1. Proposing Data Mining for Plasma Diagnosis
SFR Workshop
November 8, 2000
Dong Wu Zhao, Costas Spanos
Berkeley, CA
2001 GOAL: Install automated OES and Z-scan sensor on LAM 9400 and build large statistical database of process fingerprinting data by 9/30/2001.
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2. Motivation Plasma signals are difficult to characterize.
Subject to preventive maintenance, machine aging, chamber memory effect, etc.
Need to describe signals both qualitatively and qualitatively by syntactic analysis.
Need to find out meaningful features effectively from a large amount of data.
Based exploring results, specify syntactic rules to characterize signals formally.
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3. Available Data from LAM 9400
machine settings
Optical Emission Spectroscopy (200 – 1100 nm).
Z-scan signals of current voltage, impedance, five harmonics of Mhz.
Regular machine signals & settings, power, pressure, temperature, gas flow rate.
Monitor the chamber all the time to build a large database.
machine signals Z
OES
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4. Some Features for Exploration
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5. OES Peak Exploration
To figure out the peaks that change due to certain faulty condition.
To help establish threshold criteria.
baseline
faulty
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6. Exploring the Database Population
For this example, RF top power values of 280~310 W should be used as operating points for exploration purpose.
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7. Exploring Trends E.g., fix all other machine settings, vary pressure.
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8. Syntactic Analysis
Assign codes to OES peaks, Z-scan harmonics, and machine signals:
Large increase: 2
Moderate increase: 1
Unchanged: 0
Moderate decrease: -1
Large decrease: -2
Selectively monitor numerical values of certain signals. For example, if pressure consistently drifts away from the setting point, we should fire an alarm.
Put the code in a stream:
{OES peak codes}{Z-scan harmonic codes}{machine signal codes}, e.g.,
{ …. 0 –1}{0 0 –2 –1 … 0 1 0}{ … 0 –1 0}
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9. 2002 and 2003 Goals
Deploy automated fault detection system using high sampling rate RF fingerprinting. Study automated generation of syntactic analysis rules for RF fingerprinting, by 9/30/2002.
Study systems of real-time instability detection and plasma stabilization control; perform field studies of automated OES classification for fault diagnosis, by 9/30/2003.
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