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研究生： 艾文
IVAN PUTERA PRATAMA
論文名稱： 導體滲鍍瑕疵; 利用同欣電子提供之少量樣本資料獲得生產線中最關鍵工作站
Bridging Conductor Defect; Obtaining the Most Decisive Production Line Station by using Small Sample Size Data in Tong Hsing Electronics Industry
指導教授： 梁德容
DERON LIANG
張欽圳
CHIN-CHUN CHANG
口試委員:
學位類別： 碩士
Master
系所名稱： 資訊電機學院 - 資訊工程學系
Department of Computer Science & Information Engineering
論文出版年： 2019
畢業學年度： 107
語文別： 英文
論文頁數： 48
中文關鍵詞： 少量樣本資料 、標準距離 、卡方檢驗 、累積分佈函數
外文關鍵詞： small sample, univariate distance, chi-square, cumulative distribution function
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    • 同欣電子是一間專門製造印刷電路板的台灣公司。在他們的生產線上，會產生很多的瑕疵品。其中一種瑕疵品叫導體滲鍍。到目前為止，該公司並不了解造成該瑕疵品的可能因素。透過分析生產資料，我們希望能獲得最有可能造成該瑕疵品的站別和生產參數。然而，紀錄這種瑕疵品的生產資料量太小，導致任何的機器學習模型都無法正確地解決問題。儘管如此，我們仍然可以使用統計方法分析生產資料來獲得結果。基於分析結果，公司可以更謹慎地調整他們的機器參數，進而降低造成該瑕疵品的機率，在未來同時也可以提高生產良率。

    Tong Hsing Electronics Industry is a Taiwan-based company that specialized in PCB manufacturing. Currently, they still found many bad products from their production line. One type of defect that they encounter is “Bridging Conductor”. Until now the company doesn’t have any strong knowledge about possible factors causing that defect to their products. By analyzing the production line log data hopefully, we can obtain station and production parameters that most likely contributing to the defect. However, the production line log data for this type of defect are too small to be analyzed, so that any machine learning models can’t be used to solve the problem correctly. Nevertheless, we still can analyze the data by using any statistical approach to obtain the result. Based on that, the company can adjust its machine parameters more carefully, thus decreasing the probability of finding these defects while also increasing its production yield rate in the future.

    摘要 i ABSTRACT ii ACKNOWLEDGMENT iii Chapter 1 Introduction 1 1.1. Background 1 1.2. Problem Statement 2 1.3. Constraints 2 1.4. Outline of Chapters 2 Chapter 2 Literature Review 3 2.1. Univariate Standard Distance 3 2.2. Pearson’s Chi-square Test 3 2.3. Shapiro-Wilk Test 5 2.4. Cumulative Distribution Function 6 Chapter 3 Methodology 8 3.1. System Architecture 8 3.2. Dataset 9 3.2.1. Hardware-based Parameters 9 3.2.2. Chemical solution Parameters 10 3.2.3. Variables 10 3.2.4. Pre-Analysis Insights 11 3.3. Data Preprocessing 12 3.3.1. Convert Raw Data to CSV 13 3.3.2. Extract Data 14 3.3.3. Gather Parameter Usages 15 3.3.4. Filter LOT 17 3.4. Feature Selection 18 3.4.1. Prepare Dataset 19 3.4.2. Feature Selection using Univariate Standard Distance 20 3.4.3. Classify Data Type 21 3.4.4. Feature Selection using Pearson’s Chi-square Test 22 3.4.5. Perform Normality Test 23 3.4.6. Feature Selection using Cumulative Distribution Function 24 3.5. Assumptions 26 3.6. Experiment Settings 26 Chapter 4 Results 28 4.1. Data Preprocessing 28 4.2. Feature Selection 29 4.2.1. Standard Univariate Distance 29 4.2.2. Pearson’s Chi-square Test 31 4.2.3. Cumulative Distribution Function 32 Chapter 5 Conclusion 34 5.1. Conclusion 34 5.2. Suggestion 35 Bibliography 36

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