NDLI: Selection of Invariant objects with a data-mining approach

Content Provider	IEEE Xplore Digital Library
Author	Kusiak, A.
Copyright Year	1999
Abstract	An approach based on data-mining for identifying invariant objects in semiconductor applications is presented. An invariant object represents a set of parameter (feature) values of a process and the corresponding outcome, e.g., process quality. The key characteristic of an invariant object is that its outcome can be accurately predicted in the changing data environment. One of the most powerful applications of invariant objects involves the generation of robust settings for controllers in a multiparameter process. The prediction accuracy of such robust settings should be invariant in time, features, and data form. The notion of time-invariant objects refers to objects for which the prediction accuracy is not affected by time. Analogous to time-invariance, objects can be invariant in the features and the data form. The former implies that the prediction accuracy of a set of objects is not impacted by the set of features selected from the same data set. The outcomes of data-form invariant objects prevail despite the change in the data form (data transformation). The use of data transformation methods defined in this paper is twofold: first, to identify the invariance of objects and secondly, to enhance prediction accuracy. The concepts presented in this paper are illustrated with a numerical example and two semiconductor case studies.
Sponsorship	IEEE Components, Packaging, and Manufacturing Technology Society International Microelectronics Assembly and Packaging Society (IMAPS)
Starting Page	187
Ending Page	196
Page Count	10
File Size	834957
File Format	PDF
ISSN	1521334X
Volume Number	28
Issue Number	2
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2005-04-01
Publisher Place	U.S.A.
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Accuracy Process control Data mining Decision making Semiconductor device modeling Power generation Robust control Robustness Association rules Intelligent systems time invariance form invariance invariant objects knowledge discovery parameter invariance process control
Content Type	Text
Resource Type	Article
Subject	Industrial and Manufacturing Engineering Electrical and Electronic Engineering

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