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Rule-based data mining for yield improvement in semiconductor manufacturing

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Abstract

We describe an automated system for improving yield, power consumption and speed characteristics in the manufacture of semiconductors. Data are continually collected in the form of a history of tool usage, electrical and other real-valued measurements—a dimension of tens of thousands of features. Unique to this approach is the inference of patterns in the form of binary regression rules that demonstrate a significantly higher or lower performance value for tools relative to the overall mean for that manufacturing step. Results are filtered by knowledge-based constraints, increasing the likelihood that empirically validated rules will prove interesting and worth further investigation. This system is currently installed in the IBM 300 mm fab, manufacturing game chips and microprocessors. It has detected numerous opportunities for yield and performance improvement, saving many millions of dollars.

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Authors and Affiliations

  1. IBM, T.J. Watson Research Center, P.O. Box 218, Yorktown Heights, NY, 10598, USA

    Sholom M. Weiss, Robert J. Baseman & Fateh Tipu

  2. IBM Systems and Technology Group, Hopewell Junction, NY, 12533, USA

    Christopher N. Collins, William A. Davies & Raminderpal Singh

  3. IBM Systems and Technology Group, Essex Junction, VT, 05452, USA

    John W. Hopkins

Authors
  1. Sholom M. Weiss
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  2. Robert J. Baseman
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  3. Fateh Tipu
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  4. Christopher N. Collins
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  5. William A. Davies
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  6. Raminderpal Singh
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  7. John W. Hopkins
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Correspondence to Sholom M. Weiss.

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Weiss, S.M., Baseman, R.J., Tipu, F. et al. Rule-based data mining for yield improvement in semiconductor manufacturing. Appl Intell 33, 318–329 (2010). https://doi.org/10.1007/s10489-009-0168-9

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  • DOI: https://doi.org/10.1007/s10489-009-0168-9

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