Wafer defect prediction with statistical machine learning
Author(s)Arnold, Naomi (Naomi Aiko)
Leaders for Global Operations Program.
Roy Welsch and Duane Boning.
MetadataShow full item record
In the semiconductor industry where the technology continues to grow in complexity while also striving to achieve lower manufacturing costs, it is becoming increasingly important to drive cost savings by screening out defective die upstream. The primary goal of the project is to build a statistical prediction model to facilitate operational improvements across two global manufacturing locations. The scope of the project includes one high-volume product line, an off-line statistical model using historical production data, and experimentation with machine learning algorithms. The prediction model pilot demonstrates there exists a potential to improve the wafer sort process using random forest classifier on wafer and die-level datasets. Yet more development is needed to conclude final memory test defect die-level predictions are possible. Key findings include the importance of model computational performance in big data problems, necessity of a living model that stays accurate over time to meet operational needs, and an evaluation methodology based on business requirements. This project provides a case study for a high-level strategy of assessing big data and advanced analytics applications to improve semiconductor manufacturing.
Thesis: S.M. in Engineering Systems, Massachusetts Institute of Technology, School of Engineering, Institute for Data, Systems, and Society, 2016. In conjunction with the Leaders for Global Operations Program at MIT.Thesis: M.B.A., Massachusetts Institute of Technology, Sloan School of Management, 2016. In conjunction with the Leaders for Global Operations Program at MIT.Cataloged from PDF version of thesis.Includes bibliographical references (pages 81-83).
DepartmentMassachusetts Institute of Technology. Institute for Data, Systems, and Society.; Sloan School of Management.; Massachusetts Institute of Technology. Engineering Systems Division.; Leaders for Global Operations Program.; Massachusetts Institute of Technology. Engineering Systems Division; Massachusetts Institute of Technology. Institute for Data, Systems, and Society; Sloan School of Management
Massachusetts Institute of Technology
Institute for Data, Systems, and Society., Sloan School of Management., Engineering Systems Division., Leaders for Global Operations Program.