DocumentCode :
1425027
Title :
Functional Kernel-Based Modeling of Wavelet Compressed Optical Emission Spectral Data: Prediction of Plasma Etch Process
Author :
Ko, Young-Don ; Jeong, Young-Seon ; Jeong, Myong-Kee ; Garcia-Diaz, Alberto ; Kim, Byungwhan
Author_Institution :
Dept. of Ind. & Inf. Eng., Univ. of Tennessee, Knoxville, TN, USA
Volume :
10
Issue :
3
fYear :
2010
fDate :
3/1/2010 12:00:00 AM
Firstpage :
746
Lastpage :
754
Abstract :
This study reports the use of a kernel-based process model, consisting of kernel partial least squares regression and kernel ridge regression, to model etch rate and uniformity in a plasma etch process. In order to characterize the plasma etch process, a 24 - 1 fractional factorial design was implemented on the process parameters: CHF3 flow rate, CF4 flow rate, RF power, and pressure. In this modeling, both functional data and in situ optical emission spectroscopy (OES) data associated with the etch response were used to formulate the model. In an effort to effectively deal with the complexity of the data, wavelet transformation with vertical-energy-thresholding (VET) shrinkage procedures were used to reduce the dimensions of the functional data. In addition, a Bayesian information criterion (BIC) was used to select the best subset to improve the model predictions. The proposed kernel-based approaches were evaluated by comparing them to conventional neural networks (NNs)-based modeling and linear-based regression techniques. Comparisons revealed that the proposed approach exhibits an improved prediction over NNs and linear-based models. Implicated in the study is a detection of process fault patterns by combining the kernel-based modeling, wavelet transformation with VET, and BIC.
Keywords :
belief networks; electronic engineering computing; least squares approximations; photoluminescence; regression analysis; sputter etching; wavelet transforms; Bayesian information criterion; CF4 flow rate; CHF3 flow rate; RF power; fractional factorial design; functional data; kernel partial least squares regression; kernel ridge regression; kernel-based process model; optical emission spectroscopy data; plasma etch process; pressure; vertical-energy-thresholding shrinkage procedures; wavelet transformation; Bayesian methods; Etching; Kernel; Least squares methods; Plasma applications; Plasma waves; Predictive models; Radio frequency; Spectroscopy; Stimulated emission; Bayesian information criterion (BIC); kernel; multiple linear regression (MLR); neural network (NN); optical emission spectroscopy (OES); partial least squares (PLSs); plasma process modeling; principal component analysis (PCA); ridge regression (RR); statistical modeling; wavelet; wavelet thresholding;
fLanguage :
English
Journal_Title :
Sensors Journal, IEEE
Publisher :
ieee
ISSN :
1530-437X
Type :
jour
DOI :
10.1109/JSEN.2009.2038569
Filename :
5419251
Link To Document :
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