A virtual metrology scheme for predicting CVD thickness in semiconductor manufacturing

Tung Ho Lin, Ming Hsiung Hung, Rung Chuan Lin, Fan Tien Cheng

研究成果: Conference contribution

26 引文 斯高帕斯(Scopus)

摘要

For maintaining high stability and production yield of production equipment in a semiconductor fab, on-line quality monitoring of wafers is required. In current practice, physical metrology is performed only on monitor wafers that are periodically added in production equipment for processing with production wafers. Hence, equipment performance drift happening in-between the scheduled monitoring cannot be detected promptly. This may cause defects of production wafers and the production cost. In this paper, a novel virtual metrology scheme (VMS) that is based on a radial basis function neural network (RBFN) is proposed for overcoming this problem. The VMS is capable of predicting quality of production wafers using real-time sensor data from production equipment. Consequently, equipment performance abnormality or drift can be detected timely. Finally, the effectiveness of the proposed VMS is validated by tests on chemical vapor deposition (CVD) processes in practical semiconductor manufacturing. It is therefore proved that RBFN can be effectively used to construct prediction models for CVD processes.

原文English
主出版物標題Proceedings 2006 IEEE International Conference on Robotics and Automation, ICRA 2006
頁面1054-1059
頁數6
DOIs
出版狀態Published - 2006 十二月 27
事件2006 IEEE International Conference on Robotics and Automation, ICRA 2006 - Orlando, FL, United States
持續時間: 2006 五月 152006 五月 19

出版系列

名字Proceedings - IEEE International Conference on Robotics and Automation
2006
ISSN(列印)1050-4729

Other

Other2006 IEEE International Conference on Robotics and Automation, ICRA 2006
國家/地區United States
城市Orlando, FL
期間06-05-1506-05-19

All Science Journal Classification (ASJC) codes

  • 軟體
  • 控制與系統工程
  • 人工智慧
  • 電氣與電子工程

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