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Noise and Learning in Semiconductor Manufacturing

Author

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  • Roger E. Bohn

    (School of International Relations and Pacific Studies, University of California, San Diego, La Jolla, California 92093-0519)

Abstract

Rapid technological learning is critical to commercial success in VLSI semiconductor manufacturing. This learning is done through deliberate activities, especially various types of experimentation. Such experiments are vulnerable to confounding by process noise, caused by process variability. Therefore plants with low noise levels can potentially learn more effectively than high noise plants. Detailed die yield data from five semiconductor plants were examined to estimate process noise levels. A bootstrap simulation was used to estimate the error rates of identical controlled experiments conducted in each plant. Absolute noise levels were high for all but the best plants, leading to lost learning. For example, the probability of overlooking a three percent yield improvement was above twenty percent in all but one plant. Brute-force statistical methods are either expensive or ineffective for dealing with these high noise levels. Depending on the criterion used, there was a four- to ten-fold difference among the plants.

Suggested Citation

  • Roger E. Bohn, 1995. "Noise and Learning in Semiconductor Manufacturing," Management Science, INFORMS, vol. 41(1), pages 31-42, January.
  • Handle: RePEc:inm:ormnsc:v:41:y:1995:i:1:p:31-42
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    File URL: http://dx.doi.org/10.1287/mnsc.41.1.31
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    Citations

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    Cited by:

    1. Mathews, John A. & Cho, Dong-Sung, 1999. "Combinative capabilities and organizational learning in latecomer firms: the case of the Korean semiconductor industry," Journal of World Business, Elsevier, vol. 34(2), pages 139-156, July.
    2. Frances X. Frei & Ravi Kalakota & Leslie M. Marx, 1997. "Process Variation as a Determinant of Service Quality and Bank Performance: Evidence from the Retail Banking Study," Center for Financial Institutions Working Papers 97-36, Wharton School Center for Financial Institutions, University of Pennsylvania.
    3. David Besanko & Ulrich Doraszelski & Yaroslav Kryukov & Mark Satterthwaite, 2008. "Learning-by-Doing, Organizational Forgetting, and Industry Dynamics," GSIA Working Papers 2009-E22, Carnegie Mellon University, Tepper School of Business.
    4. Donald E. Harter & Mayuram S. Krishnan & Sandra A. Slaughter, 2000. "Effects of Process Maturity on Quality, Cycle Time, and Effort in Software Product Development," Management Science, INFORMS, vol. 46(4), pages 451-466, April.
    5. Stefan H. Thomke, 1998. "Managing Experimentation in the Design of New Products," Management Science, INFORMS, vol. 44(6), pages 743-762, June.
    6. David Besanko & Ulrich Doraszelski, 2005. "Learning-by-Doing, Organizational Forgetting, and Industry Dynanmics," Computing in Economics and Finance 2005 236, Society for Computational Economics.
    7. Niemi, Petri & Huiskonen, Janne & Kärkkäinen, Hannu, 2009. "Understanding the knowledge accumulation process--Implications for the adoption of inventory management techniques," International Journal of Production Economics, Elsevier, vol. 118(1), pages 160-167, March.
    8. Christoph H. Loch & Christian Terwiesch & Stefan Thomke, 2001. "Parallel and Sequential Testing of Design Alternatives," Management Science, INFORMS, vol. 47(5), pages 663-678, May.
    9. Frances X. Frei & Ravi Kalakota & Andrew J. Leone & Leslie M. Marx, 1999. "Process Variation as a Determinant of Bank Performance: Evidence from the Retail Banking Study," Management Science, INFORMS, vol. 45(9), pages 1210-1220, September.
    10. Thomke, Stefan H., 1998. "Simulation, learning and R&D performance: Evidence from automotive development," Research Policy, Elsevier, vol. 27(1), pages 55-74, May.
    11. repec:eee:respol:v:46:y:2017:i:7:p:1215-1233 is not listed on IDEAS

    More about this item

    Keywords

    yield; learning by experimentation; noise;

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