of one aspect of the digital twin, computational models for plasma etching, will be discussed.  The current status of reactor and feature scale modeling for plasma etching will be reviewed, with assessments of progress needed to achieve digital twin status.  The roles of fundamental physics-based modeling and that of machine learning in the digital twin will be discussed.