Modeling the manufacturing performance of semiconductor equipment with deep and recurrent neural networks = 심층 및 회귀 인공신경망을 이용한 반도체 장비의 제조 성능 모델링

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Clustered photolithography tool often has a bottleneck due to its complex internal structure and is by far one of the most expensive equipment tools. Therefore, it is very important to accurately and efficiently estimate the cycle time, lot residency time and throughput time, which are directly related to the production yield. Physics-based models, revealed by previous studies, are inaccurate, computationally complex, or accurate, but have low fidelities under certain settings. Therefore, deep and recurrent neural networks are used to make an accurate and robust model regardless of the input condition, e.g., when lot sizes or buffer sizes change in this paper. We additionally compare the accuracy of recurrent neural network when test input gets farther from train input condition.
Advisors
Park, Jinkyooresearcher박진규researcher
Description
한국과학기술원 :산업및시스템공학과,
Publisher
한국과학기술원
Issue Date
2020
Identifier
325007
Language
eng
Description

학위논문(석사) - 한국과학기술원 : 산업및시스템공학과, 2020.2,[ii, 46 p. :]

Keywords

semiconductor equipment modeling▼amanufacturing performance▼amachine learning▼adeep neural network▼arecurrent neural network▼aDirichlet distribution; 반도체 장비 모델링▼a제조 성능▼a기계 학습▼a심층 인공 신경망▼a회귀 인공 신경망▼a디리클레 분포

URI
http://hdl.handle.net/10203/283924
Link
http://library.kaist.ac.kr/search/detail/view.do?bibCtrlNo=910096&flag=dissertation
Appears in Collection
IE-Theses_Master(석사논문)
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