산업공학 (IE interfaces)

  • 제25권2호
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  • Pages.178-186
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  • 2012
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  • 1225-0996(pISSN)
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  • 2234-6465(eISSN)
대한산업공학회 (Korean Institute of Industrial Engineers)
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앙상블 학습을 이용한 DRAM 모듈 출하 품질보증 검사 불량 예측

Fail Prediction of DRAM Module Outgoing Quality Assurance Inspection using Ensemble Learning Algorithm

  • 투고 : 2011.12.16
  • 심사 : 2012.03.22
  • 발행 : 2012.06.01
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초록

The DRAM module is an important part of servers, workstations and personal computer. Its malfunction causes a lot of damage on customer system. Therefore, customers demand the highest quality products. The company applies DRAM module Outgoing Quality Assurance Inspection(OQA) to secures the highest quality. It is the key process to decides shipment of products through sample inspection method with customer oriented tests. High fraction of defectives entering to OQA causes inevitable high quality cost. This article proposes the application of ensemble learning to classify the lot status to minimize the ratio of wrong decision in OQA, observing a potential in reducing the wrong decision.

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참고문헌

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피인용 문헌

  1. Under Sampling for Imbalanced Data using Minor Class based SVM (MCSVM) in Semiconductor Process vol.40, pp.4, 2014, https://doi.org/10.7232/JKIIE.2014.40.4.404