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研究生:邱雅惠
研究生(外文):Ya-Hui Chiou
論文名稱:應用決策樹於含檢驗誤差之批量檢驗計劃~C2F6半導體原料為個案分析
論文名稱(外文):Applying Decision Tree to Reduce Costs When Inspection Sampling Errors are Present~ A Case Study of C2F6 Gas in Semi-conductor Manufacturing
指導教授:徐旭昇徐旭昇引用關係
指導教授(外文):Chiuh-Cheng Chyu
學位類別:碩士
校院名稱:元智大學
系所名稱:工業工程與管理學系
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:2009
畢業學年度:97
語文別:中文
論文頁數:95
中文關鍵詞:半導體製程決策樹抽樣檢驗檢驗誤差模式驗證敏感度分析
外文關鍵詞:Semi-conductor manufacturingdecision treesampling inspectiontypes I and II errorsmodel validationsensitivity analysis
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氣體產品是現代工業重要的基礎原料,廣泛應用於化工、石化、機械、電子、食品、核工業等諸多領域。隨著半導體業的蓬勃發展,氣體也為半導體製造不可或缺的流體原料,為半導體製程的重大考量因素。目前供應半導體製程所需氣體均採用全檢方式檢驗氣體中不純物含量。本研究運用決策樹工具搭配貝氏理論作為本研究之研究工具,以最佳經濟成本為考量,在液態氣體產品出貨之前,選擇最好的抽樣計劃,採用貝氏決策方法使得期望損失成本為最小。

研究中以液態氣體產品C2F6 產品為個案,研究方法以決策樹為工具,建立問題決策模式,並以決策樹理論推導出整體決策目標函數,再以C++語言撰寫模式程式。研究結果考量的因素包括:批量大小、單位檢驗成本、賠償損失成本與檢驗誤差等,即可得到一個最佳抽樣數及臨界判斷值之最小期望損失成本為目標之最佳經濟型抽樣檢驗計劃,以此方式將實際之檢驗數據透過自行撰寫之程式來計算最小期望成本,並以此例對模式之相關參數(檢驗成本、期望良率、檢驗誤差、損失成本)做敏感度分析。

本研究搜集個案公司四年資料,使用前三年資料估計模式之驗前分配與檢驗誤差等參數,再與第四年資料做驗証比較。由於現實因素部份資料難以取得,本研究另發展驗証模式以估算所提經濟檢驗模式與實計狀況所產生的成本之差異,結果皆顯示所提模式適用案例公司,能獲得較小期望成本。
Gas products are an important basic material in modern industry and have been widely applied to many industries such as chemical, petrochemical, mechanical, electronic, semi-conductor, nuclear etc. This research studies a quality-and-loss relation problem arising in a semi-conductor manufacturing environment. The upstream supplier provides C2F6 to its downstream manufacturer for thin film and etching. The study develops a Bayesian rectifying inspection sampling model with the objective of minimizing the expected total cost from the producer’s point of view. The factors that influence the total cost include item quality, inspection cost, sampling information, product failure cost, and decision on the remaining items after the sampling. In addition, it is assumed that the inspection is imperfect; that is, both type I and type II errors may occur. A decision tree is used to represent the cost model, and derive the total objective function. The optimal solution is solved by backward dynamic programming.
The application of the model is presented through a real world case. To validate the model, we collect four years of data and apply the first three years of data to estimate four model parameters. The estimated parameters are then used to calculate the expected benefit by comparing the results of the fourth year. Additionally, sensitivity analysis is performed to provide reference for the decision-maker regarding the effects of respective model parameters in the problem.
中文提要 ------------------------------------------------------ i英文提要 ----------------------------------------------------- ii
誌謝 -------------------------------------------------------- iii
目錄 --------------------------------------------------------- iv
表目錄 ------------------------------------------------------ vii
圖目錄 ----------------------------------------------------- viii
第 一 章 緒論 ------------------------------------------------- 1
1.1 研究背景 ---------------------------------------------- 1
1.2 研究動機 ---------------------------------------------- 2
1.3 研究目的 ---------------------------------------------- 3
1.4 研究架構與流程 ---------------------------------------- 4
第 二 章 文獻探討 --------------------------------------------- 6
2.1 背景及過程 -------------------------------------------- 6
2.2 決策樹 ------------------------------------------------ 6
2.2.1 決策樹概念 --------------------------------------- 6
2.2.2 決策樹範例 --------------------------------------- 8
2.2.3 效用函數 ---------------------------------------- 10
2.2.4 敏感度分析 -------------------------------------- 12
2.2.5 決策樹國外相關文獻 ------------------------------ 13
2.3 抽樣檢驗 --------------------------------------------- 17
2.3.1 計量值與計數值抽樣檢驗 -------------------------- 18
2.3.1.1 計量值抽樣檢驗 --------------------------- 18
2.3.1.2 計數值抽樣檢驗 --------------------------- 18
2.3.1.3 計量值與計數值抽樣檢驗方式之比較 --------- 19
2.3.2 抽樣檢驗方法 ------------------------------------ 20
2.3.3 抽樣檢驗國外相關文獻 ---------------------------- 21
2.3.4 抽樣檢驗國內相關文獻 ---------------------------- 23
2.4 貝氏方法 --------------------------------------------- 25
2.4.1 貝氏理論 ---------------------------------------- 28
2.4.2 Beta 分配 --------------------------------------- 29
2.4.3 貝氏決策準則 ------------------------------------ 30
2.4.3.1 貝氏決策準則敏感度分析 ------------------- 31
2.4.4 貝氏決策國外相關文獻 ---------------------------- 31
2.4.5 貝氏決策國內相關文獻 ---------------------------- 33
第 三 章 問題描述 -------------------------------------------- 37
3.1 問題描述 --------------------------------------------- 37
3.2 C2F6產品概述 ------------------------------------------ 38
第 四 章 研究方法 -------------------------------------------- 44
4.1 變數符號說明 ----------------------------------------- 44
4.2 檢驗誤差 --------------------------------------------- 45
4.3 模式假設條件 ----------------------------------------- 46
4.4 決策樹模式 ------------------------------------------- 46
4.5 模式解算 --------------------------------------------- 52
第 五 章 實驗結果分析 ---------------------------------------- 53
5.1 實驗所使用之電腦設備及軟體 --------------------------- 53
5.2 案例公司之實例說明 ----------------------------------- 53
5.2.1 檢驗成本 -------------------------------------- 54
5.2.2 半導體損失成本 -------------------------------- 55
5.2.3 重新填充成本 ---------------------------------- 56
5.3 個案模式參數敏感度分析 ------------------------------- 63
5.3.1 檢驗成本敏感度分析 ---------------------------- 63
5.3.2 期望良率敏感度分析 ---------------------------- 65
5.3.3 檢驗誤差敏感度分析 ---------------------------- 70
5.3.4 損失成本敏感度分析 ---------------------------- 75
5.4 模式與個案結果比較 ----------------------------------- 80
5.4.1 公司現行方案之成本 ---------------------------- 81
5.4.2 經濟型檢驗模式 -------------------------------- 82
5.4.3 驗證模式-應用資料(N,y)之比較方法 -------------- 82
5.4.4 總結 ------------------------------------------ 85
第 六 章 結論與建議 ------------------------------------------ 87
6.1 結論 ------------------------------------------------- 87
6.2 後續研究方向與建議 ----------------------------------- 88
參考文獻 ----------------------------------------------------- 90
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