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研究生:張上淵
研究生(外文):SHANG-YUAN CHANG
論文名稱:應用電腦視覺與類神經網路於BGA檢測系統
論文名稱(外文):Applying Computer Vision And Neural Network for BGA Inspection System
指導教授:林錫寬
指導教授(外文):Shir-Kuan Lin
學位類別:碩士
校院名稱:國立交通大學
系所名稱:電機與控制工程系
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2002
畢業學年度:90
語文別:中文
中文關鍵詞:類神經BGA影像處理
外文關鍵詞:neural networkBGAimage processing
相關次數:
  • 被引用被引用:6
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本文應用電腦視覺技術與改良式SOM網路(Self-Organizing Map進行BGA量測與缺陷的檢測,系統之基本架構主要是以擷取到的BGA原始影像,利用彩色像素群聚與類神經網路做最佳化的影像分割,最後用影像處理的技術,將BGA構裝型式的銲球直徑、銲球真圓度、銲球密度、球位偏差、球距、雙球、銲球破損或缺球與否的性質一一檢測出來。在BGA自動檢測系統的研究方法與處理程序上,首先需將銲球與BGA原始影像中銲墊、通孔與綠色導線分割出來,同時將所有之雜訊一併濾除,因此,如果要將BGA銲球與其它視為雜訊之影像分離出來,也是本文所究研的範圍。主要目的是希望從帶有雜訊的BGA影像中,利用類神經網路與形態學處理程序把非銲球之影像與雜訊去除,然後將所得BGA完整影像,利用區塊點標記、區塊分析,和區塊像素的邊緣檢測,將區塊內的銲球座標點找出,然後再利用最小誤差圓方程式與簽名(signature) 等,計算出銲球的大小、半徑、球距、面積、真圓度是否和標準值相近,最後配合相關係數的選定檢測銲球密度是否有缺陷。綜合以上的檢測結果,將缺陷的數量、種類與缺陷位置清楚的標示在原始BGA影像上,作為良品分類之參考。

This paper applies computer vision and the improved Self-Organizing Map (SOM) network techniques to perform BGA measurement and defect inspection. The basic structure of the investigated system is, mainly, to automatically measure or inspect, one by one, the high solder ball, the solder ball diameter of the fine-pitched BGA structure and mounting pattern, solder ball roundness, solder ball density, ball offset, ball pitch, double ball, the characteristics of solder ball damage or ball missing, using the retrieved BGA original image, the optimal image separation with color pixel grouping and neural network methods, and finally digital image processing techniques.
In the aspects of investigation method and treating process of a BGA automated inspection system, we first need to separate solder balls from the solder pads, holes and green conductors in the original BGA image. Meanwhile, all noises must be filtered out. Consequentially, it is within the scope of this study to separate BGA solder balls from other images viewed as noises. The main purpose of this study is to filter out non-solder ball images and noises in the noise-corrupted BGA images, using neural network techniques and image filtering processes, to locate the solder ball coordinates within the regions in the retrieved full BGA image applying region point labeling, region analysis and edge detection of region pixels, and, then, to compute the size, diameter, ball pitch, area, and roundness-- whether they approach standard values-- of the solder balls using least error circle equation / best fit circle equation, signature, etc. Lastly, by matching the selected correlation coefficient, we inspect whether the solder ball density is defected. Combining the above-mentioned inspection results and applying various categories of defects as categorization standards, we clearly depict the quantities and categories of the defects and the locations of the defects in the original BGA image for the categorization reference for GOOD quality product.

第一章 緒論
1.1 前言
1.2 研究背景與動機
1.3 文獻回顧
1.3.1 BGA檢測相關文獻回顧
1.3.2 影像分割與邊緣檢測相關文獻回顧
1.4 研究目的
1.5 研究範圍與研究方法
1.6 論文整體編排介紹
第二章 影像分割技術與類神經網路分割法
2.1 前言
2.2 整體性的臨界值選定法
2.2.1 OTSU法影像分割技術
2.2.2 直方圖波谷法與峰值法影像分割技術
2.2.3 影像疊代修正法
2.3 傳統影像邊緣檢測分析
2.3.1 一階微分影像邊緣檢測技術
2.3.2 其它以遮罩運子邊緣檢測技術
2.4 彩色影像分割使用類神經網路
2.4.1 倒傳遞類神經網路影像分割法
2.4.2 學習向量量化網路影像分割法
2.4.3 放射基底函數網路影像分割法
2.5比較結果與討論
第三章 改良式SOM網路影像分割與BGA缺陷檢測方法
3.1前言
3.2 系統架構與檢測流程
3.3 群聚分析與改良式SOM類神經網路樣本建立
3.4 改良式SOM網路應用於影像最佳化分割
3.4.1 SOM神經網路的組成元素
3.4.2改良式SOM類神經網路演算法
3.5數學形態學處理
3.6點標識法
3.7銲球真圓度檢測
3.7.1最小誤差圓方程式
3.7.2真圓度檢測使用簽名法
3.8 BGA銲球密度檢測原理
第四章 BGA缺陷檢測與實驗結果
4.1前言
4.2改良式SOM類神經網路影像分割結果
4.3 BGA瑕疵點尋找與點標記技術結果
4.4 BGA銲球真圓度與錫球密度檢測結果
4.5 BGA缺陷檢測系統實驗結果
4.5.1 BGA離線特徵學習
4.5.2 BGA線上缺陷檢測
第五章 結果與未來展望
5.1前言
5.2具體成果
5.3未來展望

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