Wafer Scratch Pattern Reconstruction for High Diagnosis Accuracy and Yield Optimization

Katherine Shu Min Li, Leon Li Yang Chen, Peter Yi Yu Liao, Sying Jyan Wang, Andrew Yi Ann Huang, Leon Chou, Nova Cheng Yeh Tsai, Ken Chau Cheung Cheng, Gus Chang Hung Han, Chen Shiun Lee, Jwu E Chen, Hsin Tsung Liang, Chung Lung Hsu

Research output: Contribution to journalArticlepeer-review

Abstract

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.

Original languageEnglish
JournalIEEE Transactions on Semiconductor Manufacturing
DOIs
StateAccepted/In press - 2022

Keywords

  • Feature extraction
  • Image reconstruction
  • Optimization
  • Pattern recognition
  • scratch defect type
  • Shape
  • Sonogram
  • Transforms
  • wafer defect map
  • wafer defect pattern.
  • wafer test
  • yield optimization

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