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研究生: 張祐賑
YOU-JHEN JHANG
論文名稱: 低溫電漿輔助化學氣相沉積法製備OLED封裝阻障層SiOxNy薄膜參數最佳化之研究
The Preparation of OLED Encapsulation Barrier Layer by Low-Temperature PECVD-A Study on the SiOxNy Film Parameter Optimization
指導教授: 郭中豐
Chung-Feng Jeffrey Kuo
口試委員: 黃昌群
Chang-Chiun Huang
蘇德利
Te-Li Su
學位類別: 碩士
Master
系所名稱: 工程學院 - 自動化及控制研究所
Graduate Institute of Automation and Control
論文出版年: 2012
畢業學年度: 100
語文別: 中文
論文頁數: 121
中文關鍵詞: 田口品質工程灰關聯分析理想解類似度順序偏好法倒傳遞類神經網路薄膜封裝製程軟性顯示器
外文關鍵詞: Thin-Film Encapsulation
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本研究為有機發光二極體(Organic Light-Emitting Diode, OLED)在製程封裝上應用低溫電漿輔助化學氣相沉積法(Plasma-Enhanced Chemical Vapor Deposition, PECVD)來製備SiOxNy之膜材,來防止水氣與氧氣的入侵,達到延長OLED元件的發光壽命。利用高密度的電感式耦合電漿源(Inductively-Coupled Plasma, ICP)於聚醚砜酯(Polyethersulfone, PES)與silicon及玻璃基板鍍上薄膜,進而探討製程參數與1. 鍍膜均勻度2. 鍍膜厚度3. 水氣滲透之間的關聯性,其中研究製程參數包括1. 腔體溫度2. 六甲基二矽氧烷流量(Hexamethyldisiloxane, HMDSO)3. Ar流量4. O2流量5. NH3流量6. 鍍膜位置高度7. 電漿功率8. 腔體壓力。本研究採用單一品質分析田口法中的 直交表來規劃實驗,來達到最少的實驗次數以進行鍍膜製程。實驗結束後進行變異數分析與訊號雜訊比來探討各個控制因子對品質特性的影響程度,並得到單一最佳化結果,再利用理想解類似度順序偏好法與灰關聯分析找出各個最佳水準組合,進而利用倒傳遞類神經結合Levenberg-Marquardt演算法模擬並建構預測系統,最後再利用田口法確認實驗與信心區間計算並驗證實驗結果。本研究在多品質最佳化設計後,單層膜材最佳水氣滲透率達到 ,最大鍍膜厚度可達到420nm,而最快速率可達到21nm/min,高於業界標準規格(10nm/min)110%,且鍍膜均勻度之誤差可達到2.9%。


This study applied low temperature PEVCD (Plasma-Enhanced Chemical Vapor Deposition) in the preparation of SiOxNy film in OLED (Organic Light-Emitting Diode) encapsulation process to prevent the intrusion of water vapor and oxygen to prolong the light-emitting service life of OLED components. The high density ICP (Inductively-Coupled Plasma) was used to deposit films on the PES (Polyethersulfone), silicon and glass substrate. The correlation between the process parameters and the followings were discussed: 1) coating film uniformity; 2) coating film thickness; 3) WVTP. The process parameters under research included: 1) cavity temperature; 2) HMDSO (Hexamethyldisiloxane) flow; 3) Ar flow; 4) O2 flow; 5) NH3 flow; 6) coating film position height; 7) electro-plasma power; and 8) cavity pressure.This study used the orthogonal array of the Taguchi method of the single quality analysis to plan the experiments and conduct the coating process with the least experimental times. After the experiments, the variance analysis was conducted, and the S/N ratio was used to discuss the impact of various control factors on quality characteristics, in order to obtain the single optimization result. Next, using the ideal solution similarity degree order of preferenceapproach and grey relational analysis, this study determined the optimum level combinations before simulating and establishing the prediction system using the back propagation neural network and the Levenberg-Marquardt algorithm. Finally, the Taguchi method confirmation experiments and confidence level were used to calculate and verify the experimental results.After the multi-quality optimization design, the optimum WVTR (Water Vapor Transmission Rate) of the single layer film can be 0.02g/m2/day. The maximum coating film thickness could be up to 420nm, and the fasted speed could be up to 21nm/min, being higher than the industrial standard specifications by 110%. Meanwhile, the coating film uniformity error can be 2.9%.

目錄 中文摘要 I 英文摘要 III 誌謝 V 圖目錄 X 表目錄 XII 第1章 緒論 1 1.1 研究動機與目的 2 1.2 文獻回顧探討 4 1.3 論文架構 8 1.4 實驗流程圖 10 第2章 軟性顯示器相關封裝製程介紹 11 2.1 軟性顯示器 11 2.2 軟性顯示器之基板 13 2.3 AMOLED與PMOLED 14 2.4 OLED相關封裝技術 14 2.5 鍍膜實驗所用之電漿源-電感式耦合電漿(ICP) 16 第3章 單一品質與多品質分析方法 18 3.1 田口品質工程 18 3.1.1 田口品質工程概述 19 3.1.2 規劃實驗與參數設計 20 3.1.3 田口品質工程實驗步驟 22 3.1.4 田口品質工程之直交表介紹 23 3.1.5 訊號雜訊比(S/N比) 27 3.1.6 因子反應分析與主效果分析 28 3.1.7 變異數分析(Analysis of Variance, ANOVA) 29 3.1.8 確認實驗 32 3.1.9 交互作用 33 3.1.10 田口法不足之處 34 3.2 灰色系統理論 36 3.2.1 灰關聯生成(Grey Generating) 36 3.2.2 灰關聯分析(Grey Relational Analysis) 37 3.2.3 屬性權重之決定─熵測度 39 3.3 理想解類似度順序偏好法(TOPSIS) 40 3.4 倒傳遞類神經網路(Back Propagation Network, BPN) 42 3.4.1 倒傳遞類神經網路架構 43 3.4.2 倒傳遞類神經網路演算法則 44 3.4.3 執行倒傳遞類神經網路相關之設定 47 3.4.4 倒傳遞類神經網路演算流程 48 第4章 實驗方法與實驗設備 50 4.1 實驗規劃與流程 50 4.2 實驗設備與量測設備 54 4.3 實驗量測分析儀器 65 第5章 實驗結果與討論 68 5.1 鍍膜實驗配置 68 5.2 鍍膜實驗數據與分析 69 5.2.1 鍍膜厚度數據 69 5.2.2 鍍膜均勻度數據 74 5.2.3 水氣滲透率數據 78 5.2.4 灰關聯度分析 82 5.2.5 理想解類似度順序偏好法(TOPSIS)分析 88 5.3 倒傳遞類神經網路預測系統 94 5.3.1 數據正規化 95 5.3.2 建立預測系統 97 5.3.3 檢驗預測效果 99 第6章 結論與未來展望 101 6.1 結論 101 6.2 未來與展望 102 參考文獻 103

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