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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Vydáno v:IEEE transactions on semiconductor manufacturing Ročník 34; číslo 3; s. 408 - 419
Hlavní autoři: Kim, Dong Hwan, Hong, Sang Jeen
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York IEEE 01.08.2021
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
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
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Shrnutí: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 find the root cause of the anomaly in the process parameters. Fault detection and control is also demonstrated to predict the shift of the process parameter along the amount of process gas flow rate injected into the chamber, considering a fault. Especially, plasma information (PI), such as electron temperature and electron density, was derived from OES data into equation-based corona model. These were utilized to evaluate which process parameter is the most significantly affecting on the performance of the established model through Shapley value in fault detection and control. By the combination of isolation forest algorithm for finding the plasma abnormalities in real time and Adaboost algorithm for classifying root causes of faults, the suggested algorithm could accurately detect the root cause. DeepSHAP algorithm helped not only the prediction of gas flow rate, but PI was identified as critical parameter, interpreting the model through Shapley value. We propose a new multi-function integrated algorithm by the ensemble algorithms.
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content type line 14
ISSN:0894-6507
1558-2345
DOI:10.1109/TSM.2021.3079211