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研究生:陳麗伃
研究生(外文):Li-Yu Chen
論文名稱:以資料挖礦為基礎之半導體測試性WAT資料分析診斷
論文名稱(外文):A Diagnostic Analysis for the Test Vehicle of Wafer Acceptance Test Base on Data Mining in Semiconductor Manufacturing
指導教授:陳飛龍陳飛龍引用關係
指導教授(外文):Fei-Long Chen
學位類別:碩士
校院名稱:國立清華大學
系所名稱:工業工程與工程管理學系
學門:工程學門
學類:工業工程學類
論文種類:學術論文
論文出版年:2004
畢業學年度:92
語文別:中文
論文頁數:79
中文關鍵詞:半導體測試性製程工程資料分析晶圓允收測試資料挖礦自組織特徵映射網路
外文關鍵詞:SemiconductorTest VehicleEngineering Data Analysis(EDA)Wafer Acceptance Test(WAT)Data MiningSelf-Organize Feature Map(SOM)
相關次數:
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半導體是一個製程相當複雜的產業,其產品具有生產周期長與生命週期短之特徵。因為半導體產業產品更新迅速,擁有快速提升測試性製程良率的能力是相當重要的。在半導體測試製程當中,通常使用晶圓允收測試資料分析其樣型(patterns),以判定及診斷出可能異常的製程。本研究使用資料挖礦的概念,配合使用類神經網路中自組織特徵映射網路方法,建立測試性WAT參數特徵值之萃取的模式,使得隱含在測試性晶圓中高維度的測試性WAT資料中可能影響良率的因素得以發現。
本研究蒐集了某知名半導體廠中實際測試製程中的WAT參數資料,並以所建立的分析診斷架構進行測試。從所得到的實證結果發現,本研究方法可以找出測試性參數在晶圓上所呈現的樣型,如進一步利用WAT參數工程知識庫追蹤可能的問題製程,則可以達到快速穩定測試性製程,並提升測試性產品良率的目標。
Semiconductor manufacturing is an extremely complex undertaking. With the characteristic of long processing time and shorter product life cycle, it is important to build up the capability of quickly improving the yield of test vehicles to shorten the time lag between test runs and mass production. To investigate the performance of the test vehicles, wafer acceptance test(WAT)needs to be conducted to see whether there exists any special pattern or not. This kind of diagnosis is usually inefficient since it is performed through visual judgment. For this reason, this research use the concept of data mining, together with the Self-Organize Feature Map(SOM)of Neuron Network method, to automatically identify the existence of special WAT patterns. The possibly faulty causes of the test process in the fabrication can then be discovered.
Real WAT data of test vehicles obtained from a famous semiconductor manufacturing company were experimented through the developed diagnostic system. The results show that the system can recognize the existence of clustering phenomenon and patterns on wafers. Possible causes of the patterns can also be inferred from the established WAT knowledge base. By the assistance of this system, engineers can more effectively find out process problems on test vehicle process problem to achieve the goal of yield enhancement.
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