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Proceedings Paper

Fuzzy logic connectivity in semiconductor defect clustering
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Paper Abstract

In joining defects on semiconductor wafer maps into clusters, it is common for defects caused by different sources to overlap. Simple morphological image processing tends to either join too many unrelated defects together or not enough together. Expert semiconductor fabrication engineers have demonstrated that they can easily group clusters of defects from a common manufacturing problem source into a single signature. Capturing this thought process is ideally suited for fuzzy logic. A system of rules was developed to join disconnected clusters based on properties such as elongation, orientation, and distance. The clusters are evaluated on a pair-wise basis using the fuzzy rules and are joined or not joined based on a defuzzification and threshold. The system continuously re- evaluates the clusters under consideration as their fuzzy memberships change with each joining action. The fuzzy membership functions for each pair-wise feature, the techniques used to measure the features, and methods for improving the speed of the system are all developed. Examples of the process are shown using real-world semiconductor wafer maps obtained from chip manufacturers. The algorithm is utilized in the Spatial Signature Analyzer software, a joint development project between Oak Ridge National Lab. and SEMATECH.

Paper Details

Date Published: 2 February 1998
PDF: 10 pages
Proc. SPIE 3306, Machine Vision Applications in Industrial Inspection VI, (2 February 1998); doi: 10.1117/12.301244
Show Author Affiliations
Thomas P. Karnowski, Oak Ridge National Lab. (United States)
Shaun S. Gleason, Oak Ridge National Lab. (United States)
Kenneth W. Tobin, Oak Ridge National Lab. (United States)


Published in SPIE Proceedings Vol. 3306:
Machine Vision Applications in Industrial Inspection VI
A. Ravishankar Rao; Ning S. Chang, Editor(s)

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