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研究生: 陳彥良
Yen-Liang Chen
論文名稱: 整合田口法與粒子群演算法應用於鐵酸鉍摻雜鈮MFIS電容器之最佳化
Optimization of Nb-doped BiFeO3 film in MFIS capacitors using improved PSO integrating Taguchi method
指導教授: 劉傳璽
Liu, Chuan-Hsi
陳珍源
Chen, Jen-Yang
學位類別: 碩士
Master
系所名稱: 機電工程學系
Department of Mechatronic Engineering
論文出版年: 2011
畢業學年度: 99
語文別: 中文
論文頁數: 116
中文關鍵詞: 鐵酸鉍MFIS電容器粒子群演算法田口方法
英文關鍵詞: BiFeO3, MFIS capacitors, particle swarm optimization, Taguchi method
論文種類: 學術論文
相關次數: 點閱:410下載:17
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  • 本研究主要是探討使用整合田口法的改良型粒子群演算法在鐵酸鉍摻雜鈮MFIS電容器最佳化上的應用。本論文可分為兩部分:(一)粒子群演算法整合田口方法(二)鐵酸鉍摻雜鈮MFIS電容器之最佳化。
    粒子群演算法是近年來應用在諸多領域的最佳化技術。全域最佳型(gbest)和區域最佳型(lbest)是粒子群演算法的其中兩種變型,其分別擁有收斂性與探索性的優點。整合田口方法可結合兩者的優勢,使新衍生的變型兼具更好的最佳化效率和更好的精確度。此變型一開始先採全域最佳型快速收斂,接著再採用區域最佳型的探索能力,當最佳化效果不彰時,再使用田口法,自群體中萃取出具有潛力的元素,形成群體學習的對象,間接加強了群體的最佳化能力。實驗結果以t檢定驗證此改良型粒子群演算法的確結合了此兩種傳統方法的各自優點,在15個適應性函數的條件下展現擁有更好的表現。
    我們將此整合田口法的粒子群演算法變型應用在鋁/鐵酸鉍摻雜鈮/二氧化鉿/p型矽MFIS結構之電容器的最佳化上,以期得到最佳的製程配方。鐵電材料因其特殊的鈣鈦礦結構,很適合當作記憶體單元的材料。其中鐵酸鉍因具有高居禮溫度、高尼爾溫度、低結晶溫度和很大的殘留極化值的優點,所以成為一種很具前景的記憶體材料。唯其漏電流太大的缺點仍待改善。藉由摻雜鈮可解決此問題,最終的目標是產生具有最大記憶視窗寬度和最小漏電流密度的電容結構。考量最大記憶視窗寬度與最小漏電流密度的情況,可得最佳化後的配方:鈮摻雜直流濺鍍瓦數15.5watt、氧化層厚度69.2nm、氬氧比17.3、快速熱退火850°C。

    This study is about the application of improved particle swarm optimization (PSO) integrating Taguchi method over Nb-doped BiFeO3 MFIS capacitors. This thesis has two main subjects: (1) The variant of PSO integrating Taguchi method. (2) The optimization of Nb-doped BiFeO3 MFIS capacitors.
    PSO has been a popular optimization technique applied over many fields. The two of PSO variants, gbest and lbest, are reported to have the advantages of exploratory capability and exploitability, respectively. Integrating Taguchi method combines these two advantages for a newly-derived PSO variant with better efficiency and less error. The novel variant proceeds with gbest for fast convergence until the shrinking of the swarm stops. lbest succeeds the following optimization to the occurrence of deadlock. Then, the Taguchi method helps to extract best recipe from the swarm to continue the optimization. The experimental results are analyzed with t-test. The superiority of this variant has been verified under fifteen fitness functions.
    This proven PSO variant is utilized over Al/ Nb-doped BiFeO3/HfO2/p-Si capacitors for fabrication recipe. Ferroelectric materials are suitable for being memory cells with its unique “Perovskite” structure. BiFeO3 is one of the promising substitutes with high Curie temperature, high Neel temperature, low crystallization temperature, and large remnant polarization. But the major issue is its relatively large leakage current. Doping Nb can suppress the leakage. The larger memory window and the less leakage current is the contribution of this study. The optimized recipe is 15.5 W for DC power of Nb sputtering, 69.2 nm for insulator thickness, 17.3 for argon-to-oxyen ratio, and 850°C for RTA temperature.

    總目錄 第一章 緒論 1 1-1 記憶體的種類 1 1-2 鐵電材料在非揮發性記憶體上的應用 1 1-3 粒子群演算法應用在MFIS結構製程最佳化 2 1-4 本論文研究方向 2 第二章 文獻探討 4 2-1 粒子群演算法(Particle Swarm Optimization, PSO) 4 2-1-1 粒子群演算法的最初準則 4 2-1-2 粒子群演算法的變型 6 2-1-3 適應性函數 10 2-2 實驗設計法(Design of experiments) 11 2-2-1 嘗試錯誤法 12 2-2-2 一次一因子法 12 2-2-2-1 常態分布 15 2-2-2-2 訊噪比 17 2-2-3 全因子法 19 2-2-4 田口方法 21 2-3 金氧半場效電晶體(MOSFET) 24 2-3-1 電晶體的結構 24 2-3-2 氧化層與電流的關係 25 2-3-3 high-k材料 28 2-3-4 漏電流機制 29 2-3-4-1 直接穿隧的漏電流機制 30 2-3-4-2 傅勒-諾德翰穿隧的漏電流機制 32 2-3-4-3 蕭基發射的漏電流機制 33 2-3-4-4 普爾-夫倫克爾發射的漏電流機制 36 2-3-5 介面層 38 2-3-6 絕緣層電荷陷阱 39 2-3-6-1 固定氧化層電荷 40 2-3-6-2 移動離子電荷 41 2-3-6-3 介面陷阱電荷 43 2-3-6-4 氧化層陷阱電荷 43 2-3-7 MOSFET電性的量測 44 2-3-8 鐵電材料 49 第三章 實驗設計 52 3-1 粒子群演算法與其參數設定 52 3-1-1 改善粒子群演算法的契機 54 3-1-2整合田口方法以改善粒子群演算法 57 3-2 薄膜沉積置備MFIS結構電容器 66 3-2-1 薄膜沉積原理 66 3-2-2 濺鍍沉積法原理 67 3-2-3 直流濺鍍與射頻濺鍍沉積原理 68 3-2-4 X-ray繞射儀分析 70 3-2-5 MFIS電容器的製備 72 3-2-6 MFIS電容器的製備配方 75 3-2-7 MFIS電容器的電性量測 75 第四章 結果與討論 82 4-1 整合田口方法以改善粒子群演算法 82 4-2薄膜沉積置備MFIS結構電容器 98 第五章 結論與未來展望 110 5-1 整合田口直交表以改善粒子群演算法 110 5-2 Al/BFO(Nb5+)/HfO2/p-Si結構電容器的電性 110 5-3 未來展望 111 參考文獻 112 表目錄 表2-1 包含八個因子的一次一因子實驗 13 表2-2 影響磁磚製程厚度的八因子與其水準表 14 表2-3 影響磁磚製程厚度的八因子一因子實驗結果 14 表2-4 影響磁磚製程厚度的八因子一因子實驗結果 18 表2-5 全因子法對三因子實驗的實驗規劃 19 表2-6 全因子法對三因子實驗結果的訊噪比 20 表2-7 田口方法的L8(27)表 23 表2-8 各種high-k材料 28 表3-1 田口方法的L12(211)直交表 58 表3-2 各方程式的邊界條件與初始設定值 60 表3-3 各方程式的邊界條件與初始設定值 64 表3-4 田口方法的L32(231)直交表 65 表3-5 本實驗嘗試的配方組合 75 表3-6 目前配方、gbest演算法和lbest演算法定義下的最佳配方表 81 表3-7 田口方法的L9(34)直交表 81 表4-1 針對六個適應性函數所做的模擬結果 86 表4-2 gbest、lbest與PSOTaguchi以t檢定所得最終誤差與執行時間比較 96 表4-3 本實驗嘗試的配方組合與其相應記憶視窗寬度、漏電流密度 107 表4-4 記憶視窗寬度最佳化後的十二組配方 108 表4-5 漏電流密度最佳化後的十二組配方 109 圖目錄 圖2-1 粒子移動準則示意圖 5 圖2-2 (a) gbest的拓撲法包含全部粒子群(b) gbest的拓撲法僅包含粒子本身和前後編號者 9 圖2-3 常態分布的曲線 15 圖2-4 三種擁有相同平均值但不同標準差的常態分布曲線 16 圖2-5 n型金氧半場效電晶體的結構 25 圖2-6 四種漏電流機制的能帶圖(a)直接穿隧(b)傅勒-諾德翰穿隧(c)蕭基發射(d)普爾-夫倫克爾發射 30 圖2-7 能帶圖中的導帶補償ΦB(∆EG) 31 圖2-8 各種氧化物的能帳高ΦB(∆EG) 31 圖2-9 HfO2 MOS電容器的電流密度-電場圖 33 圖2-10 ZrO2 MOS電容器在各種溫度下的Schottky圖 35 圖2-11 原位型(in-situ type)電容器的Poole-Frenkel圖 37 圖2-12 原位型(in-situ type)電容器的Poole-Frenkel圖 39 圖2-13 四種主要氧化層電荷及其在氧化層中相對位置 40 圖2-14 表面態電荷密度與氧化溫度、回火環境氣體的關係 41 圖2-15 氧化製程中加入含氯化合物,吸附鹼金屬離子 42 圖2-16 n-MOS電容器與其等效電容值 44 圖2-17 n-MOS 高低頻C-V圖 46 圖2-18 製備Al2O3/TiO2/Al2O3奈米薄膜在p型氮化鎵(GaN) MOS電容器上所得J-V圖 48 圖2-19 鐵電材料BaTiO3的鈣鈦礦結構 49 圖2-20 鐵電材料的典型電滯曲線 50 圖3-1 原始粒子群演算法的流程圖 53 圖3-2 原始粒子群演算法的最佳化歷程圖 54 圖3-3 原始粒子群演算法、gbest和lbest三者的最佳化歷程圖 55 圖3-4 整合田口方法改善粒子群演算法的流程圖 61 圖3-5 物理氣相沉積法(PVD)與化學氣相沉積法(CVD) 67 圖3-6 直流濺鍍系統示意圖 69 圖3-7 射頻濺鍍系統示意圖 70 圖3-8 「布拉格定律」示意圖 70 圖3-9 Nb-doped BFO/ HfO2/p-Si電容器摻雜不同瓦數的鈮所得XRD圖 71 圖3-10 p型矽基板浸泡在BOE水溶液中,以去除其表面的原生氧化層 71 圖3-11 濺鍍沉積一層(分別膜厚20nm、40nm、60nm)HfO2絕緣層在試片上 72 圖3-12 共鍍沉積一層膜厚250nm的鐵電層(BiFeO2+Nb)在試片上 72 圖3-13 試片分別經歷不同溫度的快速熱退火處理 73 圖3-14 沉積一層厚250nm鋁電極在試片上,供電性量測 74 圖3-15 共鍍系統示意圖 74 圖3-16 正偏壓掃描至負偏壓與負偏壓掃描至正偏壓C-V圖 76 圖3-17 將C-V圖所得資料輸入Origin 6.1欄位 77 圖3-18 將±8V區間的C-V圖資料欄位反白並按下圓圈處按鈕 77 圖3-19 ±8V區間的C-V圖 78 圖3-20 放大工具拖曳滑鼠視窗至±8V區間的C-V圖一半高度處 78 圖3-21 游標工具讀出左側圓圈處電壓值 79 圖3-22 游標工具讀出右側圓圈處電壓值 79 圖3-23 游標工具讀出-1伏特處漏電流密度大小(圓圈處) 80 圖4-1 三種演算法針對11因子Sphere function的模擬 82 圖4-2 三種演算法針對11因子Quadric function的模擬 83 圖4-3 三種演算法針對11因子Rosenbrock’s function的模擬 83 圖4-4 三種演算法針對11因子Rastrigin’s function的模擬 84 圖4-5 三種演算法針對11因子Noncontinuous Rastrigin’s function的模擬 84 圖4-6 三種演算法針對11因子Askley’s function的模擬 85 圖4-7 三種演算法針對31因子Sphere function所做模擬 87 圖4-8 三種演算法針對31因子Quadric function所做模擬 88 圖4-9 三種演算法針對31因子Rosenbrock’s function所做模擬 88 圖4-10 三種演算法針對31因子Rastrigin’s function所做模擬 89 圖4-11 三種演算法針對31因子Noncontinuous Rastrigin’s function所做模擬 89 圖4-12 三種演算法針對31因子Askley’s function所做模擬 90 圖4-13 三種演算法針對31因子Weierstrass function所做模擬 90 圖4-14 三種演算法針對31因子Generalized penalized function所做模擬 91 圖4-15 三種演算法針對31因子Generalized penalized function所做模擬 91 圖4-16 三種演算法針對31因子Rotated Rosenbrock’s function所做模擬 92 圖4-17 三種演算法針對31因子Rotated Rastrigin’s function所做模擬 92 圖4-18 三種演算法針對31因子Rotated Noncontinuous Rastrigin’s function所做模擬 93 圖4-19 三種演算法針對31因子Rotated Askley’s function所做模擬 93 圖4-20 三種演算法針對31因子Rotated Weierstrass function所做模擬 94 圖4-21 三種演算法針對31因子Rotated Generalized penalized function所做模擬 94 圖4-22 配方一(直流濺鍍功率5 watt,氧化層厚度40 nm,氬氧比10,快速熱退火600°C )所得C-V圖(所得記憶視窗寬度為0V) 98 圖4-23 配方二(直流濺鍍功率10 watt,氧化層厚度40 nm,氬氧比10,快速熱退火600°C )所得C-V圖(所得記憶視窗寬度為2.02V) 99 圖4-24 配方三(直流濺鍍功率15 watt,氧化層厚度40 nm,氬氧比10,快速熱退火600°C )所得C-V圖(所得記憶視窗寬度為2.97V) 99 圖4-25 配方四(直流濺鍍功率5 watt,氧化層厚度20 nm,氬氧比10,快速熱退火600°C )所得C-V圖(所得記憶視窗寬度為0.44V) 100 圖4-26 配方五(直流濺鍍功率5 watt,氧化層厚度40 nm,氬氧比10,快速熱退火600°C )所得C-V圖(所得記憶視窗寬度為2.13V) 100 圖4-27 配方六(直流濺鍍功率5 watt,氧化層厚度60 nm,氬氧比10,快速熱退火600°C )所得C-V圖(所得記憶視窗寬度為2.93V) 101 圖4-28 配方七(直流濺鍍功率10 watt,氧化層厚度40 nm,氬氧比5,快速熱退火600°C )所得C-V圖(所得記憶視窗寬度為2.07V) 101 圖4-29 配方八(直流濺鍍功率10 watt,氧化層厚度40 nm,氬氧比10,快速熱退火600°C )所得C-V圖(所得記憶視窗寬度為2.02V) 102 圖4-30 配方九(直流濺鍍功率10 watt,氧化層厚度40 nm,氬氧比15,快速熱退火600°C )所得C-V圖(所得記憶視窗寬度為0.58V) 102 圖4-31 配方十(直流濺鍍功率10 watt,氧化層厚度40 nm,氬氧比10,快速熱退火500°C )所得C-V圖(所得記憶視窗寬度為0.87V) 103 圖4-32 配方十一(直流濺鍍功率10 watt,氧化層厚度40 nm,氬氧比10,快速熱退火600°C )所得C-V圖(所得記憶視窗寬度為0V) 103 圖4-33 配方十二(直流濺鍍功率10 watt,氧化層厚度40 nm,氬氧比10,快速熱退火700°C )所得C-V圖(所得記憶視窗寬度為2.03V) 104 圖4-34 配方一、二、三(直流濺鍍功率5、10、15 watt,氧化層厚度40 nm,氬氧比10,快速熱退火600°C )所得I-V圖 105 圖4-35 配方四、五、六(直流濺鍍功率5 watt,氧化層厚度20、40、60 nm,氬氧比10,快速熱退火600°C )所得I-V圖 105 圖4-36 配方七、八、九(直流濺鍍功率10 watt,氧化層厚度40 nm,氬氧比5、10、15,快速熱退火600°C )所得I-V圖 106 圖4-37 配方十、十一、十二(直流濺鍍功率10 watt,氧化層厚度60 nm,氬氧比10,快速熱退火500、600、700°C )所得I-V圖 106

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