臺灣博碩士論文加值系統

化學機械研磨是目前唯一可以達到全域性平坦化的技術，它獨特非等向研磨性質廣泛用於晶圓表面輪廓之平坦化，是半導體製程中相當重要的一部份。
由於統計製程控制缺乏有效的調整方法，加上半導體產業缺乏線上控制器，因而發展出批次控制用來改善製程品質。其中，指數加權移動平均法廣泛被應用於化學機械研磨製程。然而，模式修正之權重固定不變，發生製程或誤差變異時，對輸出之控制效果是有限的。
本研究利用結合批次控制與資料探勘的方法，從歷史資料中收集相關資料，探討批次間的關係並找出關鍵因子形成法則樣式，使用粗集合理論萃取出合適的法則，以有效預測出研磨的結果。在製程或誤差發生變異時，能根據預測的結果決定權重的調整方案，使化學機械研磨有較佳的控制效果。而實作結果顯示，本研究之控制方法可以在維持同樣的研磨移除率中，對晶圓表面不均勻度品質提供較好的控制效果。

Chemical mechanical polishing(CMP) is the only technique to achieve global flat. Its unique polishing nature widely uses in the wafer surface outline smoothly, is a quite important part in the semiconductor manufacturing process.
Because statistical process control (SPC) lacks the effective adjustment method, in addition the semiconductor industry lacks on-line controller, thus develops run-to-run control to use for to improve the quality of the process. Among them, exponential weighted moving average (EWMA) is applied widely in chemical mechanical polishing process. However, weight of the model tuning fixed invariable, while taking place the manufacturing process or the error margin variation, to outputs the control effect is limited.
This research combines the method that run-to-run control and data mining, collects the related data from the history data, the discussion of the relation between runs and finds out the key factor to form rule pattern, applies rough set theory extract to leave the appropriate rules, comes to forecast the attrition effectively the result. At the manufacturing process or the error margin occurrence variation, ability according to the forecast result decide the weight the adjustment plan, enable chemical mechanical polishing to have better control result. The experimental result showed that, this research control method may in the maintenance similar removal rate, to the spatial uniformity of the wafer surface provide better control result.

目錄
摘要 i
ABSTRACT ii
誌謝 iii
目錄 iv
圖目錄 vi
表目錄 vii
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化學機械研磨之介紹 6
2.1.1 化學機械研磨之使用 6
2.1.2 化學機械研磨機台設備 7
2.1.3化學機械研磨影響參數 9
2.2 統計製程管制 10
2.3 工程製程管制 12
2.4 批次控制器 13
2.4.1 指數加權移動平均法 13
2.4.2模式預測控制 17
2.4.3最佳品質控制器 18
2.4.4人工智慧類神網路 19
2.4.5設定值方法 20
2.5化學機械研磨實作模擬情境 21
2.6 粗集合理論 23
第三章 研究方法 29
3.1 收集相關資料 30
3.1.1製程飄移之分析 30
3.1.2 選取適合的分析因子 33
3.1.3 時間因子之考量 36
3.1.4 資料轉換與切割 37
3.2 法則之萃取 39
3.2.1 法則挖掘之過程 39
3.2.2 法則之篩選 43
3.3 法則結合批次控制 44
3.3.1 法則之使用 44
3.3.2 控制器之模式 45
3.3.3 控制器加入法則運用 46
3.4 衡量指標 50
第四章 實作研究 51
4.1 產生資料 52
4.2 粗集合法則之萃取 56
4.3 模擬控制的情境 58
4.3.1模擬機台限制 59
4.3.2 批次大小變更 63
4.3.3 機台噪音改變 64
第五章 結論與建議 66
5.1 結論 66
5.2建議與未來發展 67
參考文獻 68
附錄一 71

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