臺灣博碩士論文加值系統

在半導體產業進入奈米世代後，許多製程對積體電路佈局的敏感度越來越顯著，製程的變異比例也隨著線寬的縮小而提高，許多與電路佈局設計相關的問題已經無法用傳統的檢驗和管制來解決，必須追溯到研發階段之電路佈局設計來做改變與配合，以提升其製造的穩定。從研發設計階段投入不同的測試載具在一片晶圓上，經過設定之製程參數製造後，再將測試載具上每一個Test Structure進行晶圓允收測試(Wafer Acceptance Test；WAT)的參數收集，而研發工程師可藉由WAT電性測試資料，尋找製程空間(Process Window)，而所謂的製程空間就是製程能力，意指晶圓進行所有的製造流程能滿足其設計與製造規範。然而，目前在找出積體電路設計與製程參數最佳組合問題中，皆缺乏一套有效的方法協助研發設計工程師進行分析，需依靠其研發製程參數之個人經驗，因此，本研究提出以分子啟發式粒子群最佳化(Molecular-Inspired Particle Swarm Optimization, MI-PSO)演算法，將測試載具中每一個Test Structure所量測之WAT參數進行分析，找出符合設計規範與電路佈局下表現最佳的參數組合，以提供研發設計工程師有更多的資訊進行可能量產之可製造性的分析。

目錄
摘要 I
Abstract II
謝辭 III
目錄 IV
圖目錄 VI
表目錄 VII
第一章 緒論 1
1.1 研究背景 1
1.2 研究動機 3
1.3 研究目的 4
1.4 研究架構 6
第二章 文獻探討 8
2.1 半導體製程介紹 8
2.1.1 半導體之設計開發流程 8
2.1.2 半導體之製造流程 10
2.2 晶圓測試 15
2.2.1 晶圓測試 15
2.2.2 晶圓允收測試 16
2.2.3 製程空間分析 19
2.3 資料探勘技術 20
2.3.1 資料前處理 21
2.3.2 粒子群最佳化演算法 24
第三章 半導體測試載具中MI-PSO模式之建構 28
3.1 問題定義 28
3.2 資料前處理 32
3.2.1資料標準化 32
3.2.2極端值剔除 32
3.2.3資料正規化 33
3.3 改進型粒子群最佳化演算法 34
3.3.1 粒子分群 34
3.3.2 權重計算 36
3.3.3 分子啟發式粒子群最佳化演算法流程 37
第四章 系統實作與實證分析 40
4.1 系統建構與實作 40
4.2 MI-PSO與基本型PSO結果比較 46
4.3 結果比較與分析 49
第五章 結論與討論 53
5.1 結論 53
5.2 未來研究方向及討論 53
參考文獻 55

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