Use of Plasma Information in Machine-Learning-Based Fault Detection and Classification for Advanced Equipment Control

For advanced equipment control, two schemata of real-time fault detection were performed using machine learning algorithms in silicon etching in SF 6 /O 2 /Ar plasma. Fault detection and classification is investigated with the plasma state information with optical emission spectroscopy (OES) data to...

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Veröffentlicht in:	IEEE transactions on semiconductor manufacturing Jg. 34; H. 3; S. 408 - 419
Hauptverfasser:	Kim, Dong Hwan, Hong, Sang Jeen
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York IEEE 01.08.2021 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Abnormalities advanced equipment control Algorithms Argon plasma Classification Classification algorithms Control equipment data mining Electron density Electron energy Emission analysis Etching Fault detection FDC Flow velocity Gas flow Machine learning Machine learning algorithms Mathematical models Optical emission spectroscopy Parameter identification Plasma plasma information (PI) Plasmas Prediction algorithms Process control Process parameters Real time Real-time systems Root cause analysis
ISSN:	0894-6507, 1558-2345
Online-Zugang:	Volltext
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