Data mining approaches for packaging yield prediction in the post-fabrication process

Seung Hwan Park, Cheong Sool Park, Jun Seok Kim, Sung Shick Kim, Jun-Geol Baek, Daewoong An

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

5 Citations (Scopus)

Abstract

In the post-fabrication process for semiconductors, it is critical to predict the yield. This process consists of a series of electrical and physical tests following semiconductor fabrication, tests that generate a significant volume of parametric data. While past research has investigated yield prediction using parametric test data, most studies have difficulty correctly predicting the low and high yield because of the wide range of variables and the large data set. Also, in the case of the packaging yield, prediction is inaccurate as this yield does not directly correlate with the parametric test data. Therefore, this study proposes a framework in which the packaging yield is classified using the parametric test data of the previous step of the packaging test. This study involves three stages. In the first, data preprocessing is conducted due to the large data set. To learn a data mining model using much more data, parametric test data generated in the die level need to be changed into the wafer level. In the second stage, a random forest algorithm is used to select significant variables affecting the packaging yield. Finally, the third stage uses a nonlinear support vector machine (SVM) to classify the low and high yield. Through the three stages, this study demonstrates that this proposed algorithm has a superior performance.

Original languageEnglish
Title of host publicationProceedings - 2013 IEEE International Congress on Big Data, BigData 2013
Pages363-368
Number of pages6
DOIs
Publication statusPublished - 2013 Oct 28
Event2013 IEEE International Congress on Big Data, BigData 2013 - Santa Clara, CA, United States
Duration: 2013 Jun 272013 Jul 2

Other

Other2013 IEEE International Congress on Big Data, BigData 2013
CountryUnited States
CitySanta Clara, CA
Period13/6/2713/7/2

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Keywords

  • Ensemble Support Vector Machine
  • Packaging Yield Classification
  • Random Forests
  • Semiconductor Manufacturing Process

ASJC Scopus subject areas

  • Computer Science Applications

Cite this

Park, S. H., Park, C. S., Kim, J. S., Kim, S. S., Baek, J-G., & An, D. (2013). Data mining approaches for packaging yield prediction in the post-fabrication process. In Proceedings - 2013 IEEE International Congress on Big Data, BigData 2013 (pp. 363-368). [6597159] https://doi.org/10.1109/BigData.Congress.2013.55