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

Feedforward Neural Network Trained by BFGS Algorithm for Modeling Plasma Etching of Silicon Carbide

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

3 Author(s)
Jing-Hua Xia ; Sch. of Electr. & Electron. Eng., Nanyang Technol. Univ., Singapore, Singapore ; Rusli ; Kumta, A.S.

Electron cyclotron resonance (ECR) plasma etching of silicon carbide is numerically modeled by a feedforward neural network (FNN), which is trained by the Broyden, Fletcher, Goldfarb, and Shanno (BFGS) optimization algorithm and the conventional backpropagation (BP) algorithm. The training samples are obtained from our experimental results, which meet the requirement of Box-Wilson central-composite-designed experimental test design. By using the samples, the BFGS algorithm is compared with the conventional BP algorithm with different hidden neuron numbers, different number of iterations and various learning rates. It is shown that the BFGS algorithm requires less hidden neurons and less iteration to obtain the same training results, and it also provides much smaller cross-validation errors. Therefore, the FNN trained by the BFGS algorithm possesses much better approximation and generalization ability. The silicon carbide ECR process modeling results demonstrate that the FNN trained by the BFGS algorithm are fast, reliable, and accurate.

Published in:

Plasma Science, IEEE Transactions on  (Volume:38 ,  Issue: 2 )

Date of Publication:

Feb. 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.