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Classification of semiconductor defects using a small number of training data and qualitative knowledge
Author(s) -
Shimomura Shohei,
Igarashi Hajime,
Hiroi Takashi,
Hosoya Naoki,
Nakagawa Yasuo
Publication year - 2008
Publication title -
electronics and communications in japan
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.131
H-Index - 13
eISSN - 1942-9541
pISSN - 1942-9533
DOI - 10.1002/ecj.10183
Subject(s) - cluster analysis , wafer , a priori and a posteriori , semiconductor , semiconductor device fabrication , computer science , cluster (spacecraft) , artificial intelligence , data mining , pattern recognition (psychology) , reliability engineering , engineering , electronic engineering , materials science , electrical engineering , philosophy , epistemology , programming language
In semiconductor wafer manufacturing processes, defect candidates are usually extracted by an inspection system. The defect candidates are composed of true defects such as open circuits, contaminants, and bridges, as well as nondefect patterns, called nuisances, which predominate over true defects. The goal of this study is to classify the defect candidates as the various true defects and nuisances by using a small number of training data obtained by SEM inspection. It is shown that the accuracy of clustering is considerably improved by use of qualitative knowledge about the defects, given a priori by inspectors, in the clustering processes. © 2009 Wiley Periodicals, Inc. Electron Comm Jpn, 91(11): 46–54, 2008; Published online in Wiley InterScience ( www.interscience.wiley.com ). DOI 10.1002/ecj.10183