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An Enhanced Approach using Time Series Segmentation for Fault Detection of Semiconductor Manufacturing Process

Tian, Runfeng
http://orcid.org/0000-0003-2962-7694
http://rave.ohiolink.edu/etdc/view?acc_num=ucin1562923441016763

Abstract Details

2019, MS, University of Cincinnati, Engineering and Applied Science: Mechanical Engineering.
The semiconductor etching process is an essential and complex manufacturing process, in which the degradation is unobservable. Due to issues related to data quality and limited data quantity, the fault detection of the semiconductor etching process remains difficult. Dozens of studies in the past focused on developing algorithms based on local models to adapt to process drift. However, the issues mentioned above have not been solved completely. In order to improve the data and feature quality, an enhanced feature extraction approach using time series segmentation could be implemented. This approach absorbs the advantages of both statistical features and structural features. Meanwhile, selecting a suitable time series segmentation algorithm for feature extraction during fault detection is also important. This thesis focuses on the fault detection of the semiconductor etching process and the implementation method of the enhanced feature extraction algorithm using time series segmentation, as well as comparison of three residual based algorithms and benchmark of three time series segmentation algorithms. Performances of different algorithms are evaluated, and the results are discussed. The enhanced feature extraction algorithm is based on time series segmentation instead of conventional feature extraction. The implementation of time series segmentation algorithm requires the utilization of dynamic time warping and other techniques. Performances of different algorithms are evaluated, and the results are discussed. By implementing time series segmentation for feature extraction, improvement of model performance is observed during this study, with a high fault detection rate and a low false alarm rate, in comparison to the results using conventional feature extraction methods.
Jay Lee, Ph.D. (Committee Chair)
Janet Jiaxiang Dong, Ph.D. (Committee Member)
Jay Kim, Ph.D. (Committee Member)
97 p.
Mechanical Engineering
Semiconductor fault detection; Time series segmentation; Feature extraction

Recommended Citations

Citations

  • Tian, R. (2019). An Enhanced Approach using Time Series Segmentation for Fault Detection of Semiconductor Manufacturing Process [Master's thesis, University of Cincinnati]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1562923441016763

    APA Style (7th edition)

  • Tian, Runfeng. An Enhanced Approach using Time Series Segmentation for Fault Detection of Semiconductor Manufacturing Process. 2019. University of Cincinnati, Master's thesis. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=ucin1562923441016763.

    MLA Style (8th edition)

  • Tian, Runfeng. "An Enhanced Approach using Time Series Segmentation for Fault Detection of Semiconductor Manufacturing Process." Master's thesis, University of Cincinnati, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1562923441016763

    Chicago Manual of Style (17th edition)

ucin1562923441016763
272
© 2019, some rights reserved.Creative Commons License An Enhanced Approach using Time Series Segmentation for Fault Detection of Semiconductor Manufacturing Process by Runfeng Tian is licensed under a Creative Commons Attribution-NonCommercial-NoDerivs 3.0 Unported License. Based on a work at etd.ohiolink.edu.
This open access ETD is published by University of Cincinnati and OhioLINK.