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The Impact of Clustered Defect Distributions in IC Fabrication

Author

Listed:
  • Susan L. Albin

    (Department of Industrial Engineering, Rutgers University, Piscataway, New Jersey 08855-0909)

  • David J. Friedman

    (AT&T Bell Laboratories, Murray Hill, New Jersey 07974)

Abstract

Whenever defect data is encountered in industrial quality control applications, the Poisson distribution is generally assumed to be the underlying distribution. It has been widely reported that the defect distributions in integrated circuit fabrication exhibit clustering behavior, a condition which invalidates the Poisson distribution assumption. In this paper we employ an alternative model for defect data that exhibits clustering. To demonstrate the impact of the proposed model, we use it to design acceptance sampling plans and show a dramatic difference between these plans and those determined under the Poisson distribution assumption. Although advances in quality control and techniques such as design for manufacture have eliminated the need for acceptance sampling in many areas, integrated circuit fabrication still involves processes with high variability (even using state of the art equipment). Thus acceptance sampling is widely used at various stages of manufacture to insure specified quality levels when 100 percent inspection is infeasible.

Suggested Citation

  • Susan L. Albin & David J. Friedman, 1989. "The Impact of Clustered Defect Distributions in IC Fabrication," Management Science, INFORMS, vol. 35(9), pages 1066-1078, September.
    • Handle: RePEc:inm:ormnsc:v:35:y:1989:i:9:p:1066-1078
    DOI: 10.1287/mnsc.35.9.1066
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    Citations

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    Cited by:

    1. David D. Yao & Shaohui Zheng, 1999. "Sequential Inspection Under Capacity Constraints," Operations Research, INFORMS, vol. 47(3), pages 410-421, June.
    2. Chun, Young H. & Sumichrast, Robert T., 2007. "Bayesian inspection model with the negative binomial prior in the presence of inspection errors," European Journal of Operational Research, Elsevier, vol. 182(3), pages 1188-1202, November.
    3. Kawamura Hironobu & Nishina Ken & Higashide Masanobu, 2008. "Control Charts for Particles in the Semiconductor Manufacturing Process," Stochastics and Quality Control, De Gruyter, vol. 23(1), pages 95-107, January.
    4. Bitran, Gabriel R. & Leong, Thin-Yin., 1990. "Distribution-free, uniformly-tighter linear approximations for chance-constrained programming," Working papers 3111-90., Massachusetts Institute of Technology (MIT), Sloan School of Management.
    5. L. Gong & H. Matsuo, 1997. "Control Policy for a Manufacturing System with Random Yield and Rework," Journal of Optimization Theory and Applications, Springer, vol. 95(1), pages 149-175, October.

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