Developing a product quality fault detection scheme

Yi Ting Huang, Fan Tien Cheng, Min Hsiung Hung

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

9 Citations (Scopus)

Abstract

In current semiconductor and TFT-LCD factories, periodic sampling is commonly adopted to monitor the stability of manufacturing processes and the quality of products (or workpieces). As for those non-sampled workpieces, their quality is usually monitored by such as a fault-detection-and-classification (FDC) server. However, this method may fail to detect defected products. For example, a workpiece with all the individual manufacturing process parameters being in-spec may still result in out-of-spec product quality. Under this circumstance, unless this certain defected workpiece is selected for sampling by chance, it cannot be detected by simply monitoring the manufacturing process parameters collected from the production equipment. To solve the abovementioned problem, this research proposes a product quality fault detection scheme (FDS), which utilizes the classification and regression tree to implement a model for identifying the relationship between process parameters and out-of-spec products. Through this model, each set of normal manufacturing process parameters can be real-time and on-line examined to detect failure or defected products.

Original languageEnglish
Title of host publication2009 IEEE International Conference on Robotics and Automation, ICRA '09
Pages927-932
Number of pages6
DOIs
Publication statusPublished - 2009 Nov 2
Event2009 IEEE International Conference on Robotics and Automation, ICRA '09 - Kobe, Japan
Duration: 2009 May 122009 May 17

Publication series

NameProceedings - IEEE International Conference on Robotics and Automation
ISSN (Print)1050-4729

Other

Other2009 IEEE International Conference on Robotics and Automation, ICRA '09
CountryJapan
CityKobe
Period09-05-1209-05-17

All Science Journal Classification (ASJC) codes

  • Software
  • Control and Systems Engineering
  • Artificial Intelligence
  • Electrical and Electronic Engineering

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  • Cite this

    Huang, Y. T., Cheng, F. T., & Hung, M. H. (2009). Developing a product quality fault detection scheme. In 2009 IEEE International Conference on Robotics and Automation, ICRA '09 (pp. 927-932). [5152474] (Proceedings - IEEE International Conference on Robotics and Automation). https://doi.org/10.1109/ROBOT.2009.5152474