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題名：虛擬量測決策支援系統及半導體製造廠之實證研究
作者：洪瑋澤
作者(外文)：Hung, Wei-Tse
校院名稱：國立清華大學
系所名稱：跨院國際博士班學位學程
指導教授：簡禎富
學位類別：博士
出版日期：2023
主題關鍵詞：先進製程控制系統；虛擬量測；決策支援系統；批次控制系統；錯誤偵測與分類；深度學習；卷積神經網路；Advanced Process Control；Virtual Metrology；Decision Support System；Run-to-Run Control System；Fault Detection and Classification；Deep Learning；Convolutional Neural Network
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半導體產業藉由導入先進製程控制技術以確保在複雜且冗長的製造過程中，運用子系統達到對製程變異的掌握、監控及控制，進而確保整體製程良率與產能。其中，虛擬量測技術被用以提高半導體先進製程控制的性能，藉由預測未抽樣晶圓的品質特性特徵值，將原有的製程控制層級從批次控制提升至晶圓控制，以應對低抽樣率所造成的量測延遲影響。
然而，在既有的虛擬量測研究中，多數著重於提升模型預測準確率作為瓶頸突破點，鮮少有研究從虛擬量測技術對先進製程控制及產能提升之交互支援關係為面向進行探討。本研究採用紫式決策分析架構有系統地構建虛擬量測決策支援系統，藉由考量虛擬量測與批次控制間對製程的循環影響以及實務部署陷阱作為預測模型建立的依據，並考量預測錯誤的風險考量與錯誤偵測與分類系統所提供的異常監控狀態資訊，作為製程工程師執行晶圓補償的決策支援，從而提升整體先進製程控制系統的性能。
本研究藉由兩項實證研究作為架構可行性的驗證，第一項實證研究是在考量預測誤差對批次控制系統的風險情況，建構決策型虛擬量測架構以確保批次控制系統可穩定進行晶圓補償，實證結果顯示該架構有效避免誇張的錯誤預測，確實捕捉整體趨勢，並藉由預測信心分數構建補償決策以確保批次控制系統可穩定補償晶圓。第二項實證研究提出簡化重新制定監控規則的策略，透過遷移學習及圖像辨識技術加速異常SVID的識別，並藉由視覺化方式提升模型說服力，實證結果顯示可確實識別出異常SVID及異常區間，有效縮短在新變異發生時重新制定監控規則的時間。
Semiconductor manufacturing employed advanced process control (APC) to ensure the yield and production capacity under complex processes. APC system can be roughly divided into three subsystems according to different purposes to control, assess, and monitor the process variance. In the case of continuous technology migration of semiconductor manufacturing, many studies developed various technologies to improve the validity of each subsystem. However, the effectiveness of yield enhancement by improving each subsystem individually remains limited. A framework that considers the interaction relationship between APC subsystems is needed to improve the existing gap. Therefore, this study proposed a decision support system of virtual metrology (VM) that consider the influence between VM and run-to-run control (R2R), and the interaction between fault detection and classification (FDC) and VM. And establish a decision-making process to assist engineers in effectively using the predicted value of quality characteristics to achieve the goal of yield enhancement.
Two empirical studies were conducted in a leading semiconductor manufacturing company in Taiwan to validate the feasibility of the proposed framework. The first study constructs a decision-based virtual metrology framework that considered the case of failure risk when using VM predictors in R2R systems. The second study proposed a strategy to simplify the process of the redefine monitoring rule.
李家岩、洪佑鑫 (2022)，製造數據科學，前程文化，新北市。
簡禎富 (2015)，決策分析與管理，雙葉書廊，台北。
簡禎富 (2019)，工業3.5，天下雜誌，台北。
簡禎富 (2022)，藍湖策略，天下雜誌，台北。
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