因應越趨激烈的全球化競爭，半導體廠必須不斷地追求良率提升以穩固品質的基石以進而提升競爭優勢。隨著半導體技術世代的推進，製程的容忍度也越趨緊縮，相對也影響良率提升的困難度。除了離線的製程事故原因診斷之外，線上精密的製程控制亦為改善與提升良率的重要方式。先進設備控制與先進製程控制為半導體製造中用以進行良率提升的重要方法，其主要功能包含縮減品質特性產出變異的批次控制以及監控設備健康狀態的錯誤偵測與分類模式。其中，批次控制模式的設計除了須考量到製程模型的建構與控制機制的設計等問題面之外，決策者的偏好也會影響控制層級的設定與變數的蒐集範圍，同時與製程模型的建構與控制機制也會有交互的影響關係。另一方面，半導體廠實際生產環境存在的許多限制與不確定因素也會影響製程控制的成效，在建立模型的同時須對潛在的不確定因素進行架構與影響形式的客觀描述，並針對各種情況進行診斷與分析之後，方能決定適合的製程控制模式導入於線上使用。聚焦於既有研究上的缺口與實務需求，本研究發展半導體先進製程控制之品質工程研究架構來具體呈現此決策問題的架構過程，在考量人（參與者）、問題（執行程序）以及環境（考慮因素）等三個面向之下，透過兩階段、六個步驟對應的方法與工具逐步發展符合真實需求的製程控制模式解決方案以達成良率提升的根本目標。本研究分別以兩項實證研究檢驗研究架構之效度，包含微影製程的線寬控制以及蝕刻製程的線寬控制。此兩項實證研究所發展的模式截然不同，但最終目的都是要能夠降低線寬的變異與提升製程能力，並已實際導入個案公司上線使用，擴散使用效益。

As the global competition in semiconductor industry becomes fiercer, yield enhancement, the basis of quality, is the key factor to maintain competitive advantages for semiconductor companies. With the shrinking feature size in advanced semiconductor technology generations, the process window is tightened to meet high quality requirement, and thus increase the difficulty of yield enhancement. In addition to the diagnosis of low yield for trouble shooting, the online process control plays a critical role to enhance the yield in advanced technologies. Advanced equipment control/advanced process control (AEC/APC) including run-to-run (R2R) control and fault detection and classification (FDC) is the core methodology in semiconductor process control. In particular, R2R control functions aims to reduce the variability of process output through manipulating the process/equipment parameters. Indeed, developing a R2R control model not only considers process modeling and controller design, but also involve the preference of the decision-maker and the uncertainty in production environments. The decision-maker determines the control hierarchy and the scope of influence factors, and thus has inter-relationship with process modeling and controller design. The uncertain events and production limitations in fab affect the performance of R2R control model, so it is needed to structure the relationship, sense the influence outcomes, and overall evaluation to select the best fitted model in real problems. Focusing on the research gap in literatures and realistic needs, this study proposes a quality engineering framework for advanced process control in semiconductor manufacturing. The decision context is embedded in the proposed framework to support implementation in semiconductor industries. Two empirical studies were conducted to explain the framework process and validate the derived advanced process control model including dynamic adjusted proportional-integral (DAPI) feedback controller for lithography processes and tool similarity feed-forward controller for etch processes.

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