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dc.contributor.author洪志明en_US
dc.contributor.authorChih-Ming Hungen_US
dc.contributor.author李安謙en_US
dc.contributor.authorDr. An-Chen LEEen_US
dc.date.accessioned2014-12-12T02:20:57Z-
dc.date.available2014-12-12T02:20:57Z-
dc.date.issued2005en_US
dc.identifier.urihttp://140.113.39.130/cdrfb3/record/nctu/#GT009169524en_US
dc.identifier.urihttp://hdl.handle.net/11536/64468-
dc.description.abstract本論文主要目的為設計一套應用於微影製程之關鍵尺寸(Critical Dimension,CD)的先進製程控制方法。利用實驗設計法(Design of Experiment, DOE)找出步進機之輸入變數(製程配方,recipes)與輸出變數(晶圓良率變數:關鍵尺寸)之間的關係,建立批次控制之預測模式,然後利用Recursive係數調變方法,做模式係數的動態調變,以適應製程隨時間變化之變異及干擾,再將已調變完成之模式代入最小變異控制器(Minimum Variance Controller,MVC),使機台的輸入變數可以在改變量最小的限制下,即製程的變動最小的情形下,讓輸出變數快速地到達我們所設定的目標值,以提升製程之良率。zh_TW
dc.description.abstractThis thesis presents a new run-to-run (R2R) multiple-input-single-out controller for photolithography process. The controller, termed dynamical minimum variance controller, can act both as a dynamical model optimizer and as a controller for given models. In this thesis, the relationships and run-to-run control model among input recipes (Exposure dose and Focus) and output variables (Critical Dimension) are formed by using experimental design method. Then on-line (Recursive) model identification and recipes generation under input and output constraints are performed by using dynamical model optimizer and non-linear minimum variance controller, respectively. Improvements due to advanced control have been quantified in simulations and actual fab operations.en_US
dc.language.isozh_TWen_US
dc.subject先進製程控制zh_TW
dc.subject微影製程zh_TW
dc.subject最小變異控制器zh_TW
dc.subjectAdvanced Process Controlen_US
dc.subjectLithography Processen_US
dc.subjectMinimum-Variance Controlleren_US
dc.title應用動態模式最小變異控制器於先進微影製程控制zh_TW
dc.titleA Dynamical Model Tuning and Non-linear Minimum Variance Controller for Advanced Photolithography Process Controlen_US
dc.typeThesisen_US
dc.contributor.department工學院精密與自動化工程學程zh_TW
Appears in Collections:Thesis