Data science framework for variable selection, metrology prediction, and process control in TFT-LCD manufacturing

Chia Yen Lee, Tsung Lun Tsai

研究成果: Article同行評審

14 引文 斯高帕斯(Scopus)

摘要

TFT-LCD panel manufacturers rely on experimental design and engineering experience for process monitoring and quality control throughout the production line. To shorten production and reduce the cost of labor resources, this study proposes a three-phase data science framework embedded with several data mining and machine learning techniques, which can identify the variables affecting yield, predict the metrology result of photo spacer process, and suggest the process control in the color filter manufacturing process. An empirical study of Taiwan's leading TFT-LCD manufacturer is conducted to validate the proposed framework. The results indicate that the proposed framework effectively and quickly selects the important variables, predicts the metrology result with higher performance, and identifies the main effect and interaction effect of the selected variables for yield improvement.

原文English
頁(從 - 到)76-87
頁數12
期刊Robotics and Computer-Integrated Manufacturing
55
DOIs
出版狀態Published - 2019 二月

All Science Journal Classification (ASJC) codes

  • 控制與系統工程
  • 軟體
  • 數學(全部)
  • 電腦科學應用
  • 工業與製造工程

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