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Data mining for improving a cleaning process in the semiconductor industry
Dan Braha,
Armin Shmilovici
Department of Software and Information Systems Engineering
Research output
:
Contribution to journal
›
Article
›
peer-review
92
Scopus citations
Overview
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Dive into the research topics of 'Data mining for improving a cleaning process in the semiconductor industry'. Together they form a unique fingerprint.
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Keyphrases
Cleaning Methods
100%
Semiconductor Industry
100%
Microcontaminants
100%
Data Mining Methodology
100%
Semiconductors
50%
Highly Effective
50%
Neural Network
50%
Negative Impact
50%
Physical Parameters
50%
Laser Beams
50%
Complex Interactions
50%
Classification Basis
50%
Data Mining Techniques
50%
Chemical Parameters
50%
Device Geometry
50%
Decision Tree Induction
50%
Online Monitoring
50%
Individual Classifiers
50%
Clean Technology
50%
Wafer Yield
50%
Dry Cleaning
50%
Classifier Architectures
50%
Data Mining Applications
50%
Contamination Problem
50%
Engineering
Physical Parameter
100%
Negative Impact
100%
Laser Beam
100%
Chemical Parameter
100%
Dry Cleaning
100%
Computer Science
Data Mining
100%
Neural Network
25%
Data Mining Technique
25%
Decision Tree Induction
25%
Negative Impact
25%
Individual Classifier
25%
Earth and Planetary Sciences
Data Mining
100%
Semiconductor Industry
100%
Dry Cleaning
20%
Chemical Engineering
Neural Network
100%