By Topic

GA Guided Cluster Based Fuzzy Decision Tree for Reactive Ion Etching Modeling: A Data Mining Approach

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
$33 $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)
Sanjay Kumar Shukla ; Evalueserve, Gurgaon, India ; Manoj Kumar Tiwari

There are various data mining techniques that are frequently used for the mining of vital patterns embedded within bulk data. These techniques include neural network, regression analysis, rough set theory, Bayesian network, decision trees, and so on. In this research, a novel data mining technique, genetically guided cluster based fuzzy decision tree (GCFDT), is introduced for the mining task. In order to test the efficacy of GCFDT, it is employed for building the predictive process models of reactive ion etching (RIE) with the aid of optical emission spectroscopy (OES) signals. This model endeavors to predict the wafer surface conditions for the new incoming set of process parameters. OES is an efficient tool for monitoring plasma emission intensity. In contrast with the C-fuzzy decision tree where granules are devolved through fuzzy clustering here, granulation is practised through genetically guided fuzzy clustering. The growth of the tree is governed by expanding the node having highest diversity. The results obtained by employing CGFDT in RIE process modeling reveal that it dominates both the traditional C-fuzzy decision trees and C4.5 decision trees in terms of both the accuracy and compactness.

Published in:

IEEE Transactions on Semiconductor Manufacturing  (Volume:25 ,  Issue: 1 )