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10 May 2005 Automatic classification of microlithography macrodefects using a knowledge-based system
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Abstract
The benefits of automatic classification of microlithography defects include fast and reliable rework decisions, improved root-cause analysis, and more consistent SPC data that significantly enhances yield in the lithography cell. An adaptive knowledge-based system has demonstrated the ability to accurately classify defects more than 85% of the time and is sufficiently versatile to classify new defect modes that will accompany advanced lithography processes. The knowledge-based system defines each class of defects with mathematical descriptors that include categories such as size, intensity, edge sharpness, color, etc. New defect classes can be defined with as few as three to five images of the specific defect. All defect classes are stored in the knowledge-base as rule vectors consisting of values for each descriptor. Different defect classes can share many common descriptors. However, as long as there is at least one descriptor that differentiates them, the defect class can be deemed unique. This method provides manufacturers the ability to define defects according to their existing rules and to define new defect types as they occur.
© (2005) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Michael Darwin, Pinar Kinikoglu, Yongqiang Liu, Kristin Darwin, and Jana Clerico "Automatic classification of microlithography macrodefects using a knowledge-based system", Proc. SPIE 5752, Metrology, Inspection, and Process Control for Microlithography XIX, (10 May 2005); https://doi.org/10.1117/12.599660
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