Wafer defect prediction with statistical machine learning
Name
963214425-MIT.pdf
Description
Full printable version
Size
12.97 MB
Format
Adobe PDF
Checksum (MD5)
36c6868ce8e03dd8c8344f0be2b3244a
Author(s)
Arnold, Naomi (Naomi Aiko)
Advisor(s)
Roy Welsch and Duane Boning.
Date Issued
2016
Publisher
Massachusetts Institute of Technology
Abstract
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.
Description
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).
Subjects
Institute for Data, Systems, and Society.
Sloan School of Management.
Engineering Systems Division.
Leaders for Global Operations Program.
MIT Department
Leaders for Global Operations Program at MIT
Massachusetts Institute of Technology. Engineering Systems Division
Massachusetts Institute of Technology. Institute for Data, Systems, and Society
Sloan School of Management
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