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

A neural-network approach to recognize defect spatial pattern in semiconductor fabrication

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

2 Author(s)
Fei-Long Chen ; Dept. of Ind. Eng., Nat. Tsing Hua Univ., Hsinchu, Taiwan ; Shu-Fan Liu

Yield enhancement in semiconductor fabrication is important. Even though IC yield loss may be attributed to many problems, the existence of defects on the wafer is one of the main causes. When the defects on the wafer form spatial patterns, it is usually a clue for the identification of equipment problems or process variations. This research intends to develop an intelligent system, which will recognize defect spatial patterns to aid in the diagnosis of failure causes. The neural-network architecture named adaptive resonance theory network 1 (ART1) was adopted for this purpose. Actual data obtained from a semiconductor manufacturing company in Taiwan were used in experiments with the proposed system. Comparison between ART1 and another unsupervised neural network, self-organizing map (SOM), was also conducted. The results show that ART1 architecture can recognize the similar defect spatial patterns more easily and correctly

Published in:

Semiconductor Manufacturing, IEEE Transactions on  (Volume:13 ,  Issue: 3 )

Date of Publication:

Aug 2000

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.