|
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 . |