近年來，資料分析之技術應用在電子業之之表面黏著技術(surface mount technology, SMT)製程的改善，不僅有相當多之文獻可供參考，且歷經數十年持續之發展，該製程已有穩定良好之生產良率。然自2006年7月1日起，輸往歐盟之產品須依照其所規範之危害物質限用「Restriction of Hazardous Substance，RoHS」指令，不可含有六項危害環境之危險物質，其中以鉛影響最大，乃因電子業原使用已超過四十年之含鉛焊材，在因應RoHS規範而改用無鉛者後，必須重新考量其材料合金成分與熔融焊點之改變，其中導致不良情況發生之各項要因，必須重新進行實驗以得到最佳之參數設定與材料選擇。
　　由於無鉛化製程之導入，而使原本穩定之各製程參數產生許多不同於以往之設定與選擇，本研究則於此期間，在表面黏著製程站擷取一年份之製程資料，並應用模式樹方法來建構表面黏著製程品質管制模式，同時以多元線性迴歸來比較模式樹分枝後之迴歸預測結果，且藉倒傳遞類神經網路的強大預測能力來做為預測準確度的比較標竿。最後從預測能力指標來看，模式樹與倒傳遞類神經網路不相上下，卻明顯優於多元線性迴歸，且其所形成之樹狀結構確能清楚呈現資料之流向以及分類之法則，若能與統計製程管制(SPC)系統結合，確能給製程工程師在材料的選擇與參數的設定上予以參考之建議，以縮短實驗設計時程或產線偵錯作業的流程。

　　The European Union issued Directive on the restriction and use of certain hazardous substances in electrical and electronic equipment. Known as Restriction of Hazardous Substances (RoHS), it was enforced throughout the European community effective 1 July 2006. With the RoHS directive, the companies whose electronic products that include lead based soldering would not be allowed to trade with the member states of the European Union. Therefore, companies would begin to experiment on a reengineering effort that could be unsuccessful if they do not find suitable alternatives.
　　The data is collected from the surface mount process station in IC assembly factory while the company phased in lead-free process. Model tree is used to construct the quality control model to help process engineers identifying the critical variables that affect the results as yield. To validate the model, back-propagation neural network and multiple linear regression are used in this research.
　　Finally, the predictive capability of this model is similar with back-propagation neural network but much better than multiple linear regression. Besides, it indeed provides clear process information and predicted yield for engineers through the tree branches and regression functions in the leaf-nodes while making decisions on troubleshooting and experiment designs.

邱皓政，量化研究法(二)統計原理與分析技術，雙葉書廊有限公司，民國九十四年。
葉怡成，類神經網路模式應用與實作，儒林圖書公司，民國九十二年三月。
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