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研究生：	施宏達 Shin, Hung-Ta
論文名稱：	使用擴散小波神經網路學習短期時間序列資料 -以晶圓級封裝之試產問題為例 Using Diffusion Wavelet Neural Network for Learning Short-Term Time Series Data: A Case Study of the Pilot Run Data in Wafer Level Chip Scale Package Process
指導教授：	利德江 Li, Der-Chiang
學位類別：	碩士 Master
系所名稱：	管理學院 - 工業與資訊管理學系碩士在職專班 Department of Industrial and Information Management (on the job class)
論文出版年：	2012
畢業學年度：	100
語文別：	中文
論文頁數：	55
中文關鍵詞：	短期時間序列資料、虛擬樣本、小波轉換、倒傳遞類神經網路、灰預測
外文關鍵詞：	short-term time series data, virtual samples, wavelet transform, back-propagation neural network, grey prediction
相關次數：	點閱：116 下載：1
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晶圓級晶片封裝的過程中，具有高效能、高功率與高密度等優點，它可以確保印刷電路板一致性裝配有必要實現高產量和高可靠性。由於電子產品輕、薄、短、小的便攜式趨勢，WLCSP已成為未來發展重點。雖此封裝技術使電子訊號輸入/輸出密度得以提升，然而初期導入階段之良率偏低，而影響封裝過程之因素良多，是以多層金屬薄膜上之錫球高度具有決定性之影響。由於新產品開發初期之試製產品樣本稀少，於統計製程管制圖中能提供予之資訊有限，故本研究基於時間分割的概念，應用由Li等人於2011年所提出的相依虛擬樣本產生法，於各分割時間軸產生對應之虛擬樣本，藉以改善小波神經網路(WNN)對短期時間序列資料的預測績效，並稱之為擴散小波神經網路(DWNN)。DWNN確實能有效比灰預測GM(1,1)與倒傳遞類神經網路更為精準。

The process of wafer level chip scale package (WLCSP) has advantages of high efficiency, high power and high density and it ensures consistent printed circuit board assembly necessary to achieve high yield and reliability. Due to the tendency of electronic appliances is with the light, thin, short, and small portable trend, WLCSP becomes the focus of future development. Though the package technology enhances electronic signal input/output density, low yield often appears in the early stage of introduction. Quite a few manufacturing factors influence the package process, and the height of solder ball on multilayer metallic film is the decisive one. Due to the sparse samples of pilot run in the early stage of new product development, the information that statistical process control charts provide is limited. This study is on the basis of timeline division and proposes the diffusion wavelet neural network which uses correlated virtual sample generating method proposed by Li et al. (2011) to improve the predictive performance of wavelet neural network for short-term time series. The diffusion wavelet neural network could more effectively improve the predictive accuracy than that of back-propagation neural network and grey-based forecasting method.

目錄
摘要.....................................................................	I
Abstract...............................................................II
誌謝 ......................................................................III 
目錄..................................................................... IV
圖目錄..................................................................	VII
表目錄..................................................................	IX
第一章 緒論...........................................................1
1.1	研究背景........................................................	1
1.1.1 個案描述......................................................	1
1.1.2 小樣本學習問題............................................	4
1.2 研究動機........................................................	6
1.3 研究目的........................................................	7
1.4 研究範圍與限制..............................................	7
1.5 研究流程........................................................ 	8
第二章 文獻探討....................................................	10
2.1 虛擬樣本產生法...............................................	10
2.1.1 資訊擴展......................................................	11
2.1.2 其他虛擬樣本產生方法...................................	14
2.2 小波神經網路..................................................	17
2.2.1 小波轉換......................................................	18
2.2.2 多重尺度分析................................................19
2.2.3 小波函數與尺度函數之關係............................	21
2.2.4 Daubechies的尺度函數與小波函數................	22
2.3 倒傳遞類神經網路...........................................	23
2.4 灰預測理論.....................................................	25
第三章 研究方法....................................................	28
3.1 時間分割........................................................	28
3.2 相依虛擬樣本產生法........................................	30
3.2.1 範圍推估.....................................................	31
3.2.2 補值機制.....................................................	34
第四章 實例驗證....................................................	36
4.1 實驗環境........................................................	36
4.1.1 實驗資料.....................................................	36
4.1.2 實驗方式.....................................................	37
4.1.3 建模軟體.....................................................	38
4.1.4 預測誤差評估指標	.........................................38
4.2 實驗結果........................................................	39
4.2.1 預測準確度比較............................................	39
4.2.2 結果說明......................................................	44
第五章 結論與建議.................................................	47
5.1 結論...............................................................	47
5.2 未來研究建議..................................................	48
參考文獻..............................................................	49

圖目錄
圖1-1 封裝方式(a)傳統封裝；(b)晶圓級封裝...............................................	2
圖1-2 晶圓級封裝製程以及錫球高度示意圖..................................................4
圖1-3 本研究流程圖.................................................................................	9
圖2-1 小樣本資料結構的資訊間隙	..............................................................10
圖2-2 小波分解樹....................................................................................	23
圖2-3 類神經網路架構圖..........................................................................	24
圖3-1 本研究之新訓練樣本建構過程..........................................................	28
圖3-2 非等間距時間序列資料示意圖..........................................................	29
圖3-3 相依虛擬樣本之值域推估示意圖 (Li, Chen, Chen, et al., 2011)..........	30
圖3-4 外圍值域計算之示意圖...................................................................	31
圖3-5 當擴散水準 由0.99降低至0.5時之[LIF, UIF](Li, Chen, Chen, et al., 2011)	..............................................................................................................32
圖3-6 最終可能值域推估示意圖.................................................................	33
圖3-7 相關性為無關時的模糊化求解圖示....................................................	33
圖3-8 以UB、 、以及LB建構模糊三角隸屬函數...........................................	34
圖3-9 可能性評估機制之運作方式	..............................................................35
圖4-1 改良式擴散小波類神經網路驗證結果比較圖.......................................	36
圖4-2 原始實驗資料(30筆)小波(db 4, Level 4)之Superimpose圖................	45
圖4-3 加入虛擬樣本之實驗資料(59筆)小波(db 4, Level 4)之Superimpose圖.	45
圖4-4 原始資料(30筆).............................................................................	46
圖4-5 本研究敏感度分析之9組實驗資料趨勢圖...........................................	46

表目錄
表2-1 一筆時間相依之時間序列資料範例....................................................	16
表2-2 時間相依資料所組成之訓練與測試資料集...........................................	17
表4-1 新裝機試產前三十筆資料.................................................................	37
表4-2 一筆時間相依之時間序列資料範例....................................................	38
表4-3 時間相依資料所組成之訓練與測試資料集...........................................	38
表4-4 灰預測GM(1,1)與BPN、WNN之預測值與誤差....................................	40
表4-5 DWNN當K=1時於三種擴散係數之預測值與誤差................................	41
表4-6 DWNN當K=3時於三種擴散係數之預測值與誤差................................	42
表4-7 DWNN當K=4時於三種擴散係數之預測值與誤差................................	43
表 4-8 DWNN 與GM(1,1)、BPN及WNN之MAPE彙整表................................	43
                                    

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