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研究生:陳杰笙
研究生(外文):Chen, Jie-Sheng
論文名稱:化學機械研磨加工參數多目標最佳化程序
論文名稱(外文):Multi-Objective Optimization Process for Chemical Mechanical Polishing Parameters
指導教授:楊棧雲楊棧雲引用關係
指導教授(外文):Yang, Chan-Yun
口試委員:詹景裕陳炤彰王碩仁楊智旭楊棧雲
口試委員(外文):Jan, Gene-EuChen, Chao-ChangWang, Shoue-JenYang, Jr-SyuYang, Chan-Yun
口試日期:2023-09-11
學位類別:碩士
校院名稱:國立臺北大學
系所名稱:電機工程學系
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2023
畢業學年度:112
語文別:中文
論文頁數:63
中文關鍵詞:非支配排序遺傳演算法-II層級分析法帕雷托前沿化學機械研磨
外文關鍵詞:NSGA-IIAHPPareto FrontierCMP
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這項研究的核心方法涉及將 NSGA-II(非支配排序遺傳算法-II)與 AHP(層次分析法)相結
合,以驗證多目標最最佳化和多準則決策的結合方法,強調它在提高最佳化結果準確性方面
的優越性。傳統的多準則決策方法,例如僅使用 AHP,可能會在處理複雜的工程問題時受到
早期收斂和部分解未被考慮的問題的製約。
在這項研究中,使用 CMP 機台 PM5 進行資料蒐集,透過綜合應用 NSGA-II 和 AHP,旨
在實現更全面、精確且具有實際應用潛力的最佳化結果。 NSGA-II 用於處理多目標最佳化問
題,它採用了非支配排序方法和多樣性預存策略,以確保發現 Pareto 前沿上的多樣性解決方
案。與此同時, AHP 用於多準則決策,它透過建立成對比較矩陣、求解特徵向量以及檢查一
致性,可以有效地分配權重和生成綜合的評估分數。
這一綜合方法的優勢在於它不僅提供了更好的最佳化準確性,還解決了傳統方法可能出
現的問題。透過結合這兩種方法,可以更全面地考慮工程問題的各個方面,避免了單一方法
可能導致的偏差和不完整的結果。這種方法在工程和製造領域有著廣泛的應用前景,特別適
用於需要高度精確性和全面性的最佳化問題。這項研究為實際應用提供了有力的工具,以提
高決策過程的效率和質量。
The core methodology of this study involves the integration of NSGA-II (Non-dominated Sorting Genetic Algorithm II) with AHP (Analytic Hierarchy Process) to validate the combined approach of multi-objective optimization and multi-criteria decision-making, emphasizing its superiority in enhancing the accuracy of optimization outcomes. Traditional multi-criteria decision-making methods, such as using AHP alone, may be constrained by early convergence and the omission of partial solutions when dealing with complex engineering problems.In this study, data collection was carried out using CMP machine PM5, and a comprehensive application of NSGA-II and AHP was aimed at achieving more comprehensive, precise, and practically applicable optimization results. NSGA-II was used to handle multi-objective optimization problems, employing non-dominated sorting techniques and diversity-preserving strategies to ensure the discovery of diverse solutions on the Pareto front. Meanwhile, AHP was employed for multi-criteria decision-making, effectively assigning weights and generating integrated assessment scores through the construction of pairwise comparison matrices, eigenvector computation, and consistency checks.The key advantage of this integrated approach is that it not only provides improved optimization accuracy but also addresses potential issues associated with traditional methods. By combining these two approaches, a more comprehensive consideration of various aspects of engineering problems is achieved, mitigating biases and incomplete results that a single method might lead to. This approach holds significant promise for applications in engineering and manufacturing fields, particularly in cases requiring high precision and comprehensiveness in optimization problems. This research provides a robust tool for enhancing the efficiency and quality of the decision-making process in practical applications.
致謝 I
中文論文提要 III
ABSTRACT IV
目錄 VI
圖目錄 VIII
表目錄 X
第一章 緒論 1
1.1. 研究動機 1
1.2. 研究目的 5
1.3. 論文架構 5
第二章 文獻探討 7
2.1. 多目標最佳化 7
2.1.1. NSGA-II 8
2.2. 多準則決策 10
第三章 研究方法與理論 14
3.1. 多目標最佳化 14
3.1.1. 決策變數 14
3.1.2. 目標函數及最佳化問題 14
3.1.3. 映射空間 15
3.1.4. 帕雷托優勢(Pareto Dominance) 16
3.1.5. 帕雷托最佳解集與帕雷托前沿 16
3.2. NSGA-II 17
3.2.1. 非支配排序方法(Nondominated Sorting Approach) 18
3.2.2. 多樣性預貯(Diversity Preservation) 20
3.2.2.1. 密度估計(Density Estimation) 20
3.2.2.2. 擁擠比較運算子(Crowded-Comparison Operator) 22
3.2.3. NSGA-II主要流程 22
3.3. 多準則決策 24
3.4. 層級分析法(ANALYTIC HIERARCHY PROCESS) 24
3.4.1. 建立成對比較矩陣 25
3.4.2. 求解權重(特徵向量) 28
3.4.3. 檢查一致性比率(Consistency Ratio) 28
3.4.4. 計算加權分數 29
第四章 加工參數多目標最佳化 33
4.1. 流程圖 33
4.1.1. NSGA-II流程圖 33
4.1.2. AHP流程圖 34
4.2. 程式介紹 35
4.2.1. NSGA-II 35
4.2.2. AHP 36
第五章 資料蒐集 37
5.1. 實驗設備與材料 37
5.1.1. CMP研磨拋光機 37
5.1.2. 摩擦力量測系統(Friction Sensor System, FSS) 39
5.1.3. 電壓電流計 40
5.1.4. 精密分析天平 41
5.1.5. 綠光干涉儀 42
5.1.6. 實驗試片 45
5.1.7. 拋光液 46
5.2. 實驗流程圖 47
第六章 結果與討論 48
6.1. 實驗數據整理與說明 48
6.1.1. 重現性(Repeatability) 49
6.1.2. 總能耗 51
6.1.3. MRR 52
6.1.4. 表面粗糙度 53
6.2. NSGA-II之帕雷托前沿 54
6.3. AHP之多準則決策範例 56
6.4. 與單純使用AHP直接決策方法的比較 59
6.5. 考慮碳稅與否之最佳參數比較 60
第七章 結論與未來展望 63
7.1. 結論 63
7.2. 未來展望 63
參考文獻 65
圖目錄
圖 1 摩爾定律發展方向 2
圖 2 FOWLP AND 3DS-IC 2
圖 3 各種元件之薄化製程工具獲利預測 3
圖 4 晶圓薄化製程改善裝置元件效能表現 3
圖 5 非支配排序方法圖例 19
圖 6 擁擠距離計算 21
圖 7 NSGA-II 流程 23
圖 8 本研究之AHP樹狀圖 25
圖 9 總能耗正規化圖表 30
圖 10 表面粗糙度正規化圖表 30
圖 11 所有數據之總能耗正規化 31
圖 12 所有數據之表面粗糙度正規化 31
圖 13 NSGA-II流程圖 33
圖 14 AHP流程圖 34
圖 15 PM5 38
圖 16 FSS量測系統與其內部 39
圖 17 蔡岳勳CMP/EKF-CMP 於銅圖案化晶圓之製程中 COF 40
圖 18 PM5實際COF表現 40
圖 19 精密分析天平 41
圖 20 綠光干涉儀 43
圖 21 40MM*40MM晶圓底板 44
圖 22 附點位之40MM*40MM晶圓上蓋 44
圖 23 上機操作畫面 45
圖 24 實驗流程圖 47
圖 25 放置晶片之載體 48
圖 26 各轉速下壓力總能耗比較 51
圖 27 各轉速下壓力MRR比較 52
圖 28 各轉速下壓力表面粗糙度比較 53
圖 29 帕雷托前沿的形成 55
圖 30 帕雷托前沿之二維視圖拆分 55
圖 31 帕雷托前沿解之總能耗正規化分數 58
圖 32 帕雷托前沿解之表面粗糙度正規化分數 58
圖 33 帕雷托前沿可行解之加權分數 59
圖 34 所有轉速下壓力加權分數 60
圖 35 無考慮總能耗之帕雷托前沿的形成 61
圖 36 無考慮總能耗之帕雷托前沿二為視圖拆分 61

表目錄
表 1 多目標最佳化方法比較 8
表 2 多準則決策方法比較 11
表 3 nP與SP關係表 19
表 4 重要程度量表 25
表 5 相對重要程度量表 26
表 6 成對比較矩陣 A 27
表 7 隨機一致性指標 RI 29
表 8 本研究參考NSGA-II之虛擬碼 35
表 9 AHP虛擬碼 36
表 10 PM5規格表 38
表 11 精密分析天平規格表 42
表 12 試片耗材 46
表 13 C8902 拋光液基本物性表 46
表 14 實驗程序重現性變異率 50
表 15 帕雷托前沿上數據點 56
表 16 不考慮總能耗之帕雷托前沿數據點 62


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