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研究生:陳俊傑
研究生(外文):Jun-Jei Chen
論文名稱:應用類神經網路與田口基因演算法於表面聲波器黃光製程最佳化-以表面聲波濾波器為例
論文名稱(外文):Applying Neural Network and Taguchi Genetic Algorithm to Optimization of Photolithography Process of SAW Device-A Case Study on SAW Filter
指導教授:張耀仁張耀仁引用關係
指導教授(外文):Yaw-Jen Chang
學位類別:碩士
校院名稱:中原大學
系所名稱:機械工程研究所
學門:工程學門
學類:機械工程學類
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:68
中文關鍵詞:類神經網路與田口基因演算法
外文關鍵詞:Applying Neural Network and Taguchi Genetic Algorithm
相關次數:
  • 被引用被引用:2
  • 點閱點閱:171
  • 評分評分:
  • 下載下載:0
  • 收藏至我的研究室書目清單書目收藏:3
摘要

表面聲波元件(Surface Acoustic Wave Device,簡稱SAW Device)主要為在一壓電性材料上,經由黃光製程控制指叉電極(Inter-Digital Transducer,IDT)粗細 ,另外在鍍膜製程控制金屬薄膜的均勻性與厚度,經由此兩者來得到適當的頻率。在半導體製程中,表面聲波元件在製程的複雜性算是較為不繁雜的,但頻率的控制與集中性卻是頻率元件發展最難控制的一環,如何控制製程中的各項參數以取得最佳配方,使頻率的標準差(Sigma)值最小化,但如何在數道製程與不同機台中找到最佳配方,減少製程工程師繁複的實驗與次數,並精確地獲得最佳配方,改善產品良率,是我們所努力與嚮往的目標。本研究使用田口法(Taguchi Method)設計實驗,可控制因子為(1)光阻塗佈轉速(2)光阻烘烤溫度(3)光阻烘烤時間(4)曝光時間(5)曝光後烘烤溫度(6)曝光後烘烤時間(7)顯影時間,輸出為頻率的Sigma。使用田口法設計的實驗當作樣本,用來訓練類神經網路的模型,逐漸獲得輸入和輸出(非隨機樣式)彼此之對應關係。並將此模型用於表面聲波器製程,然後結合田口基因演算法取得最佳配方。過去使用類神經研究最佳配方不在少數但橫跨多道製程與不同機台的探討最佳化本論文的重點。
本研究使用徑向基底類神經網路(Radial Basis Function Neural Network,RBFN)作模型建構,並搭配田口基因演算法﹙Taguchi Genetic Algorithms,TGA﹚求取跨機台的最佳配方不但快速且有效,在本研究產品良率有約20%顯著改善,未來不僅可用於表面聲波器其他產品製程的最佳化取得,更可應用此方法於其他製程研究。
Abstract
The fabrication of inter-digital transducer (IDT) on piezoelectric material is the key of the surface acoustic wave (SAW) devices, which includes the photolithography process to control the width of IDT and the sputtering process to dominate the uniformity and thickness of deposited metal. The aim is to obtain the designed frequency. Even though the fabrication process of SAW devices is simpler than that of other semiconductor devices, the control of fabricated SAW frequencies and concentration level is difficult. To achieve a fabrication with low cost and high production yield, process optimization is the solution. In this study, an experimental design based on Taguchi method is used. Controllable parameters in this experiment are spinning speed, soft bake temperature and time, exposure time, hard bake temperature and time, and development time. Neural network is used to establish the process model. Then, the optimal recipe of SAW fabrication process is obtained by Taguchi genetic algorithm. In this study, we obtain the optimal recipe for a serial of process tools in the photolithography process.
By using radial basis function neural network (RBFN) and Taguchi genetic algorithm, an improvement in the fabrication of SAW devices was observed. The yield was raised up to 20%. This method can also be used for different processes.
目錄
中文摘要 ﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒I
Abstract ﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒III
致謝 ﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒IV
目錄 ﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒Ⅴ
圖目錄 ﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒Ⅷ
表目錄 ﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒Ⅹ
第一章 緒論 ﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒1
1-1 前言 ﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒1
1-2 研究動機 ﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒1
1-3 文獻回顧 ﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒3
1-4 論文架構 ﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒5
第二章 表面聲波器製程與原理 ﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒7
2-1 表面聲波器起源與應用 ﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒7
2-2 表面聲波器原理 ﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒8
2-3 表面聲波器製程 ﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒10
2-3.1 晶圓生產流程說明 ﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒11
第三章 製程最佳化應用理論﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒14
3-1 表面聲波器製程參數決定﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒15
3-1.1 Spin coater ﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒15
3-1.2 軟烤﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒17
3-1.3 曝光﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒18
3-1.4 曝後烤﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒18
3-1.5 顯影 ﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒ 18
3-2 直交表﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒20
3-3 田口法簡介﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒22
3-4 類神經網路簡介 ﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒24
3-5 徑向基底網路 ﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒25
3-5.1 RBFN基本架構.﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒26
3-5.2 設定初始參數與終止條件﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒28
3-5.3 輸入學習資料﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒28
3-5.4 計算誤差函數﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒29
3-5.5 RBF參數調整﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒29
3-5.6 RBF學習演算﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒31
3-6 基因演算法(GA) ﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒33
3-6.1 適應函數(fitness function) .﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒34
3-6.2 複製(reproducetion) .﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒34
3-6.3 交配(crossover) ﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒35
3-6.4 突變﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒36
3-6.5 終止條件﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒37
3-6.6 區域最佳解﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒37
3-7 田口基因演算法(TGA) ﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒37
3-7.1 TGA流程﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒39
3-7.2 TGA與SGA不同之處﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒39
第四章 實驗驗證與討論﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒42
4-1 產品輸出值.﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒42
4-2 執行田口實驗.﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒44
4-3 RBFN訓練與驗證模 ﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒47
4-4 演化參數設定與流程﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒48
4-5 輸入與輸出值正規化﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒49
4-6 RBFN與TGA求解最佳配方﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒50
4-7 最佳配方驗證 ﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒51
第五章 結論與未來展望﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒ 54
5-1 結論﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒54
5-2 未來展望﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒55
參考文獻﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒56


圖目錄
圖2.1 表面聲波器發展史 ﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒07
圖2.2 表面聲波器應用 ﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒07
圖2.3 表面聲波器基本架構 ﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒08
圖2.4 表面聲波器電性規格 ﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒09
圖2.5 交叉指狀電極(IDT) ﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒09
圖2.6 晶圓製造流程 ﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒11
圖2.7 晶圓清洗機 ﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒12
圖2.8 Track(上光阻、烘烤、顯影) ﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒12
圖2.9 Stepper曝光機 ﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒13
圖2.10 蒸鍍機 ﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒13
圖3.1 研究流程 ﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒14
圖3.2 膜厚與線寬的頻率相關圖﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒15
圖3.3 線寬SEM圖 ﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒15
圖3.4 光阻厚度與轉速對應圖 ﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒16
圖3.5 顯影速率與軟烤溫度關係圖﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒17
圖3.6 顯影狀況﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒18
圖3.7 光阻塗佈特性要因圖.﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒19
圖3.8 徑向基底函數類神經網路﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒25
圖3.9 徑向基底網路流程﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒26
圖3.10 高斯函數 ﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒30
圖3.11 學習率調整示意圖 ﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒31
圖3.12 輪盤選擇法 ﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒35
圖3.13 突變 ﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒36
圖3.14 傳統基因演算法(SGA)流程 ﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒38
圖3.15 田口基因演算法(TGA)流程 ﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒38
圖4.1 線寬大小 ﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒42
圖4.2 生產線參數頻率標準差水準 ﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒43
圖4.3 E-gun鍍鍋(18片) ﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒45
圖4.4 膜厚分佈圖 ﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒45
圖4.5 均勻性分佈圖 ﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒46
圖4.6 RBFN訓練流程﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒47
圖4.7 TGA演化流程 ﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒49
圖4.8 6000次演化Probing data 1 ﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒51
圖4.9 6000次演化Probing data 2 ﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒52
圖4.10 生產線參數頻率分佈 ﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒53
圖4.11 基因演算法最佳參數頻率分佈 ﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒53
圖5.1 研究方向圖 ﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒55




表目錄
表3.1 光阻均勻性與厚度影響因子 ﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒16
表3.2 田口控制因子與參數水準 ﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒19
表3.3 直交表選擇﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒21
表3.4 表面聲波器製程參數直交表﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒22
表3.5 TGA直交表﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒41
表4.1 產品測試規格 ﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒43
表4.2 膜厚與均勻性分佈 ﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒45
表4.3 晶圓測試輸出結果 ﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒46
表4.4 TGA參數設定﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒48
表4.5 輸入與輸出正規化 ﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒50
表4.6 最佳配方參數 ﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒51
表4.7 最佳參數輸出 ﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒51
表4.8 6000次演化最佳配方驗證 ﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒﹒52
參考文獻
[1] 張斐章、張麗秋、黃浩倫,“類神經網路理論與實務﹐”東華書局,2004.
[2] 杜孟奇,“應用RBF類神經網路於超音波馬達之位置控制﹐”國立中央大學碩士論文,2001.
[3] 賴正穎,“應用智慧型控制器整合靜態同步串聯補償器之動態分析﹐”國立中山大學碩士論文,2008.
[4] 林祖強,“微影製程之疊對控制﹐”中原大學碩士論文,2005
[5] D. J. H. Wilson and G. W. Irwin, “RBF Principal Manifolds for Process Monitoring,”IEEE Transaction on neural Networks, Vol. 10
No. 6, November, 1999.
[6] 莊信源,“類神經模糊系統與遺傳演算法在加工參數最佳化之應用﹐”海洋大學機械與輪機研究所碩士論文,(2001).
[7] 張益瑞,“銅化學機械研磨之製程參數最佳化﹐”中原大學碩士論文,2005.
[8] 楊明三、林居南,“台灣應如何發展SAW Filter﹐”電子與材料雜誌第17期.
[9] H. J.Levinson,“Lithography Process Control﹐”SPIE PRESS Vol. TT28,2001.
[10] 吳復強,“田口品質工程﹐”全威圖書有限公司,2002.
[11] 鄭燕琴,“田口品質工程技術理論與實務﹐”中華民國品質管制學會,1993.
[12] S.-Y. Ho, L.-S. Shu, and H.-M. Chen, “Intelligent genetic algorithm with a new intelligent crossover using orthogonal arrays﹐” in Proc. Genetic and Evolutionary Computation Conference, pp.289-296, 1999.
[13] 蘇木春、張孝德,“機器學習類神經網路、模糊系統以及基因演算法則﹐”全華科技圖書股份有限公司,1997.
[14] J.-T. Tsai and J.-H. Chou, “Tuning the Structure and Parameters of a Neural Network by Using Hybrid Taguchi-Genetic Algorithm﹐”IEEE Transactions on Neural Networks, Vol. 17, No. 1, Januray 2006.
[15] J.-T. Tsai, T.-K. Liu, and J.-H. Chou, “Hybrid Taguchi-Genetic Algorithm for Global Numerical Optimization﹐” IEEE Transactions on Evolutionary Computation, VOL. 8, NO. 4, August 2004.
[16] J.-T. Tsai and J.-H. Chou, “Optimal Design of Digital IIR Filters by Using Hybrid Taguchi Genetic Algorithm﹐”IEEE Transactions On Industrial Eelectronics, Vol. 53, No. 3, June 2006.

[17] Y.-J. Chang and J.-J. Tsai ,“Process Optimization Based on Neural Network Model and Orthogonal Arrays﹐” 2008 IEEE Congress on Evolutionary Computation (CEC 2008).
[18] 莊達人,“VLSI 製造技術﹐”高立圖書有限公司
[19] P. Hudek and D. Beyer, “Exposure optimization in high-resolution e-beam lithography﹐”Microelectronic Engineering 83 P.780–783,2006.
[20]Y.-K. Yang and T.-C. Chang,“Experimental analysis and optimization of a photo resist coating process for photolithography in wafer fabrication﹐”Microelectronics Journal 37 ﹐P.746–751,2006.
[21] P. Hudek, I.-W. Rangelow, I. Kostic, N. MtinzeI and I. Daraktchiev, “Evaluation of Chemically Amplified Deep UV Resist for Mieromachining using E-Beam Lithography and Dry Etching﹐” Microelectronic Engineering 30 ﹐P.309–312,1996.
[22] W. Liu,“Nanoimprint lithography for RF SAW manufacture﹐” IEEE Ultrasonics Symposium﹐P.1303–1306,2005.
[23] M. Rosenblum and L. S. Davis, “An Improved Radial Basis Function Network for Visual Autonomous Road Following﹐”IEEE Transactions on Neural Networks, Vol. I , No. 5, September 1996.
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