Principal Investigator Duane Boning
There is great interest in “digital twins” to improve many aspects of semiconductor manufacturing, from increased device yield and performance, reduced consumption of energy and materials, increased flexibility, and to enable rapid uptake and scaling of new material, equipment, and process innovations. The digital twin has both physical and virtual components, with bilateral communication and control; the hope is to enable a wide range of models (of equipment, processes, wafers) at different fidelities (physical to simplified empirical, and machine-learning enabled), to support a wide range of “smart” functionalities. The road to digital twins goes through and builds upon many well-trodden paths. Here, several lines of research at MTL since the late 1980’s are highlighted, beginning with elements of the MIT Computer Aided Fabrication Environment including process flow languages, to DOE/Opt methods for automated surrogate model construction, and run by run control to track and compensate for equipment state and wear in CMP and other unit processes. The development of “statistical metrology” methods encompassed characterization and modeling of semiconductor variation, with layout pattern dependent models to identify “hot spots” in planarization, dishing, and erosion for a given design, as well as to guide dummy fill generation. An evolution from statistical to ML/AI approaches, particularly Bayesian methods, enabled design for manufacturability (DFM) for rapid MOSFET characterization, and then rapid fabrication process tuning, as well as AI-enabled anomaly detection. These and other paths bring us to an exciting next stage of the journey: by harnessing advances in sensing and data collection, AI methods, and computational power not possible at the beginning, the community is poised to create and deploy digital twins for semiconductor manufacturing.
Brian Anthony Faculty Lead, Industry Immersion Program in Mechanical Engineering Co-Director, MIT Clinical Research Center Associate Director, MIT.nano Duane Boning Clarence J. LeBel Professor in Electrical Engineering, Department of Electrical Engineering and Computer Science Erik v Head of Product & EcosystemTulip Steven Moskowitz Director of Digital Transformation, Entegris
Steven Leeb Professor of Mechanical and Electrical Engineering and Computer Science Duane Boning Clarence J. LeBel Professor, Electrical Engineering and Computer Science, MIT Alejandro Beivide García Chief Digital Officer, Acciona Infraestructures David Knezevic Co-founder & CTO, Akselos Jongyoon Han Professor of Electrical Engineering and Professor of Biological Engineering Rohit Karnik Professor & Associate Department Head for Education Department of Mechanical Engineering Robert Teich Senior Marketing Director, Analytics, Xylem Anurag Bajpayee Co-Founder and CEO, Gradiant
John Lienhard Abdul Latif Jameel Professor of Water and Food Director, Abdul Latif Jameel World Water and Food Security Lab Director, Center for Clean Water and Clean Energy MIT Department of Mechanical Engineering
The large amounts of both structured and unstructured data created in manufacturing and operations today present enormous opportunities to apply advanced analytics, machine learning and deep learning. This talk will describe specific use cases in process control and optimization; yield prediction and enhancement; defect inspection and classification and anomaly detection in time series data. Additionally, some of the unique manufacturing and operations challenges like: class imbalance, concept drift and complex multivariate time dynamics will be described. This research has led to the creation of MIT MIMO (Machine Intelligence for Manufacturing and Operations) which will be described during this talk.