Cart (Loading....) | Create Account
Close category search window
 

Functional Kernel-Based Modeling of Wavelet Compressed Optical Emission Spectral Data: Prediction of Plasma Etch Process

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

5 Author(s)
Young-Don Ko ; Dept. of Ind. & Inf. Eng., Univ. of Tennessee, Knoxville, TN, USA ; Young-Seon Jeong ; Myong-Kee Jeong ; Garcia-Diaz, A.
more authors

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.

Published in:

Sensors Journal, IEEE  (Volume:10 ,  Issue: 3 )

Date of Publication:

March 2010

Need Help?


IEEE Advancing Technology for Humanity About IEEE Xplore | Contact | Help | Terms of Use | Nondiscrimination Policy | Site Map | Privacy & Opting Out of Cookies

A not-for-profit organization, IEEE is the world's largest professional association for the advancement of technology.
© Copyright 2014 IEEE - All rights reserved. Use of this web site signifies your agreement to the terms and conditions.