딥러닝을 활용한 반도체 제조 물류 시스템 통행량 예측모델 설계

• 영문명: A Deep Learning-Based Model for Predicting Traffic Congestion in Semiconductor Fabrication
• 발행기관: 강원대학교 산업기술연구소
• 저자명: 김종명(Jong Myeong Kim) 김옥현(Ock Hyeon Kim) 홍성빈(Sung Bin Hong) 임대은(Dae-Eun Lim)
• 간행물 정보: 『산업기술연구』vol.39, 27~31쪽, 전체 5쪽
• 주제분류: 공학 > 공학일반
• 파일형태: PDF
• 발행일자: 2019.10.30

영문 초록

Semiconductor logistics systems are facing difficulties in increasing production as production processes become more complicated due to the upgrading of fine processes. Therefore, the purpose of the research is to design predictive models that can predict traffic during the pre-planning stage, identify the risk zones that occur during the production process, and prevent them in advance. As a solution, we build FABs using automode simulation to collect data. Then, the traffic prediction model of the areas of interest is constructed using deep learning techniques (keras - multistory conceptron structure). The design of the predictive model gave an estimate of the traffic in the area of interest with an accuracy of about 87%. The expected effect can be used as an indicator for making decisions by proactively identifying congestion risk areas during the Fab Design or Factory Expansion Planning stage, as the maximum traffic per section is predicted.

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1. 서 론

2. 연구 배경

3. 연구 절차

4. 결 론

키워드

Semiconductor prediction model AutoMod deep learning