臺灣博碩士論文加值系統

為了提高生產效率、降低成本和改善品質，半導體業的工程師們需要有效的分析工具來處理相關的資料分析以及決策問題。
故障排除分析時，工程師不能僅靠專業物理知識或是電性知識，如此不能解決問題，因為問題成因過於複雜。目前研究如何從製程資料裡擷取知識或是建立生產系統決策規則的研究者很少。而在半導體的良率提昇領域，討論Class Imbalanced Problem的研究就更少了。
因此，此篇論文發展一個創新的決策樹演算法「Main Branch Decision Tree Algorithm」。我們提出的理論將考慮到Focus Class，此目標將會隨著分析者的使用環境不同而能自行訂定，例如將低良率的晶圓設定為Focus Class。此外，我們以此決策樹演算法為基礎，建立一個良率提昇分析架構，並以DRAM晶圓廠的實際資料作為驗證。
我們結合了領域專家的經驗和資料挖礦分析方法，對於製程資料進行分析，將可疑問題成因做成報告，提供給工程師作為依據。我們協助工程師縮小可疑原因的範圍，並縮短故障排除的時間，藉此將可提昇良率並降低產品損失。

In order to response the production promoting, cost-reducing and quality-improving in semiconductor industry, engineers need effective analytical method to deal with relevant data analysis and decision problem.
While making trouble shooting in semiconductor industry, engineers can not only use the expert knowledge on physics or electronics to answer the problem because of numerous relevant analysis factors. At present, only few researchers study how to acquire knowledge from manufacturing data and describe the characteristic of production system in the form of decision rules. There are almost none related research which is about yield enhancement in semiconductor manufacturing talk about the class imbalanced problem encountered in data analysis.
So, this thesis develops a new decision tree algorithm called “Main Branch Decision Tree Algorithm” which is different from the general decision tree. The proposed algorithm concerns about the user-defined focus class in dataset for the specific situation, such as finding the root cause of yield-loss wafer in tremendous instances. And we suggested a framework based on our proposed decision tree algorithm and conducted an empirical study in a DRAM FAB for yield enhancement for validation.
We combine the domain expert's experience and data mining methodology to sum up the assignable root-cause of the manufacture system, and offer the engineers the reference basis of solving the problem. We help engineers to shrink the range of possible causes, and shorten the time of trouble shooting, so as to improve yield and prevent more suffered wafer.

簡禎富、李培瑞、彭誠湧（2003），「半導體製程資料特徵萃取與資料挖礦之研究」，資訊管理學報，第十卷，第一期，63-84頁。
王文志（2003），「實驗設計為基礎架構之資料挖礦方法及其實證研究」，國立清華大學工業工程與工程管理學研究所碩士論文。
林鼎浩（2000），「建構半導體製程資料挖礦架構及其實證研究」，國立清華大學工業工程與工業管理研究所碩士論文。
林大欽（1997），「IC封裝業之短期生產排程之探討」，國立清華大學工業工程與工業管理研究所碩士論文。
李培瑞（2000），「半導體製程資料挖礦架構、決策樹分類法則及其實證研究」，國立清華大學工業工程與工業管理研究所碩士論文。
鄭仁傑（2003），「以混合決策樹方法分析有相互關係之半導體製造資料」，國立清華大學工業工程與工業管理研究所碩士論文。
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