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Analytics-statistics mixed training and its fitness to semisupervised manufacturing

Author

Listed:
  • Parag Parashar
  • Chun Han Chen
  • Chandni Akbar
  • Sze Ming Fu
  • Tejender S Rawat
  • Sparsh Pratik
  • Rajat Butola
  • Shih Han Chen
  • Albert S Lin

Abstract

While there have been many studies using machine learning (ML) algorithms to predict process outcomes and device performance in semiconductor manufacturing, the extensively developed technology computer-aided design (TCAD) physical models should play a more significant role in conjunction with ML. While TCAD models have been effective in predicting the trends of experiments, a machine learning statistical model is more capable of predicting the anomalous effects that can be dependent on the chambers, machines, fabrication environment, and specific layouts. In this paper, we use an analytics-statistics mixed training (ASMT) approach using TCAD. Under this method, the TCAD models are incorporated into the machine learning training procedure. The mixed dataset with the experimental and TCAD results improved the prediction in terms of accuracy. With the application of ASMT to the BOSCH process, we show that the mean square error (MSE) can be effectively decreased when the analytics-statistics mixed training (ASMT) scheme is used instead of the classic neural network (NN) used in the baseline study. In this method, statistical induction and analytical deduction can be combined to increase the prediction accuracy of future intelligent semiconductor manufacturing.

Suggested Citation

  • Parag Parashar & Chun Han Chen & Chandni Akbar & Sze Ming Fu & Tejender S Rawat & Sparsh Pratik & Rajat Butola & Shih Han Chen & Albert S Lin, 2019. "Analytics-statistics mixed training and its fitness to semisupervised manufacturing," PLOS ONE, Public Library of Science, vol. 14(8), pages 1-18, August.
    • Handle: RePEc:plo:pone00:0220607
    DOI: 10.1371/journal.pone.0220607
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    References listed on IDEAS

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    3. Adi L Tarca & Vincent J Carey & Xue-wen Chen & Roberto Romero & Sorin Drăghici, 2007. "Machine Learning and Its Applications to Biology," PLOS Computational Biology, Public Library of Science, vol. 3(6), pages 1-11, June.
    4. Yuan, Tao & Kuo, Way, 2008. "Spatial defect pattern recognition on semiconductor wafers using model-based clustering and Bayesian inference," European Journal of Operational Research, Elsevier, vol. 190(1), pages 228-240, October.
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