以函數主成份分析來執行錯誤偵測分析

Fault Detection Procedure Based on Functional Principle Component Analysis

許芸榕(Hsu, Yun Jung)

指導教授 ： 曾勝滄

國立清華大學/理學院/統計學研究所/碩士(2015年)

摘要

錯誤偵測與分類為半導體製程中極為重要的分析工具。Lee, et al. (2011) 針對此目的提供一個簡單且有效的統計分析流程來分析製程剖面資料，進而可建構健康指標來衡量晶圓片的健康狀況。唯其模型中的位移量 (level shift) 容易造成批次間的效應與批次內的效應混淆。同時，在許多製程步驟之中，初期的觀測點出現 on-off action，為生產過程中在更換晶圓製造時，生產機台的量測機台會偵測到劇烈的變化，造成初期會不穩定的現象。針對上述問題，本研究先將剖面資料拆解成批次間與批次內的效應。其次，本文採用函數主成份 (functional principle components Analysis) 方法來監控批次內的晶圓片是否有異常，最後，並以Lee, et al. (2011) 所提出的監控製程方法相比較，結果顯示本研究總共發現有9個異常晶圓片，相較於 Lee et al. (2011) 所提出的方法只能偵測出3個屬於first wafer effect的異常晶圓片，本研究方法有明顯的改善效果。

關鍵字

錯誤偵測與分類 ； 函數主成份分析 ； 健康指標

並列摘要

Fault detection and classification (FDC) plays an important role in monitoring IC manufacturing process. Recently, Lee et al. (2011) proposed a simple and efficient model to analyze a typical profile data. One of main concerns in this model is that the “level shift” parameter may be completely confounding with the process lot-to-lot variation; which usually leads to make a wrong decision on FDC. To overcome this difficulty, this thesis first separates the deviations in profile data into within-lot variations and between-lot variations. Due to the fact that the intrinsic lot-to-lot variation is a natural phenomenon of IC manufacturing process, therefore between-lot-variation shall be removed completely before implementing FDC procedure. In this study, we apply the functional principle component analysis (FPCA) technique to investigate wafer-to-wafer (within-lot) variation. A modified health index (HI) has been constructed. Finally, we also compare the proposed method with that of Lee, et al. (2011). The results demonstrate that our method can efficiently overcome the weakness of Lee, et al. (2011).

並列關鍵字

Fault detection and classification (FDC) ； functional principle components analysis (FPCA) ； health index (HI)

參考文獻

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