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An Atmospheric Pressure Plasma Setup to Investigate the Reactive Species Formation
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Real-time plasma process condition sensing and abnormal process detection.

Ryan Yang1, Rongshun Chen

  • 1Department of Power Mechanical Engineering, National Tsing Hua University, Hsinchu 30013, Taiwan. ryyanga@yahoo.com.tw

Sensors (Basel, Switzerland)
|January 6, 2012
PubMed
Summary
This summary is machine-generated.

This study introduces a real-time fault detection method for semiconductor wafer fabrication using optical emission spectroscopy (OES). The sigma matching technique enhances process control and wafer yield by identifying plasma etching abnormalities.

Keywords:
optic emission spectrumprocess/equipment fault detectionspectrum

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Area of Science:

  • Semiconductor Manufacturing
  • Process Control
  • Metrology

Background:

  • Plasma processing is crucial for semiconductor wafer fabrication but suffers from poor real-time control, leading to waste and reduced yield.
  • Accurate, real-time detection of process faults in plasma reactors is essential for maximizing product wafer yield.

Purpose of the Study:

  • To develop a real-time fault detection method for plasma etching processes.
  • To improve wafer yield and reduce waste in semiconductor manufacturing.

Main Methods:

  • Utilized optical emission spectroscopy (OES) for in-situ process monitoring.
  • Developed a sigma matching technique analyzing the time series of OES full spectrum intensity.
  • Created a response model for healthy plasma spectra and defined a matching rate to compare wafer responses against the healthy model.

Main Results:

  • The proposed sigma matching method successfully detected process faults in real-time.
  • The technique demonstrated effectiveness even in complex plasma etching tools.
  • The method provides a timely and accurate indicator for process abnormalities.

Conclusions:

  • The sigma matching method offers a viable solution for real-time fault detection in plasma-based semiconductor manufacturing.
  • This approach can significantly improve process performance and product wafer yield.
  • OES data analysis challenges are addressed by this novel technique.