앙상블 학습을 이용한 DRAM 모듈 출하 품질보증 검사 불량 예측
Fail Prediction of DRAM Module Outgoing Quality Assurance Inspection using Ensemble Learning Algorithm
김민석(SK하이닉스); 백준걸(고려대학교)
25권 2호, 178~186쪽
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.
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.
- 발행기관:
- 대한산업공학회
- DOI:
- http://dx.doi.org/
- 분류:
- 산업공학