Prediction of package chip quality using fail bit count data of the probe test

Jin Soo Park, Seoung Bum Kim

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

The quality prediction of the semiconductor industry has been widely recognized as important and critical for quality improvement and productivity enhancement. The main objective of this paper is to establish a prediction methodology of semiconductor chip quality. Although various research has been conducted for predicting a yield, these studies predict a yield by lot-level and do not consider characteristics of the data. We demonstrate the effectiveness of the proposed procedure using a real data from a semiconductor manufacturing.

Original languageEnglish
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
PublisherSpringer Verlag
Pages655-664
Number of pages10
Volume9101
ISBN (Print)9783319190655
DOIs
Publication statusPublished - 2015
Event28th International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2015 - Seoul, Korea, Republic of
Duration: 2015 Jun 102015 Jun 12

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume9101
ISSN (Print)03029743
ISSN (Electronic)16113349

Other

Other28th International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2015
CountryKorea, Republic of
CitySeoul
Period15/6/1015/6/12

Keywords

  • Nonparametric variable Selection
  • Probe test
  • Quality prediction
  • Smote

ASJC Scopus subject areas

  • Computer Science(all)
  • Theoretical Computer Science

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  • Cite this

    Park, J. S., & Kim, S. B. (2015). Prediction of package chip quality using fail bit count data of the probe test. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 9101, pp. 655-664). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9101). Springer Verlag. https://doi.org/10.1007/978-3-319-19066-2_63