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2 February 1998 Fuzzy logic connectivity in semiconductor defect clustering
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Proceedings Volume 3306, Machine Vision Applications in Industrial Inspection VI; (1998) https://doi.org/10.1117/12.301244
Event: Photonics West '98 Electronic Imaging, 1998, San Jose, CA, United States
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.
© (1998) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Thomas P. Karnowski, Shaun S. Gleason, and Kenneth W. Tobin Jr. "Fuzzy logic connectivity in semiconductor defect clustering", Proc. SPIE 3306, Machine Vision Applications in Industrial Inspection VI, (2 February 1998); https://doi.org/10.1117/12.301244
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