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系統識別號 U0026-0801201621272600
論文名稱(中文) 整合資料探勘與時間序列之半導體零件變異預測模型
論文名稱(英文) An Integrated Model of Data Mining and Time Series in Predicting Variations of Semiconductor Components
校院名稱 成功大學
系所名稱(中) 工業與資訊管理學系碩士在職專班
系所名稱(英) Department of Industrial and Information Management (on the job class)
學年度 104
學期 1
出版年 105
研究生(中文) 羅勝興
研究生(英文) Sheng-Sing Luo
學號 R37031215
學位類別 碩士
語文別 中文
論文頁數 76頁
口試委員 指導教授-黃宇翔
口試委員-翁慈宗
口試委員-胡政宏
中文關鍵字 半導體製造  資料探勘  時間序列分析  ARIMA模型  主成分分析 
英文關鍵字 Semiconductor  Data Mining  Time Series  Autoregressive Integrated Moving Average Model  Principal Component Analysis 
學科別分類
中文摘要 當半導體正式進入奈米製程世代,且因應全球市場對於資訊電子產品傾向輕、薄、短、小的特性與高速化及高性能化的急遽發展,迫使半導體公司致力於縮小晶片內電路線之寬度,以獲得全球半導體產業競爭優勢,使得半導體製程更為複雜冗長、影響變數更多以及技術門檻愈來愈高。為克服製程技術不斷衍生的物理極限,半導體設備發展出各項精密感測器達成各種即時監控與偵測系統來提升晶圓良率與生產效率,但是並非所有半導體設備零件皆適用於裝載感測器來避免產出具缺陷的低良率晶圓。因此,本研究應用資料探勘技術來結合無感測器偵測所導致的晶圓缺陷特徵與其晶圓量測資料,據以建構一套晶圓缺陷偵測模型,並整合時間序列模型以預測未來零件發生變異之趨勢,進而發展出半導體零件變異預測模型,以利實現智慧製造系統之即時反饋零件生產狀態,達成長期監控以及預測與評估零件變異徵兆,來有效避免零件變異所衍發的大量晶圓良率損失問題。本研究論文最後將透過半導體覆晶封裝之凸塊製程的實例研究,闡明本研究模型之各項結構,如資料變數彙整與處理過程、晶圓缺陷分析與驗證、資料探勘建模與配適過程以及時間序列分析與ARIMA模型之應用,且經由實例成果證實本研究模型能夠有效地評估無感測器之半導體零件變異徵兆,並能準確地預測未來變異之趨勢,以排除量產前的預檢修成本與妥善評估零件檢修最佳時機,進而提升半導體設備整體效率、穩定晶圓良率以及節省半導體整體製造成本。
英文摘要 The study constructs a non-sensor semiconductor components variation prediction model, in which a data-mining framework is proposed based on clustering and decision-tree methods to mine solder bumps of flip-chip package measurement data and defect rates with variation patterns of wafers from huge databases, and thus formulate defect characteristics. This study uses the Principal Component Analysis (PCA) to develop a health index for identifying variations in semiconductor components over time. We also use the ARIMA model to obtain signs of variations, predict future variations in components, and ensure the timing for components maintenance. The obtained results show that the proposed model can successfully predict the components variation trends, reduce the consumption of manpower and production time, effectively enhance the quality and production capacity of wafers, and decrease the overall semiconductor manufacturing costs.
論文目次 第一章 緒論 1
 第一節 研究背景 1
 第二節 研究動機 3
 第三節 研究目的 5
 第四節 研究範圍與重要性 5
 第五節 論文架構 6
第二章 文獻回顧與探討 7
 第一節 半導體覆晶封裝技術簡介 7
  一、晶圓凸塊製程簡介 8
  二、電化學金屬沉積 10
  三、晶圓凸塊檢驗 12
 第二節 大數據與資料探勘 13
  一、資料探勘技術 15
  二、資料探勘流程 17
  三、時間序列資料探勘 20
 第三節 大數據資料探勘於半導體產業之運用 21
第三章 電鍍凸塊之零件健康度預測模型 25
 第一節 問題描述 25
 第二節 研究架構 27
 第三節 資料收集與預處理 29
 第四節 晶圓缺陷特徵化與資料轉換 31
 第五節 資料探勘與模型建立 33
  一、群集分析 33
  二、主成分分析 36
  三、界定控制指標 38
  四、模型校正時機 39
 第六節 時間序列分析與預測模型 39
 第七節 變異預測模型與零件檢修時機 44
第四章 實例分析 46
 第一節 晶圓缺陷特徵化與資料轉換 47
 第二節 資料探勘與模型建立 51
 第三節 時間序列分析與預測模型 58
 第四節 實施模型結果比較分析 62
第五章 結論 67
 第一節 研究貢獻 67
 第二節 研究限制 68
 第三節 未來研究方向 69
參考文獻 70

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