為了因應摩爾定律的快速變化，在半導體產業發展一系列的發展藍圖(Technology Roadmap)以保持產業的競爭力。在資本密集的半導體產業裡，良率是影響產業競爭力的要素。而測試封測製程中，探針卡是探測晶圓良率的關鍵零組件，為確保檢測品質和信度故探針卡製造廠商也必須追隨該產業的領頭技術，對於鑽孔製程方面須更精密的控制，方能面對晶圓線寬變小、尺寸變大等趨勢。本論文之目的為發展半導體探針卡的鑽孔製程的品質預測與大數據分析的架構，結合多項分析方法，包括：逐步迴歸(Stepwise Regression)、自我組織映射圖(self-organizing map; SOM)與多變量雲型適應迴歸(Multivariate Adaptive Regression Splines; MARS)，藉由完善的預測模型找尋鑽孔製程關鍵參數的機制，以提昇探針卡印刷電路板的孔徑品質與生產良率的建議。本論文並以台灣某探針卡製造廠為實證研究以檢驗效度，利用鑽孔製程的設備機台資料萃取最具影響多品質結果的關鍵參數並量化程度，同時根據產品製程狀況進行品質預測，並訂定校度檢驗指標以檢驗研究結果之可行性。研究結果找出部分鑽孔製程參數影響品質，公司人員能依據此結果並與領域專家結果討論，了解製程修正以改善目標，提出更適合的參數組合以更精準控制品質，同時公司藉由預測模型在產線上的導入，能加速判斷產品優劣準則的一個方法。未來研究期望配合多項的感測器與完整資料蒐集，以虛擬量測進一步研究以增進模型預測的效果與達成即時控制的工業4.0願景。

Semiconductor industry develops a series of technology roadmap, for example, International Technology Roadmap for Semiconductors(ITRS) to address rapid changes driven by Moore’s Law. Yield is a factor of keeping competition for the capital-intensive semiconductor companies. In particular, Probe card is a key component to test functionality of dies, that is for the reliebility &. For the drilling process of probe cards manufacturing, tolerances should be tightened to meet higher quality requirement to solve smaller critical dimension and bigger sizes in wafers than products of previous generations. Therefore, this thesis aims to develop a big data mining analytics and quality prediction framework for drilling manufacturing in probe card industry. The framework integrates stepwise regression, Self-Organizing Map, and Multivariate Adaptive Regression Splines method and construct a quality prediction model to explore impact factors in drilling process and provide suggestion to improve drilling quality and yield. Under the framework, probe card technology and concept can be more suitable for ITRS. An empirical study to validate the model, which cooperates with a Taiwanese probe card manufacturing factory, is provided in this thesis. The framework extracts critical parameters among multiple quality indices from a great amount of equipment data. The model provides quality prediction according to product situation and set the validation index. The results indicate that some drilling parameters will affect quality, and demonstrate the adjust process for operators. In addition, the enterprise can accelerate the judgment time for yield by importing the prediction model. Virtual metrology, real time control, and industry 4.0 are the future research directors by combining several sensor data and entire data collection with the model.

目錄 i
表目錄 iii
圖目錄 iv
符號定義 1
第一章 緒論 2
1.1 研究背景、動機與重要性 2
1.2 研究目的 4
1.3 論文結構 5
第二章 文獻回顧 7
2.1 探針卡與鑽孔製程介紹 7
2.2 國際半導體技術發展藍圖(ITRS) 10
2.3 APC在PCB產業的應用 13
2.4 多變量雲型適應迴歸 15
第三章 研究架構 18
3.1 問題定義 20
3.2 資料準備 21
3.2.1資料收集與檢視 21
3.2.2資料清理 22
3.2.3資料轉換 22
3.2.4資料合併 22
3.2.5正規化因子 22
3.2.6資料分割 23
3.3 模型建立 23
3.3.1去除干擾雜訊 24
3.3.2品質預測模型 24
3.4 結果評估與解釋 26
3.5 結果應用 27
第四章 實證研究 29
4.1 問題定義 29
4.2 資料準備 30
4.2.1資料收集與檢視 30
4.2.2資料轉換 32
4.2.3資料清理 32
4.2.4資料合併 33
4.2.5正規化因子 33
4.2.6資料分割 34
4.3 模型建構 35
4.3.1消除雜訊資料 35
4.3.2關鍵參數與品質預測模型 37
4.4 結果評估與應用 40
第五章 結論 42
5.1 研究貢獻 42
5.2 研究限制與未來研究方向 42
參考文獻 44

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