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系統識別號 U0026-2412201903462500
論文名稱(中文) 圖案化藍寶石基板製程結果之預測
論文名稱(英文) Forecasting Outputs of the Patterned Sapphire Substrate Processes
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 108
學期 1
出版年 108
研究生(中文) 李世堅
研究生(英文) Shih-Chien Lee
學號 R37041163
學位類別 碩士
語文別 中文
論文頁數 60頁
口試委員 指導教授-王泰裕
口試委員-陳梁軒
口試委員-謝中奇
中文關鍵字 發光二極體  圖案化藍寶石基板  類神經網路  支援向量機  複迴歸分析 
英文關鍵字 Light Emitting Diode  Patterned Sapphire Substrate  Artificial Neural Network  Support Vector Machine  Multiple Regression Analysis 
學科別分類
中文摘要 發光二極體(Light Emitting Diode)取代原有照明設備的基本功能已不能滿足人類對發光二極體的期望;發光二極體產品因人類對終端電子產品的需求,於於2012年至2016年有爆炸性的需求量,因此圖案化藍寶石基板(Pattern Sapphire Substrate)製程導入,也改變了發光二極體的原有製程。現有圖案化藍寶石基板生產工廠如何依客戶不同的規格需求,準確、快速的調整製程參數,讓測試的成本與時間降至最低,便能有效地降低生產成本與提升企業的毛利。但在圖案化藍寶石基板製程,加上製程固有的變異,傳統的調整方式是透過製程工程師個人的經驗進行調整,且不同製程工程師的從業年資及經驗,往往會有參數調整次數的不同,若調整方向錯誤,則造成不必要的成本的損失。本研究利用類神經網路(Artificial Neural Network)、支援向量機(Support Vector Machine)及複迴歸分析(Multiple Regression Analysis)發展一套圖案化藍寶石基板製程結果的預測模型,並以某圖案化藍寶石基板代工廠之生產資料比較個模型預測結果之差異。經由本研究實證結果,於圖案化藍寶石基板製程結果的預測模型,類神經網路、支援向量機、複迴歸分析,皆有良好的預測效果。因此若有製程調整需求或是新產品開發,製程工程師皆可透過預測模型的結果,做為實際參數調整的參考方案。
英文摘要 The invention of Light Emitting Diode (LED) to replace the original lighting equipment is the original goal of human beings. However, from 2012 to 2016, electronic terminal products have become the most important demand of the LED industry. The update rate of electronic terminal products is once a year, so cost and time become the key.
Patterned Sapphire Substrate (PSS) process can directly increase brightness by 10% to 25% and has changed the original LED manufacturing process. PSS helps accurately and quickly adjust the process parameters according to the specifications of the final products. This allows for the reduction of the testing time and the production cost. However, there is an inherent trial and error phenomenon in the adjustment method. Even though, the traditional adjustment is supplemented by the personal experience of the process engineer, years of experience is required for accurate production results. And different process engineers often have different suggestions for the parameter adjustments.
This thesis implements three predictive models for the PSS results using Artificial Neural Network (ANN), Support Vector Machine (SVM), and Multiple Regression Analysis (MRA). The real production data of a PSS company is used to demonstrate the predictive performance of the proposed model.
According to the experimental results, ANN, SVM, and MRA all have good prediction capabilities of the PSS results. During process adjustment and new product development, the process engineers can utilize our prediction model for process parameter tuning.
論文目次 摘要…………………………………………………………………………………….……i
英文摘要…………………………………………………………………………….…...…ii目錄…………………………………………………………………………...…………….x
表目錄………………………………………………………...………………….…….….xii
圖目錄………………………………………………………………...……...……….......xiii
第一章 緒論……………………………………..…………………………………………1
第一節 研究動機……………………………………………………………………..1
第二節 研究目的……………………………………………………………………..2
第三節 研究範圍及限制……………………………………………………………..3
第四節 論文架構……………………………………………………………………..4
第二章 文獻探討…………………………………………………………………………..7
第一節 圖案化藍寶石基板製程介紹………………………………………………..7
第二節 預測方法……………………………………………………………………10
第三節 類神經網路…………………………………………………………………12
第四節 支援向量機…………………………………………………………………16
第五節 複迴歸分析…………………………………………………………………24
第六節 小結…………………………………………………………………………26
第三章 以預測方法建構圖案化藍寶石基板成品規格預測..…………………………..27
第一節 問題描述……………………………………………………………………27
第二節 建構預測模型………………………………………………………………31
第三節 預測評估指標………………………………………………………………38
第四節 小結…………………………………………………………………………39
第四章 預測圖案化藍寶石基板成品規格模型建構與分析..…………………………..40
第一節 個案公司簡介與瓶頸……………………………………………………..40
第二節 預測模型建構的研究過程………………………………………………..44
第三節 研究結果分析……………………………………………………………..49
第四節 小結………………………………………………………………………..54
第五章 結論與建議………………………………………………………………………55
第一節 研究結論…………………………………………………………………..55
第二節 對管理的啟發與意涵……………………………………………………..56
第三節 未來研究建議……………………………………………………………..56
參考文獻…………………………………………………………………………………..58





表目錄
表2-1類神經網路於製程控制或研究的相關文獻…………….………………….15
表2-2 常用核心函數……………………………………………………………….23
表2-3 SVM於製程控制或研究的相關文獻…………………..…………………..23
表2-4 複迴歸分析於製程控制或研究的相關文獻……………..…..…..………...26
表3-1 輸入變數與輸出變數……………………………………………………….31
表4-1 類神經網路模型結果……………………………………………………….47
表4-2 支援向量機預測模型結果………………………………………………….48
表4-3複迴歸分析預測模型結果…………………………………………………..49














圖目錄
圖1-1 研究範圍……………………………………………………...………………4
圖1-2 論文架構圖……………………………………………………………...........6
圖2-1圖案化藍寶石基板之應用原理………………………………………………7
圖2-2 圖案化圖型……………………………...……………………………………8
圖2-3 圖案化藍寶石基板製程………...………………………………..…………..9
圖2-4 光阻圖型製作…………………………………...…………………………..10
圖2-5 預測方法說明……………………………………...………………………..12
圖2-6 類神經網路架構…………………………………………………………….14
圖2-7支援量量機模型分類示意圖………………………………………………..18
圖2-8 支援量量機模型分類結果圖…………………………..……………...........20
圖2-9 支援量量機模型非線性分類結果圖………...……………………………..22
圖3-1 黃光製程與關鍵參數…………………………...…………………………..28
圖3-2 白光製程與關鍵參數……………………………………...………………..29
圖3-3 資料收集示意圖………………………………...…………………………..30
圖3-4 研究模型建構步驟…………………………………...……………………..32
圖3-5 本研究類神經網路模型建構之步驟……………………...………………..34
圖3-6 本研究類神經網路架構圖………………………………...………………..35
圖4-1 個案公司2016年年度成本比例……………………………………...……41
圖4-2 個案公司個案研發成本…………………………………...………………..42
圖4-3 個案公司對LED亮度提升的研究……………………………………...…43
圖4-4 不同的高寬比將增加光的反射面積與出光角度………...………………..43
圖4-5 預測模型的製程資料………………………..…………...…………………45
圖4-6 製程成品高度類神經網路預測模型示意圖……………………………….46
圖4-7 製程成品寬度類神經網路預測模型示意圖……………………………….46
圖4-8 Python軟件BPN結果…………………………………….………………..47
圖4-9 Python軟件SVM結果………………………………….………………….48
圖4-10 Python軟件MLR結果……………………………….…………….……..49
圖4-11 各預測模型MSE值……………………………………………………….50
圖4-12 各預測模型MAE值…………………………….………………………...51
圖4-13 各預測模型CC值………………………………….……………………...51
圖4-14預測模型導入個案公司預估研發成本…………………….……….……..53
圖4-15 導入預測方法預估效益分析……………………………………….……..53
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