隨著各類型電子產品的需求趨向於輕、薄、短、小，印刷電路板上零組件的精密度與零組件間的密度將有所增加。為了有效的控制生產成本、提升生產品質與效率，業者必須尋求更先進之生產技術來改善表面黏著技術(Surface Mount Technology, SMT)的品質。大部分的表面黏著技術問題與錫膏印刷製程有關(Pan et al. 1999 , Huang 2010, Tsai 2008)，而錫膏體積量為衡量錫膏印刷製程品質的重要指標，因此錫膏體積量的控制能力為獲得高良率和生產效益的關鍵生產技術之一。

錫膏體積量的控制屬於「作業窗」問題，且影響其產出的參數眾多，如果僅依據工程師經驗來判斷其設定值，可能導致製程不穩定而造成不良率提高。本研究針對錫膏印刷製程參數最佳化問題提出一系統性的求解程序，首先透過歷史資料與工程知識篩選出重要參數；接著使用田口方法推斷理想刮刀壓力與最佳參數水準組合。此外，本研究也提出透過倒傳遞類神經網路(Back-Propagation Neural Network)建立回應值之SN比與控制因子間的關係模式，以期望函數(Desirability Function)合併輸出值作為適應函數，再利用基因演算法(Genetic Algorithm)、粒子群演算法(Particle Swarm Optimization)、模擬退火法(Simulate Anneal)，分別求解最佳製程參數組合。本研究以一實際個案研究來說明所提出方法的有效性，透過個案研究發現本研究提出之方法可減少錫膏體積量的浪費和大幅的增加作業窗寬度以提高產品的品質。

With the demand for various types of electronic products tend to be light, thin, short and small. The precision of parts and the density between the parts of the printed circuit board will increase. In order to control product cost effectively and enhance production quality and efficiency, the manufacturers must find the advanced manufacturing technology to improve surface mount technology. Most surface mount technology problems are correlated to solder paste printing (Pan et al. 1999 , Huang 2010, Tsai 2008), and solder paste volume is an important quality measure of solder paste printing process. Therefore, controlling the solder paste volume is a key production technologies to obtain high–yield rate and maintain production effectiveness.

Controlling the solder paste volume is an operating window problem, plenty of parameters will affect the output; if we only rely on engineer’s experiences to determine the values, the defect rate may increase owing to the unstable manufacturing process. This study proposes a systematic procedure for parameter optimization of the solder paste printing process. First, historical data and engineering knowledge are used to determine the significant factors. Second, the ideal squeegee pressure and optimal parameter settings are determined by using Taguchi methods. Also, we propose utilizing back-propagation neural network and desirability function and integrating genetic algorithm, particle swarm optimization, and simulate anneal to obtain the optimal parameter combination. A real case was implemented and analyzed to demonstrate the proposed approach’s effectiveness. The results show that our proposed method can reduce the waste of solder paste volume and substantially increase the operation window width in order to improve product quality.

圖目錄 IX
表目錄 XI
第一章 緒論 1
1.1 研究背景與動機 1
1.2 研究目的 3
1.3 研究流程 3
1.4 論文結構 4
第二章 錫膏印刷的製程與原理介紹 5
2.1 表面黏著技術的演進 5
2.2 錫膏印刷製程簡介 6
2.3 錫膏簡介 7
2.4 錫膏製程的品質衡量與影響因子 9
第三章 相關研究 11
3.1 田口方法 11
3.1.1 基本原理 11
3.1.2 直交表的應用與選擇 13
3.1.3 信號雜音比 15
3.1.4 作業窗 17
3.1.5 確認實驗 19
3.1.6 田口參數設計之步驟 19
3.2 類神經網路 20
3.2.1 類神經網路之基本架構 20
3.2.2 類神經網路的類型 23
3.2.3 倒傳遞類神經網路 24
3.3 期望函數 27
3.4 基因演算法 29
3.4.1 基因演算法之基本原理 29
3.4.2基因演算法步驟 30
3.5 模擬退火法 35
3.5.1 模擬退火法的基本原理 35
3.5.2 模擬退火法的步驟 36
3.6 粒子群演算法 41
3.6.1 粒子群演算法的基本原理 41
3.6.2 粒子群演算法步驟 42
第四章 研究方法 49
第五章 個案研究 53
5.1 個案簡介 53
5.2 尋找回應值與關鍵製程參數 53
5.3 應用田口方法求取最佳參數水準組合 56
5.4 應用類神經網路、期望函數與啟發式演算法求取最佳參數水準組合 66
5.4.1 建構類神經網路模型 66
5.4.2 結合類神經網路與基因演算法求取最佳參數水準組合 70
5.4.3 結合類神經網路與粒子群演算法求取最佳參數水準組合 73
5.4.4 結合類神經網路與模擬退火法求取最佳參數水準組合 76
參考文獻 85
附錄A 6組類神經網路模型進行交叉驗證 91
附錄B 類神經網路對錫膏印刷製程之各參數的轉換尺度化公式 95
附錄C 錫膏體積量的平均數差異性檢定 96

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