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Defect detection in patterned wafers using anisotropic kernels

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Abstract

Wafer defect detection often relies on accurate image registration of source and reference images obtained from neighboring dies. Unfortunately, perfect registration is generally impossible, due to pattern variations between the source and reference images. In this paper, we propose a defect detection procedure, which avoids image registration and is robust to pattern variations. The proposed method is based on anisotropic kernel reconstruction of the source image using the reference image. The source and reference images are mapped into a feature space, where every feature with origin in the source image is estimated by a weighted sum of neighboring features from the reference image. The set of neighboring features is determined according to the spatial neighborhood in the original image space, and the weights are calculated from exponential distance similarity function. We show that features originating from defect regions are not reconstructible from the reference image, and hence can be identified. The performance of the proposed algorithm is evaluated and its advantage is demonstrated compared to using an anomaly detection algorithm.

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Authors and Affiliations

  1. Department of Electrical Engineering, Technion—Israel Institute of Technology, Technion City, Haifa, 32000, Israel

    Maria Zontak & Israel Cohen

Authors
  1. Maria Zontak
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  2. Israel Cohen
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Correspondence to Israel Cohen.

Additional information

This research was supported by Applied Materials Inc., Rehovot, Israel.

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Zontak, M., Cohen, I. Defect detection in patterned wafers using anisotropic kernels. Machine Vision and Applications 21, 129–141 (2010). https://doi.org/10.1007/s00138-008-0146-y

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  • DOI: https://doi.org/10.1007/s00138-008-0146-y

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