NDLI: An empirical study of design-of-experiment data mining for yield-loss diagnosis for semiconductor manufacturing

Content Provider	Springer Nature Link
Author	Chien, Chen Fu Chang, Kuo Hao Wang, Wen Chih
Copyright Year	2013
Abstract	To maintain competitive advantages, semiconductor industry has strived for continuous technology migrations and quick response to yield excursion. As wafer fabrication has been increasingly complicated in nano technologies, many factors including recipe, process, tool, and chamber with the multicollinearity affect the yield that are hard to detect and interpret. Although design of experiment (DOE) is a cost effective approach to consider multiple factors simultaneously, it is difficult to follow the design to conduct experiments in real settings. Alternatively, data mining has been widely applied to extract potential useful patterns for manufacturing intelligence. However, because hundreds of factors must be considered simultaneously to accurately characterize the yield performance of newly released technology and tools for diagnosis, data mining requires tremendous time for analysis and often generates too many patterns that are hard to be interpreted by domain experts. To address the needs in real settings, this study aims to develop a retrospective DOE data mining that matches potential designs with a huge amount of data automatically collected in semiconductor manufacturing to enable effective and meaningful knowledge extraction from the data. DOE can detect high-order interactions and show how interconnected factors respond to a wide range of values. To validate the proposed approach, an empirical study was conducted in a semiconductor manufacturing company in Taiwan and the results demonstrated its practical viability.
Starting Page	961
Ending Page	972
Page Count	12
File Format	PDF
ISSN	09565515
Journal	Journal of Intelligent Manufacturing
Volume Number	25
Issue Number	5
e-ISSN	15728145
Language	English
Publisher	Springer US
Publisher Date	2013-05-28
Publisher Place	Boston
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Data mining Design of experiment Yield enhancement Defect diagnosis Semiconductor manufacturing Production/Logistics/Supply Chain Manufacturing, Machines, Tools Control, Robotics, Mechatronics
Content Type	Text
Resource Type	Article
Subject	Industrial and Manufacturing Engineering Artificial Intelligence Software

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