臺灣博碩士論文加值系統

半導體製造過程包含繁複製作步驟，製作完成後，對晶圓切割下的每顆晶片做測試來確認其是否能正常運作。每批產品的正確率被稱為良率，而提高良率是半導體廠營運重要的課題之一。要使得產品的良率提高，必須找出製造過程中的錯誤來加以改進，然而製程變數非常多，在此情況下，較為便利的方式便是藉由晶圓圖上的瑕疵圖形去推測製造過程中發生的問題。目前分析晶圓圖的工作仍然是靠著目視去判斷，這是一項依靠經驗的工作，不同的工程師對於同一張晶圓圖可能會判斷出不同的結果，導致分類缺乏一致性，而且缺乏效率。為了使上述的不可抗力因素降低，我們希望憑藉資料分析的方法，從目前所擁有的分類實例中學習判斷法則，建立快速、有效、一致的自動化系統來分辨晶圓上的錯誤圖形。本論文提出自動化分析晶圓圖的方式，將瑕疵圖形分類為，幾何型，重複型，隨機型三種瑕疵圖形。改善現有方法無法將重複型以及隨機型圖形分類的缺點，並利用空間統計分析的方式將幾何型瑕疵自動分類，再對其加以改善，提出了新的作法，使得分類的效果更好。

Product yield is very important for integrated circuit (IC) fabrication. Because of the complex fabrication processes, it is critical to identify possible faults (ex. machine problems or parametric errors) through discriminating the failure patterns on wafer. But there will be a lot of problems if we recognize defect pattern by human operation. So designing an automatic system to classify defect pattern by standard rules efficiently is very imperious. In this paper, we develop a method to classify defect patterns to three different types (repeat, geometric, and random) automatically. That new method can identify repeat type and random type, and there is not any current method can do this. It also d make the spatial statistic method better than now, therefore we can get better result in classifying geometric and random patterns.

中文摘要 i
英文摘要 ii
誌　　謝 iii
目　　錄 iv
表 目 錄 vi
圖 目 錄 vii
第一章 序論 1
1.1研究動機與目的 1
1.2問題特徵 2
1.2.1隨機型瑕疵圖形 3
1.2.2幾何型瑕疵圖形 4
1.2.3重複型瑕疵圖形 6
1.3文獻回顧 8
1.4系統架構與論文架構 11
第二章 幾何圖形識別 13
2.1 空間統計分析 15
2.1.1鄰近區域 15
2.1.2鄰接計數統計 16
2.1.2勝算比 17
2.1.3空間相關係數 18
2.1.4空間瑕疵分佈對 19
2.1.5正規化空間瑕疵分佈對 20
2.2區域機率法 22
2.3中位濾波 23
2.4邊緣增強 26
第三章 重複圖形識別 28
3.1空間自相關 28
3.2 傅立葉轉換 33
3.3 頻率峰值偵測 38
第四章 實驗與討論 40
4.1分類樹 40
4.2分類結果評估準則 43
4.3重複圖形分類 44
4.4幾何圖形分類 50
4.4.1區域機率法實驗 50
4.4.2空間統計分析實驗 52
第五章 結論 61
參考文獻 63

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