AVS 45th International Symposium
    Manufacturing Science and Technology Group Wednesday Sessions
       Session MS-WeA

Invited Paper MS-WeA7
Using Wafermap Data for Automated Yield Analysis@footnote 1@

Wednesday, November 4, 1998, 4:00 pm, Room 317

Session: Process Control and Yield from Tool to Factory
Presenter: K.W. Tobin, Oak Ridge National Laboratory
Authors: K.W. Tobin, Oak Ridge National Laboratory
T.P. Karnowski, Oak Ridge National Laboratory
S.S. Gleason, Oak Ridge National Laboratory
D. Jensen, SEMATECH
F. Lakhani, SEMATECH
C. Long, SEMATECH
Correspondent: Click to Email
To be productive and profitable in a modern semiconductor fabrication environment, it is required that large amounts of manufacturing data be collected and maintained. This includes data collected from in-line and off-line wafer inspection systems and from the process equipment itself. This data is increasingly being relied upon to design new processes, control and maintain tools, and to provide the information needed for rapid yield learning and prediction. Because of increasing device complexity, the amount of data being generated is outstripping the yield engineer's ability to effectively monitor and correct unexpected trends and excursions. The 1997 SIA National Technology Roadmap for Semiconductors highlights a need to address these issues through "automated data reduction algorithms to source defects from multiple data sources and to reduce defect sourcing time." In this paper, we will discuss the current state of yield management automation and the role that SEMATECH and the Oak Ridge National Laboratory@footnote 2@ are taking in directing and developing new technologies that will provide the yield engineer with higher levels of automated data reduction and analysis. Yield management systems have been evolving over the past decade from a primary role of database storage and retrieval to systems that provide timely insight into the current state of manufacturing. The evolutionary process can be described in terms of five fundamental steps: (1) infrastructure and database management; (2) processes that add context to the data, i.e., that add information; (3) the use of data and context to find patterns, i.e., extract information; (4) methods of interpreting patterns, i.e., extracting knowledge; (5) and the automated application of process knowledge to yield management. In this paper we will focus on step (2), technologies which add context to data. In particular, we will discuss ORNL's contributions to the fields of automatic defect classification (ADC) and whole-wafer spatial signature analysis (SSA) for optical and electrical test data. We will also discuss preliminary results in the field of manufacturing-specific, content-based image retrieval (MSCBIR). MSCBIR is an image-based datamining technology that allows engineers to search a large image repository using an image of a semiconductor defect event as a query to locate other images that are similar in appearance. This exciting new technology is valuable due to the highly image-oriented approach taken by the yield engineer in problem solving, and the vast quantities of images stored in yield-management databases (approximately 70% of the total data). The ability to automatically extract content from raw manufacturing data will be a key factor for automating the discovery of knowledge in the dynamic semiconductor manufacturing environment. The current state of the art in yield management is only now beginning to comprehend these capabilities. Potential future applications of this knowledge in areas such as auto-sourcing statistical process control, condition-based maintenance of process tools, and yield prediction will also be briefly presented. @FootnoteText@ @footnote 1@K.W.T. (Correspondence): E-mail tobinkwjr@ornl.gov; WWW: http://www-ismv.ic.ornl.gov; Telephone: (423) 574-8521; Fax: (423) 574-6663. @footnote 2@Work Performed for SEMATECH, Austin Texas, under Contract No. ERD-95-1340 and prepared by OAK RIDGE NATIONAL LABORATORY, Oak Ridge, Tennessee, 37831-6285, managed by LOCKHEED MARTIN ENERGY RESARCH CORP. for the U.S. DEPARTMENT OF ENERGY under contract DE-AC05-96OR22464. @footnote 3@ K.W. Tobin, S.S. Gleason, F. Lakhani, and M.H. Bennett, "Automated Analysis for Rapid Defect Sourcing and Yield Learning", Future Fab International, Issue 4, Vol. 1, Technology Publishing Ltd., London 1997, p. 313.