Segmented dynamic time warping based signal pattern classification

Jae Yeol Hong, Seung Hwan Park, Jun Geol Baek

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

The semiconductor manufacturing process is divided into fabrication process and packaging process. Fabrication process is a core process for manufacturing semiconductors and consists of about 700 unit processes. This unit process accumulates vast amounts of data, and many manufacturing companies apply data-based algorithms to manufacturing systems to improve process yield and quality. Data generated during the semiconductor manufacturing process from the process equipment is called fault detection and classification (FDC) trace data, and this data has time-series characteristics of different patterns depending on the sensor type or the recipe. Therefore, it is necessary to develop a classification algorithm appropriate to the signal pattern for process monitoring. In this paper, we develop segmented dynamic time warping technique which is specialized for process signal classification. Generally, it is known that dynamic time warping (DTW) has superior classification performance for time series data. However, there is a limit to classification that reflects the characteristics of semiconductor process signals. Therefore, we developed a classification algorithm for process signal data through segmented DTW using maximum overlap discrete wavelet transform (MODWT) and random sample consensus (RANSAC), and validated it.

Original languageEnglish
Title of host publicationProceedings - 22nd IEEE International Conference on Computational Science and Engineering and 17th IEEE International Conference on Embedded and Ubiquitous Computing, CSE/EUC 2019
EditorsMeikang Qiu
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages263-265
Number of pages3
ISBN (Electronic)9781728116631
DOIs
Publication statusPublished - 2019 Aug
Event22nd IEEE International Conference on Computational Science and Engineering and 17th IEEE International Conference on Embedded and Ubiquitous Computing, CSE/EUC 2019 - New York, United States
Duration: 2019 Aug 12019 Aug 3

Publication series

NameProceedings - 22nd IEEE International Conference on Computational Science and Engineering and 17th IEEE International Conference on Embedded and Ubiquitous Computing, CSE/EUC 2019

Conference

Conference22nd IEEE International Conference on Computational Science and Engineering and 17th IEEE International Conference on Embedded and Ubiquitous Computing, CSE/EUC 2019
CountryUnited States
CityNew York
Period19/8/119/8/3

Keywords

  • -Fault-detection-and-classification-(FDC)
  • -Maximum-overlap-discrete-wavelet-transform-(MODWT)
  • -Random-sample-consensus-(RANSAC)
  • -Segmented-dynamic-time-warping-(SDTW)
  • -Semiconductor-manufacturing-process
  • Dynamic-time-warping-(DTW)

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Computer Science Applications
  • Computer Vision and Pattern Recognition
  • Hardware and Architecture
  • Human-Computer Interaction
  • Artificial Intelligence

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