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앙상블 학습을 이용한 DRAM 모듈 출하 품질보증 검사 불량 예측Fail Prediction of DRAM Module Outgoing Quality Assurance Inspection using Ensemble Learning Algorithm
- Other Titles
- Fail Prediction of DRAM Module Outgoing Quality Assurance Inspection using Ensemble Learning Algorithm
- Issue Date
- 2012
- Publisher
- 대한산업공학회
- Keywords
- decision tree(C4.5); ensemble learning; semiconductor manufacturing; DRAM module
- Citation
- 산업공학(IE interfaces), v.25, no.2, pp.178 - 186
- Indexed
- KCI
- Journal Title
- 산업공학(IE interfaces)
- Volume
- 25
- Number
- 2
- Start Page
- 178
- End Page
- 186
- ISSN
- 1225-0996
- Abstract
- 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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Related Researcher
- Baek, Jun Geol
- College of Engineering (School of Industrial and Management Engineering)