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研究生:林盈宏
研究生(外文):LIN, YIN-HUNG
論文名稱:整合資料探勘與類神經網路於半導體厚度製程之虛擬量測
論文名稱(外文):Virtual Metrology of Semiconductor Thickness Using Data Mining and Neural Network
指導教授:曾秋蓉曾秋蓉引用關係
指導教授(外文):Judycrt
學位類別:碩士
校院名稱:中華大學
系所名稱:資訊工程學系碩士班
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:2006
畢業學年度:94
語文別:中文
論文頁數:75
中文關鍵詞:資料探勘機率類神經主成份分析厚度量測製程品質控制製程能力參數
外文關鍵詞:Data MiningProbabilistic Neural NetworkPrincipal Component AnalysisThickness Control ProcessQuality ControlCapability process index
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  • 被引用被引用:1
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  • 收藏至我的研究室書目清單書目收藏:3
半導體工程隨著時間的推移進入了更微小的奈米時代,先進製程控制(Advanced Process Control,簡稱APC)的觀念漸漸的普及在半導體生產工廠中。先進製程控制的應用模組回饋與前饋批次控制(Run-to-Run,簡稱R2R)與故障偵測與分類(Fault Detection and Classification,簡稱FDC)系統已成為今日半導體製造的必要能力。半導體以DRAM的製程而言,高達300道製程以上的程序,隨著製程技術的更新,線寬從0.25微米微縮至目前0.09將來更會發展到0.06以下;這意味著一片晶圓的價值將呈倍數性的成長。因此利用先進製程控制系統穩定製程能力,降低產品損失,更進一步提昇良率,將是新一代半導體廠獲利的關鍵之一。
以往常利用QC的檢測、統計製程管制(Statistical Process Control,簡稱SPC)的監控來控制生產品質,以減低產品不良率控制,但是因為每一種製程或是設備並非每次生產都會被固定在預設好的製程控制範圍內生產產品。因此當R2R系統資料的收集時,只能使用量測資料透過統計方法,去解釋部份資料,達到推估全部資料的結果。但是統計資料會因人為或自然的突然改變製程參數及量測資料抽測結果的樣本數過少,導致統計結果的誤差,造成R2R系統不能夠準確的控制生產品質。因此如何增加R2R資料回饋的準確度,成為了當今半導體的一個重要課題。
本研究將使用類神經機率神經網路,結合資料探勘分群技術與主成份分析兩種方法,進行量測資料與FDC系統生產資料的結合,發展出一套用來預測製程參數生產結果之系統,提供製程參數調整R2R系統控制之依據。以半導體厚度量測製程為例,從實際生產資料共取得600筆樣本進行訓練、30 筆樣本進行三次測試。經本研究驗證後發現此預測之機制可推論出結果成功率大於80%,且平均誤差百分比(Mean Absolute Percentage Error, 簡稱MAPE)值皆小於20%,並將R2R製程能力參數(Capability process index,簡稱Cpk)值提升至1.9。顯示本研究所提出的厚度預測方法,其準確度極佳。結合此預測系統於FDC與R2R系統之間,將可達到厚度量測全面品質管理、縮短產品生產時間及改善生產良率。
Semiconductor enters the age of NANO as time goes by; the concept of Advanced Process Control(APC)has gradually been used into semiconductor manufactories. Nowadays the Run-to-Run(R2R)and the Fault Detection and Classification(FDC)systems of Advanced Process Control have become essential in semiconductor manufacture. In terms of DRAM processes which more than 300mm; the trench width shrinks from 110nm to 90nm and may develop to under 60nm in the future by the renewal of process technology. This means the value of a wafer will show multiple growths. Thus taking advantage of APC to stabilize processes, to reduce the production loss and to improve the yield rate will be one of the key factors of profit-making in the new generation of semiconductor manufactories.
But how to decrease the bad production rate, in the past, QC examination and Statistical Process Control(SPC)were often used to monitor and control the production quality. However, each process or equipment may not produce in the same fixed pre-determined process procedures in every production; therefore when collecting the data of R2R system, we just can infer a conclusion of complete data through partial data by using statistic methods. Even so, man-made or the natural suddenly changed process parameter and few sampling of data measurement may result in errors. This causes the inaccuracy of R2R system. For this reason, how to improve the accuracy of R2R data feedback becomes one of the important tasks in semiconductor industry.
This research will combine Probabilistic Neural Network, clustering analysis in Data Mining and Principal Component Analysis to develop a system to forecast production results of process parameters by using measuring data and FDC production data to serve as the R2R system control basis of process parameter adjustment. Take semiconductor thickness measurement process for example to obtain 200 samplings of real production data to train, 10 samplings to examine. After tests and verification, the research finds this forecast function can infer the result up to 80%, with the Mean Absolute Percentage Error(MAPE) less than 20%. It shows parameter combination result inferred by parameter adjustment function has great accuracy. Combining this forecast system with FDC and R2R systems will reach overall quality management of thickness measurement, shorten the production time and improve the yield rate.
摘要 II
Abstract IV
目錄 VI
圖目錄 VIII
表目錄 X
第一章 簡介 1
1.1研究動機與目的 1
1.2 研究方法 3
1.3 研究成果 5
1.4 研究架構 5
第二章 研究背景 6
2.1 半導體生產厚度沉積製程 6
2.1.1 化學氣相沉積原理簡介 8
2.1.2 化學氣相沉積系統的種類 10
2.1.3 電漿增強化學氣相沉積 11
2.1.4 薄膜性質參數 15
2.2晶圓廠生產系統簡介 17
第三章 厚度製程預測系統 20
3.1 厚度製程預測系統流程步驟 20
3.2 資料探勘與厚度製程預測 26
3.2.1 資料分群演算法則 26
3.2.2資料分群與半導體量測 28
3.2.3主成份分析與半導體生產 30
3.3 機率神經網路與厚度製程預測 33
3.3.1 機率神經網路架構與演算法則 34
3.3.2 機率神經網路與半導體生產 39
第四章 實驗與分析 42
4.1 研究環境與資料準備 42
4.2 資料前處理與量測資料分群 43
4.3 生產資料與主成份分析 47
4.4 機率類神經學習與驗證 49
4.5 分析研究結果 51
第五章 結論與未來發展方向 56
5.1 結論 56
5.2 未來發展方向 57
參考文獻 58
附錄 61
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