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Proceedings Paper

Feature analysis and classification of manufacturing signatures based on semiconductor wafer maps
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Paper Abstract

Automated tools for semiconductor wafer defect analysis are becoming more necessary as device densities and wafer sizes continue to increase. Trends towards larger wafer formats and smaller critical dimensions have caused an exponential increase in the volume of defect data which must be analyzed and stored. To accommodate these changing factors, automatic analysis tools are required that can efficiently and robustly process the increasing amounts of data, and thus quickly characterize manufacturing processes and accelerate yield learning. During the first year of this cooperative research projected between SEMATECH and the Oak Ridge National Laboratory, a robust methodology for segmenting signature events prior to feature analysis and classification was developed. Based on the results of this segmentation procedure, a feature measurement strategy has been designed based on interviews with process engineers coupled with the analysis of approximately 1500 electronic wafermap files. In this paper, the authors represent an automated procedure to rank and select relevant features for use with a fuzzy pair-wise classifier and give examples of the efficacy of the approach taken. Results of the feature selection process are given for two uniquely different types of class data to demonstrate a general improvement in classifier performance.

Paper Details

Date Published: 15 April 1997
PDF: 12 pages
Proc. SPIE 3029, Machine Vision Applications in Industrial Inspection V, (15 April 1997); doi: 10.1117/12.271241
Show Author Affiliations
Kenneth W. Tobin Jr., Oak Ridge National Lab. (United States)
Shaun S. Gleason, Oak Ridge National Lab. (United States)
Thomas P. Karnowski, Oak Ridge National Lab. (United States)
Susan L. Cohen, SEMATECH (United States)

Published in SPIE Proceedings Vol. 3029:
Machine Vision Applications in Industrial Inspection V
A. Ravishankar Rao; Ning S. Chang, Editor(s)

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