Journal of the Korean Institute of Electrical and Electronic Material Engineers (한국전기전자재료학회논문지)

The Korean Institute of Electrical and Electronic Material Engineers (한국전기전자재료학회)
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Defect Prediction Using Machine Learning Algorithm in Semiconductor Test Process

기계학습 알고리즘을 이용한 반도체 테스트공정의 불량 예측

  • Jang, Suyeol (Department of Electrical Engineering, Korea University) ;
  • Jo, Mansik (Department of Electrical Engineering, Korea University) ;
  • Cho, Seulki (Department of Electrical Engineering, Korea University) ;
  • Moon, Byungmoo (Department of Electrical Engineering, Korea University)
  • 장수열 (고려대학교 전기전자공학과) ;
  • 조만식 (고려대학교 전기전자공학과) ;
  • 조슬기 (고려대학교 전기전자공학과) ;
  • 문병무 (고려대학교 전기전자공학과)
  • Received : 2018.08.21
  • Accepted : 2018.09.11
  • Published : 2018.11.01
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Abstract

Because of the rapidly changing environment and high uncertainties, the semiconductor industry is in need of appropriate forecasting technology. In particular, both the cost and time in the test process are increasing because the process becomes complicated and there are more factors to consider. In this paper, we propose a prediction model that predicts a final "good" or "bad" on the basis of preconditioning test data generated in the semiconductor test process. The proposed prediction model solves the classification and regression problems that are often dealt with in the semiconductor process and constructs a reliable prediction model. We also implemented a prediction model through various machine learning algorithms. We compared the performance of the prediction models constructed through each algorithm. Actual data of the semiconductor test process was used for accurate prediction model construction and effective test verification.

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References

  1. R. Uzsoy, C. Y. Lee, and L. A. Martin-Vega, J. IIE Trans., 24, 47 (1992). [DOI: https://doi.org/10.1080/07408179208964233]
  2. X. Fan, G. Q. Zhang, W. D. van Driel, L. J. Ernst, IEEE Trans. Compon. Packag. Technol., 31, 252 (2008). [DOI: https://doi.org/10.1109/TCAPT.2008.921629]
  3. V. Chandola, A. Banerjee, and V. Kumar, J. ACM Comput. Surv., 41, 15 (2009). [DOI: https://doi.org/10.1145/1541880.1541882]
  4. C. Chen, A. Liaw, and L. Breiman, University of California, Berkeley, 110, 1 (2004).
  5. P. Kang and S. Cho, Int. Conf. Neural Inf. Process., 4232, 837 (2006). [DOI: https://doi.org/10.1007/11893028_93]
  6. N. V. Chawla, K. W. Bowyer, L. O. Hall, and W. P. Kegelmeyer, J. Artif. Intell. Res., 16, 321 (2002). [DOI: https://doi.org/10.1613/jair.953]
  7. J. R. Quinlan, Mach. Learn., 1, 81 (1986). [DOI: https://doi.org/10.1007/BF00116251]
  8. N. S. Altman, Am. Stat., 46, 175 (1992). [DOI: https://doi.org/10.1080/00031305.1992.10475879]
  9. F. Rosenblatt, Psychol. Rev., 65, 386 (1958). [DOI: https://doi.org/10.1037/h0042519]
  10. V. N. Vapnik, IEEE Trans. Neural Networks, 10, 988 (1999). [DOI: https://doi.org/10.1109/72.788640]
  11. L. Breiman, Mach. Learn., 45, 5 (2001). [DOI: https://doi.org/10.1023/A:1010933404324]
  12. A. Liaw and M. Wiener, R News, 2, 18 (2002).
  13. R. Kohavi, Proc. IJCAI '95 Proceedings of the 14th International Joint Conference on Artificial Intelligence - Volume 2 (Montreal, Quebec, Canada, 1995) p. 1137.
  14. M. Kubat, R. C. Holte, and S. Matwin, Mach. Learn., 30, 195 (1998). [DOI: https://doi.org/10.1023/A:1007452223027]