Adaptation of the fuzzy k-nearest neighbor classifier for manufacturing automation

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The use of supervised pattern recognition technologies for automation in the manufacturing environment require the development of systems that are easy to train and use. In general, these systems attempt to emulate an inspection or measurement function typically performed by a manufacturing engineer or technician. This paper describes a self-optimizing classification system for automatic decision making in the manufacturing environment. This classification system identifies and labels unique distributions of product defects denoted as signatures. The technique relies on encapsulating human experience through a teaching method to emulate the human response to various manufacturing situations. This has been successfully accomplished through ... continued below

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12 p.

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Tobin, K. W.; Gleason, S. S. & Karnowski, T. P. January 1998.

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Description

The use of supervised pattern recognition technologies for automation in the manufacturing environment require the development of systems that are easy to train and use. In general, these systems attempt to emulate an inspection or measurement function typically performed by a manufacturing engineer or technician. This paper describes a self-optimizing classification system for automatic decision making in the manufacturing environment. This classification system identifies and labels unique distributions of product defects denoted as signatures. The technique relies on encapsulating human experience through a teaching method to emulate the human response to various manufacturing situations. This has been successfully accomplished through the adaptation and extension of a feature-based, fuzzy k-nearest neighbor (k-NN) classifier that has been implemented in a pair-wise fashion. The classifier works with pair-wise combinations of the user-defined classes so that a significant reduction in feature space and problem complexity can be achieved. This k-NN implementation makes extensive use of hold-one-out results and fuzzy ambiguity information to optimize its performance. A semiconductor manufacturing case study will be presented. The technique uses data collected from in-line optical inspection tools to interpret and rapidly identify characteristic signatures that are uniquely associated with the manufacturing process. The system then alerts engineers to probable yield-limiting conditions that require attention.

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12 p.

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OSTI as DE98003174

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  • BIOS `98: an international symposium on biomedical optics, San Jose, CA (United States), 24-30 Jan 1998

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  • Other: DE98003174
  • Report No.: ORNL/CP--96062
  • Report No.: CONF-980117--
  • Grant Number: AC05-96OR22464
  • Office of Scientific & Technical Information Report Number: 634137
  • Archival Resource Key: ark:/67531/metadc689676

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  • January 1998

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  • Aug. 14, 2015, 8:43 a.m.

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  • Jan. 15, 2016, 12:34 p.m.

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Tobin, K. W.; Gleason, S. S. & Karnowski, T. P. Adaptation of the fuzzy k-nearest neighbor classifier for manufacturing automation, article, January 1998; Tennessee. (digital.library.unt.edu/ark:/67531/metadc689676/: accessed October 20, 2018), University of North Texas Libraries, Digital Library, digital.library.unt.edu; crediting UNT Libraries Government Documents Department.