Automatic semiconductor wafer map defect signature detection using a neural network classifier

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Title: Automatic semiconductor wafer map defect signature detection using a neural network classifier
Author: Radhamohan, Ranjan Subbaraya
Abstract: The application of popular image processing and classification algorithms , including agglomerative clustering and neural networks , is explored for the purpose of grouping semiconductor wafer defect map patterns . Challenges such as overlapping pattern separation , wafer rotation , and false data removal are examined and solutions proposed . After grouping , wafer processing history is used to automatically determine the most likely source of the issue . Results are provided that indicate these methods hold promise for wafer analysis applications .
URI: http : / /hdl .handle .net /2152 /ETD -UT -2010 -12 -2423
Date: 2011-02-21

Citation

Automatic semiconductor wafer map defect signature detection using a neural network classifier. Master's thesis, University of Texas at Austin. Available electronically from http : / /hdl .handle .net /2152 /ETD -UT -2010 -12 -2423 .

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