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Spatial failure patterns in wafer defect maps are very useful for root cause analysis, which is essential for yield optimization in the manufacturing process. Especially, the scratch defect type is the most challenging pattern to recognize because the position, shape, size and curvature vary widely from one scratch to another. Discontinuity points within scratches also contribute to the low recognition rate, and such points are often hidden defective dies that become reliability threat. Previous studies on defect pattern identification show that the recognition rate for scratches is among the lowest in all patterns even if the overall accuracy is high. In this paper, we propose a novel scratch pattern recognition method. In this paper, image processing techniques are applied to recognize scratch patterns in wafer maps. The Hough transform is first employed to identify line segments, from which scratches can be reconstructed. In contrast to previous machine-learning based methods, there is no need to train a complicated prediction model so that the computation time is small. The method is validated by using wafer maps from six real products and public data. Experimental results show that the proposed method achieves high recall, precision and accuracy in all cases. [ABSTRACT FROM AUTHOR]

Wafer failure pattern recognition can be used for root cause analysis, which is very important for yield learning. Recently, TestDNA was proposed to improve diagnosis resolution with data collected from wafer test. Previous studies on wafer failure pattern recognition using machine learning achieve good classification results. In this letter, we propose to enhance the classification accuracy with the help of spatial information and ensemble learning algorithms. Experimental results indicate that the proposed method can further improve the accuracy by 8.9%. [ABSTRACT FROM AUTHOR]

The spatial failure patterns in wafer defect maps can be related to problems in the manufacturing and test process. Therefore, failure pattern recognition can be used for root cause analysis, which is very important for defect diagnosis resolution improvement and yield learning. However, previous studies show that wafers with recognizable failure patterns only account for a small part of all defective wafers. In order to further improve diagnosis resolution with data collected from wafer test, we propose a novel way to present results of various test items in order to facilitate feature extraction. The test results are exhibited in a DNA-like signature called TestDNA such that sources of wafer defects can be effectively and efficiently identified. Since more defect information can be incorporated into a TestDNA, more defect types of wafer defects can be identified and predicted with higher accuracy. Experimental results show that the proposed TestDNA method can be used to identify and predict more defect types such that both higher diagnosis resolution and prediction accuracy are achieved. [ABSTRACT FROM AUTHOR]