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dc.contributor.author周佳瑜zh_TW
dc.contributor.author盧鴻興zh_TW
dc.contributor.authorChou, Chia-Yuen_US
dc.contributor.authorLu, Horng-Shingen_US
dc.date.accessioned2018-01-24T07:41:01Z-
dc.date.available2018-01-24T07:41:01Z-
dc.date.issued2017en_US
dc.identifier.urihttp://etd.lib.nctu.edu.tw/cdrfb3/record/nctu/#GT070452626en_US
dc.identifier.urihttp://hdl.handle.net/11536/141459-
dc.description.abstract近年來半導體工業精密製程蓬勃發展,然而龐大的製程需要精密的控管才能有效提升良率並節省成本。然而晶圓片在化學研磨製程中的良率與研磨率息息相關,因此對於化學機械研磨製程(CMP) 中研磨率的預測為相當重要的課題。在本篇論文中,我們提出利用深度神經網路學習的方法結合隨機森林來進行預測,它比單一模型預測更穩定,更能準確地預測晶圓片的研磨率。zh_TW
dc.description.abstractIn recent years, the semiconductor industry is very flourishing. However, a cumbersome process requires sophisticated control to achieve the high yield rate and keep the cost down actually. And the yield of the wafer is related to the removal rate in the Chemical Mechanical Planarization process. Therefore, the prediction of the CMP removal rate is the important issues. In this paper, we combine the deep neural network learning and random forest to predict the removal rate, and it is more stable and gets less loss than using the only one method.en_US
dc.language.isoen_USen_US
dc.subject化學機械研磨機台zh_TW
dc.subject深度學習zh_TW
dc.subject隨機森林zh_TW
dc.subjectChemical-Mechanical Polishingen_US
dc.subjectDeep Neural Networken_US
dc.subjectRandom Foresten_US
dc.title結合深度神經網路和隨機森林對化學研磨機台的研磨率之預測zh_TW
dc.titlePrediction of the Chemical Mechanical Polishing Removal Rate by Using a Combination of Deep Neural Network and Random Foresten_US
dc.typeThesisen_US
dc.contributor.department統計學研究所zh_TW
Appears in Collections:Thesis