Predicting the yield rate of DRAM modules by support vector regression

Shih Wei Lin*, Shih Chieh Chen

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

3 Scopus citations

Abstract

Dynamic random access memory (DRAM) module is one of the principal components of electronic equipment, which impacts the quality, performance and price of the final products singinifcantly. Typically, DRAM module is composed of DRAM ICs (integrated circuit). DRAM ICs with higher quality can be used to produce DRAM modules with higher quality. Generlly speaking, high quality DRAM ICs are more costly. Due the the cost down and material saving reason, some DRAM module manufacturers purchase batches DRAM ICs containing defective units, and then have the batch tested in order to select DRAM ICs for production of DRAM modules. Thus, this kind of DRAM module is suitable only for products not intended for work in harsh environments being sold in lower price markets. Due to the lower quality of the DRAM ICs, the actual quality of the DRAM module is not easily predicted. Predicting the yield rate of the DRAM module is thus an important issue for DRAM module manufacturers who purchase DRAM ICs with lower quality at lower prices. This study used support vector regression (SVR) to predict the yield rate of the DRAM modules produced using defective DRAM ICs. SVR is a very capable method and has been successfully applied across many fields. However, the parameters and input features differ depending on the application. Thus, a scatter search (SS) approach is proposed to obtain the suitable parameters for the SVR and to select the beneficial subset of features which result in a better prediction of the DRAM module yield rate. The experimental results showed that the performance is better than that of traditional stepwise regression analysis.

Original languageEnglish
Title of host publicationGlobal Perspective for Competitive Enterprise, Economy and Ecology - Proceedings of the 16th ISPE International Conference on Concurrent Engineering
PublisherSpringer-Verlag London Ltd
Pages747-755
Number of pages9
ISBN (Print)9781848827615
DOIs
StatePublished - 2009
Externally publishedYes
Event16th ISPE International Conference on Concurrent Engineering, CE 2009 - Taipei, Taiwan
Duration: 20 07 200924 07 2009

Publication series

NameGlobal Perspective for Competitive Enterprise, Economy and Ecology - Proceedings of the 16th ISPE International Conference on Concurrent Engineering

Conference

Conference16th ISPE International Conference on Concurrent Engineering, CE 2009
Country/TerritoryTaiwan
CityTaipei
Period20/07/0924/07/09

Keywords

  • Dram yield rate
  • Feature selection
  • Parameter determination
  • Scatter search
  • Support vector regression

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