臺灣博碩士論文加值系統

我國目前仍為晶圓代工重鎮，然而，若想在全球競爭激烈環境下繼續保有領先地位，必須維持良率與價格競爭優勢。隨著半導體元件尺寸持續縮減，微影曝光解析度也須相對增加，對於晶圓表面平坦化要求更為嚴荷，而化學機械研磨是唯一能達成全域平坦化之技術，若能提高化學機械平坦化之性能，便能提升最終晶圓之品質。
傳統統計製程管制用於系統穩定態，但產品背後之機率分配會隨時間改變，且統計製程管制並未對異常製程提出有效之補救措施。回饋控制雖可提出補救措施，但不當控制器反而會造成控制反效果。批次控制結合兩者方法，然而在眾多批次控制器中，指數加權移動平均法(Exponentially weighted moving average;EWMA)廣泛應用於半導體製程上，但對於具有老化現象之製程，EWMA仍有其不足之處，本研究即使用資料探勘之關聯式法則補足其不足之處。
本研究採用關聯式法則萃取出誤差模式間之關係，使批次間誤差具有相依性以適用於具有老化現象之化學機械研磨，提出新參數預測模式誤差，找出適用於老化現象之機台處方，期使輸出值與目標值間之誤差降至最小或達期望目標。

At present, it is important for our country to produce wafer. Howerver, saving superiority of yield and price can maintain the leader in the world. With the decrease of the feature size, flatness on the wafer is necessary. Chemical mechanical polishing is the only technique to achieve global flat. If we can enhance the performance of Chemical mechanical polishing, we can improve the quality of the wafer.
Statistical Process Control (SPC) adapts to static systems. Probability distributions will change with time, and SPC does not prescribe control actions, so it is not suitable to control. Fackback control brings up control actions, but unsuitable controller leads to instable system. Run-by-Run combine two method. Exponentially weighted moving average (EWMA) be widely applied to semiconductor manufacturing processes. Because EWMA does not have good performance in aging process, we make use of the association rule to strengthen EWMA.
In our research, we use association rule to extract error model. By dependence of error model, we forecast error in the future and suggest recipes to reduce error between output and goal.

摘要 I
Abstract II
致謝 III
目錄 IV
圖目錄 i
表目錄 ii
第一章 緒論 1
1.1研究背景與動機 1
1.2研究目的與範圍 2
1.3研究方法與步驟 3
1.4論文的研究架構 5
第二章 文獻探討 6
2.1化學機械研磨 6
2.1.1化學機械研磨背景 6
2.1.2化學機械研磨機能 7
2.1.3化學機械研磨衡量指標與模擬情境 9
2.2 統計製程管制 12
2.3工程製程管制 14
2.3.1指數加權移動平均法 14
2.3.2預測校正控制器 17
2.3.3模式預測控制 19
2.3.4最佳適應品質控制器 21
2.3.5人工智慧類神網路 22
2.3.6設定值法 23
2.4批次控制 24
2.5關聯式法則 26
第三章 研究方法 27
3.1 誤差產生原因 27
3.2 樣式選擇 29
3.2.1 誤差決定法則 29
3.2.2 輸出值過濾法則 31
3.2.3 法則與時間之關聯性 32
3.2.4 輸入輸出相依性之探討 33
3.3 由資料萃取法則 34
3.3.1 法則萃取過程 35
3.3.2 資料切割 37
3.4 法則與批次控制方法合併 39
3.4.1 法則功用 39
3.4.2 法則應用於控制器 42
3.5衡量標準 46
第四章 實驗設計 47
4.1資料轉換與法則萃取 48
4.2 實驗參數決定 54
4.3 模擬控制的情境 58
4.3.1 模擬機台限制 59
4.3.2 工程能力變更 60
4.3.3 機台噪音改變 61
4.3.4製程產生平移 63
第五章 結論與未來研究方向 64
5.1 結論 64
5.2 未來研究方向 65
參考文獻 66
附錄一 69
附錄二 90

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