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Fault Detection of Reactive Ion Etching Using Time Series Neural Networks

  • Kyung-Han Ryu
  • Song-Jae Lee
  • Jaehyun Park
  • Dong-Chul Park
  • Sang J. Hong
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3973)

Abstract

Maximizing the productivity in semiconductor manufacturing, early detection of process and/or equipment abnormality. Since most of the key processes in semiconductor production are performed under extremely high vacuum condition, no other action can be taken unless the undergoing process is terminated. In this paper, time series based neural networks have been employed to assist the decision for determining potential process fault in real-time. Principal component analysis (PCA) for the dimensionality reduction of the data is first performed to handle smoothly in real-time environment. According to the PCA, 11 system parameters were selected, and each of them were then classified using modeled and tested in time series. Successful detection on different types of process shift (or fault) was achieved with 0% false alarm.

Keywords

False Alarm Fault Detection Control Limit Tool Data Advanced Process Control 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Kyung-Han Ryu
    • 1
  • Song-Jae Lee
    • 2
  • Jaehyun Park
    • 1
  • Dong-Chul Park
    • 2
  • Sang J. Hong
    • 3
  1. 1.Department of Electronic EngineeringMyongji UniversityYonginKorea
  2. 2.Department of Information EngineeringMyongji UniversityYonginKorea
  3. 3.Department of Electronic Engineering & Nano-Bio Research CenterMyongji UniversityYonginKorea

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