Automatic Wafer Defects Analysis Using Image Processing Techniques and Neural Networks
| Autor: | GUAN-WEI Chen, 陳冠位 |
|---|---|
| Rok vydání: | 2007 |
| Druh dokumentu: | 學位論文 ; thesis |
| Popis: | 95 Analysis of semiconductor wafer is increasingly in need of applying imaging determination, yet present process still relies partly to human visual determination for categorization. It takes time and the results are closely related to the categorization and determination techniques and professional training maturity of engineers, so it is necessary to have a digitized the process with objective information instrument to provide information and suggestion to engineers in the fastest way. In this research, we applied imaging techniques and artificial neural network to analyze wafer defects automatically. In the first, the raw image is converted into gray-level image, then use Otsu computation to search for optimal threshold automatically to do image division. They Morphology techniques to eliminate noise, but retain the characteristic pattern. The obtained profile information is based to compute the image shape feature and then overlap the profile back to the original raw image to obtain the feature of internal structure. Thereafter, the fast learning features of Radial Basis Function Network (RBFN) is used to identify the wafer defects features. It enables efficiently differentiate categorized massive wafer image information. The identification rate of the Wafer Defects Image Identification Structure as presented through this research is at 80% to 85%. |
| Databáze: | Networked Digital Library of Theses & Dissertations |
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