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結論

在文檔中 中 華 大 學 (頁 60-66)

本論文利用ANOVA 與時間序列分析法對量測值歷史資料建立製程模型;為了預 測製程干擾與系統參數,將時間序列之製程模型轉換為狀態空間形式,並利用擴張卡 曼濾波器估計方法求得所需的預測製程干擾與估計參數。文中將 D-EWMA 和 time-varying D-EWMA 與擴張卡曼濾波器比較其預測效能,由第 5 章比較結果可知,

本文所提出的方法比之D-EWMA 和 time-varying D-EWMA 兩種方法而言,有著更好 的效能表現。

由第5 章的結果可以得知以下結論:

1. 利用 AVONA 分析截距,搭配執行序的使用,可以應用於半導體製程混貨生 產的製程需求,並且可以減少預測的誤差率,使預測值更具有可信度。

2. 擴張卡曼濾波器的參數估計會不斷的自動調整,比 D-EWMA 和 time-varying D-EWMA 之固定權重更為符合實際狀況。

3. 從模擬預測之比較值與實際製程之比較值可說明本論文提出的方法效能確實 優於D-EWMA 和 time-varying D-EWMA。

綜合以上,在本文中D-EWMA 與 time-varying D-EWMA 之權重由數據計算最小 的MSE 值而得,然而在實際生產過程中,權重是由工程師之經驗值所給定,由經驗 值給定權重之控制器效能並不會優於本文中由最小MSE 值所計算權重之效能,而本 文所提出之方法效能又優於給定最佳權重之方法,因此可證實由本論文所提供之方法 確能有效做到製程預測。

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在文檔中 中 華 大 學 (頁 60-66)

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