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系統識別號 U0002-0808201123200800
中文論文名稱 灰色理論與類神經網路在晶片溫度分佈之研究
英文論文名稱 Study of Grey Theory and Artificial Neural Network applied in the Temperature Distribution of IC Chip
校院名稱 淡江大學
系所名稱(中) 機械與機電工程學系博士班
系所名稱(英) Department of Mechanical and Electro-Mechanical Engineering
學年度 99
學期 2
出版年 100
研究生中文姓名 陳尚彥
研究生英文姓名 Shang-Yan Chen
電子信箱 897370044@s97.tku.edu.tw
學號 897370044
學位類別 博士
語文別 中文
口試日期 2011-07-14
論文頁數 176頁
口試委員 指導教授-楊智旭
共同指導教授-張士行
委員-陳炤彰
委員-謝宏榮
委員-李為民
委員-史建中
委員-葉豐輝
委員-蔡慧駿
委員-楊智旭
中文關鍵字 灰色理論  類神經網路  熱設計  電子散熱 
英文關鍵字 Grey theory  Neural Networks  Thermal design  Electronic Cooling 
學科別分類
中文摘要 本論文有兩個主要研究目標,一是發展出人工神經網路進行多熱源晶片封裝之溫度分佈研究,另一目標是應用灰色理論在多熱源晶片封裝溫度之最高溫度、平均溫度的預測。
研究結果得知,比較倒傳遞神經網路(Back-Propagation Neural Network, BPN)與Icepak套裝軟體兩種方法顯示,BPN準確度達97﹪,由此顯示應用BPN在多熱源晶片封裝之溫度分佈預測上,BPN是可行、可用的。
灰色建模運算只需利用少量數據(至少4筆資料)做預測使得計算效率加快。在本研究中,經由Icepak的分析數據進行灰預測建模,經由殘差分析與後驗差檢驗來驗證灰預測模型的準確性,結果顯示本研究所建立的灰預測模型屬於“好”(Good)的等級,這意味著本研究所建立的灰預測模型是精確與準確的。
本研究結果的主要貢獻在於依此方法在晶片封裝的熱設計與分析上提供設計者更為快速簡便的方法。
英文摘要 An objective of this thesis is to develop an ANN model to obtain the temperature distribution of a IC chip in the packaging processes. Another objective is to predict the highest temperature and mean temperature of it by using the Grey system theory. The calculated results of BPN(Back-Propagation Neural Network) are compared with the simulated results of the software package(Icepack). The accuracy of the results between these two methods is 97%. It shows that the BPN technique is useful to predict the temperature distribution of the IC packaging.
The advantage of the Grey prediction model is the higher calculating efficiency for a few original data (at least 4 data in the series). In the research, the highest and mean temperatures of eight chips from Icepack simulation are set to be the original data in the GM(1,1) model separately. Then, the predicted results are applied to make the post residual error analysis. The analyzed results show that this Grey prediction model is belong to the “Good” grade. It means this developed GM(1,1) model is precise and accurate.
The contribution of this research results can be applied to the thermal analysis and design for an IC chip packaging processes.
論文目次 目 錄
淡江大學論文提要i
英文提要ii
目 錄iii
圖 目 錄vi
表 目 錄ix
第一章 緒論1
1-1研究背景8
1-2研究動機與目的16
1-3研究架構17
第二章文獻回顧19
2-1 電子構裝19
2-2 PBGA簡介20
2-3 多晶片模組的優點23
2-4 PBGA熱傳效能規劃與散熱考量25
2-5 PBGA溫度循環效能的可靠性考量27
2-6 數值方法的應用28
2-7 IC散熱需求29
第三章 熱傳導影響分析30
3-1 熱傳導對最高溫度的影響34
3-2 熱傳導分析的方程式37
3-3 有限差分法38
3-4 拉式方程式的有限差分方程式40
3-5 邊界條件及其有限差分方程式40
3-5-1 熱對流時的邊界條件41
3-5-2 熱絕緣的邊界條件43
3-5-3 二個邊界的交界邊45
3-5-4 三個邊界的交界點49
3-6 IC熱傳速率Q的計算52
第四章 相關理論與研究方法54
4-1 計算流體力學(CFD)54
4-2 類神經網路(ANN)55
4-3 倒傳遞神經網路(BPN)64
4-4 灰色系統67
4-4-1 灰色系統建模理論68
4-4-2 灰色模型的建立70
4-4-3 MATLAB77
第五章 BPN實驗78
5-1倒傳遞神經網路實驗78
5-1-1 分析實驗流程78
5-1-2建立分析模型79
5-1-3建立熱源座標位置80
5-1-4 PBGA模型分析及溫度監控取樣82
5-2 BPN訓練與測試網路架構89
5-2-1 BPN數學模型建立90
5-2-2 BPN訓練過程93
5-2-3 BPN訓練結果95
5-3 BPN測試實驗96
5-3-1 BPN測試過程99
5-3-2 BPN測試結果101
5-4 BPN與Icepak數據比較104
第六章 Grey實驗120
6-1 建立分析模型122
6-2 GM(1,1)的原始序列124
6-3灰預測建模GM(1,1)125
6-4 Matlab程式撰寫133
6-5 GM(1,1)模型參數134
6-6 殘差分析135
6-7 後驗差檢驗138
6-7-1 高溫組後驗差檢驗141
6-7-2 均溫組後驗差檢驗143
6-8 實驗結果145
第七章 結論146
7-1結論146
7-2討論147
參考文獻148
附錄A訓練組計算流體力學分析資料157
附錄B 訓練組倒傳遞神經網路預測資料167

圖目錄
圖1-1電子元件損壞主因示意圖2
圖1-2藉由被動式散熱進行熱管理改善的電子產品3
圖1-3電子散熱的熱阻示意圖6
圖1-4不同應用條件的元件熱阻比重7
圖1-5半導體積體電路封裝結構9
圖1-6本研究流程示意圖18
圖2-1基板上的對準符號23
圖2-2有導線通道的PBGA27
圖3-1一維體積元素32
圖3-2二維之熱流動34
圖3-3三維模擬之熱源配置35
圖3-4水平擺置下溫度分佈36
圖3-5垂直擺置下溫度分佈36
圖3-6穩定狀態下三個方向熱傳導38
圖3-7節點在x方向的熱傳導邊界上41
圖3-8節點在x方向的熱絕緣邊界上43
圖3-9節點在兩個邊界的交界邊上45
圖3-10節點在三個邊界的交界點上50
圖4-1 BPN架構66
圖5-1計算流體力學分析模型80
圖5-2定義晶片內熱源位置起點81
圖5-3第1組訓練組晶片內熱源位置座標82
圖5-4分析模型熱傳遞係數83
圖5-5第1組晶片溫度分佈示意圖84
圖5-6晶片表面溫度監控規劃圖85
圖5-7取樣溫度點座標定義圖86
圖5-8 BPN架構圖92
圖5-9 BPN架構6輸入64輸出圖92
圖5-10 BPN訓練流程94
圖5-11訓練組溫度收斂曲線圖95
圖5-12訓練組平均溫度比較圖96
圖5-13 BPN測試流程100
圖5-14 測試組平均溫度103
圖5-15 訓練組100組最高溫度比較圖104
圖5-16 訓練組第1組BPN及Icepak溫度比較圖105
圖5-17 訓練組第2組BPN及Icepak溫度比較圖106
圖5-18 訓練組第3組BPN及Icepak溫度比較圖106
圖5-19 訓練組第4組BPN及Icepak溫度比較圖107
圖5-20 訓練組第5組BPN及Icepak溫度比較圖107
圖5-21 訓練組第6組BPN及Icepak溫度比較圖108
圖5-22 訓練組第7組BPN及Icepak溫度比較圖108
圖5-23 訓練組第8組BPN及Icepak溫度比較圖109
圖5-24 訓練組第9組BPN及Icepak溫度比較圖109
圖5-25 訓練組第10組BPN及Icepak溫度比較圖110
圖5-26 測試組第16組最高溫度比較圖111
圖5-27 測試組第1組BPN及Icepak溫度比較圖112
圖5-28 測試組第5組BPN及Icepak溫度比較圖112
圖5-29 訓練組第1組3D誤差百分比分佈圖113
圖5-30 a BPN訓練組第1組3D溫度分佈圖114
圖5-30 b Icepak訓練組第1組3D溫度分佈圖114
圖5-31 訓練組第2組BPN及Icepak3D溫度分佈圖114
圖5-32 訓練組第3組BPN及Icepak3D溫度分佈圖114
圖5-33 訓練組第4組BPN及Icepak3D溫度分佈圖115
圖5-34 訓練組第5組BPN及Icepak3D溫度分佈圖115
圖5-35 訓練組第6組BPN及Icepak3D溫度分佈圖115
圖5-36 訓練組第7組BPN及Icepak3D溫度分佈圖116
圖5-37 訓練組第8組BPN及Icepak3D溫度分佈圖116
圖5-38 訓練組第9組BPN及Icepak3D溫度分佈圖116
圖5-39 訓練組第10組BPN及Icepak3D溫度分佈圖117
圖5-40 a BPN測試組第1組3D溫度分佈圖117
圖5-40 b Icepak測試組第1組3D溫度分佈圖117
圖5-41測試組第 5組BPN及Icepak3D溫度分佈圖117
圖5-42測試組第13組BPN及Icepak3D溫度分佈圖118
圖5-43測試組第15組BPN及Icepak3D溫度分佈圖118
圖 6-1 CFD分析模型120
圖 6-2 系統中的6個座標輸入121
圖 6-3 灰預測實驗架構圖122
圖 6-4 晶片表面溫度監控規劃圖125
圖 6-5 灰建模與灰預測架構圖129
圖 6-6 高溫組溫度折線圖136
圖 6-7 均溫組溫度折線圖137

表目錄
表1-1表面實裝式(SMT)14
表1-2引腳插入式(PTH)15
表5-1第1組熱源座標資料82
表5-2封裝晶片表面溫度分隔監控區86
表5-3第1組座標與溫度對應表87
表5-4訓練組座標資料及封裝晶片表面溫度資料88
表5-5學習網路設定89
表5-6測試網路設定90
表5-7測試組第1組座標資料97
表5-8測試組座標資料及晶片表面溫度資料98
表5-9 BPN測試組溫度資料102
表5-10溫度誤差比較表119
表6-1封裝晶片表面16點溫度值131
表6-2封裝晶片表面16點高溫組溫度值132
表6-3封裝晶片表面16點均溫組溫度值132
表6-4高溫組灰預測溫度值與殘差分析136
表6-5均溫組灰預測溫度值與殘差分析137
表6-6 預測精度等級表140
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