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研究生:劉秉儒
研究生(外文):Liu, Ping-Ju
論文名稱:電腦視覺應用於IC封裝探針痕之辨識與擷取
論文名稱(外文):The Recognition and Capture of Wafer Probe Marks in IC Package Using Computer Vision
指導教授:王朝興王朝興引用關係
指導教授(外文):Wang, Chau-Shing
口試委員:陳德超王朝興楊文然
口試委員(外文):Chen, Te-ChauWang, Chau-ShingYang, Wen-Ren
口試日期:2017-06-11
學位類別:碩士
校院名稱:國立彰化師範大學
系所名稱:電機工程學系
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:2017
畢業學年度:105
語文別:中文
論文頁數:56
中文關鍵詞:晶圓檢測探針痕人工類神經網路支持向量機電腦視覺機器學習opencvscikit-learn
外文關鍵詞:wafer testchip probeprobe mark inspectionartificial neural networkcomputer visionmachine learningsupport vector machineopencvscikit-learn
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  半導體是相當高度自動化的產業,但部分環節仍需依賴人眼找出製程瑕疵,例如封裝產業的晶片針測(Chip Probe)會在測試過程中檢出問題晶片,而遺留的探針痕會對後續的銲線接合產生程度不一的影響,而引致成品瑕疵。已有研究針對此一問題提出探針痕的識別與評估,可惜距離實務應用仍有距離,本研究透過電腦語言Python與擴充函式庫OpenCV與SciKit-learn進行軟體開發,以支持向量機建立機器學習模型,並與文獻所使用的類神經網路比較模型效能。
  研究結果顯示,支持向量機在探針痕的測試結果中,其精準度、召回率與F1-score等三項指標均優於類神經網路,且運算需求遠低於類神經網路。在實務應用中,識別率的提升除了可以降低誤判的成本耗損,較低的硬體需求與模型運算速度,也能為晶圓檢測硬體帶來正面的結果。
Semiconductor is a highly automatically industry, but semiconductor companies rely on engineers using eyeball analysis to judge wafer defect, like probe mark inspection during chip probe (CP). The probe mark will reduce wire bonding strange at chip assembly and packing stage, then cause final product test failed.
Some of researcher had found the way of probe mark recognition and classifying, however, it's not close practice online applications enough. To fill the gap, the study aims to develop an inspection software by using computer programming language Python and extend library Open CV, SciKit-learn for machine learning, and compare the model performance of artificial neural network (ANN).
The results show SVM has better test precision, recall and F1-score than artificial neural networks, and compute time is much shorter. This will help to reduce online error judge cost. Lower hardwarerequirement and good model performance are also the main benefits for inspection hardware design.
中文摘要 I
英文摘要 II
致謝 III
目錄 IV
圖目錄 VI
表目錄 VIII
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 研究方法 4
1.4 論文架構 5
第二章 封測製程介紹 7
2.1 晶圓製造 7
2.2 晶圓測試 8
2.3 晶片封裝 9
2.4 最終測試 12
第三章 電腦視覺 13
3.1 影像前置處理 13
3.2 影像二值化 14
3.3 邊緣偵測 15
3.4 數學形態學 17
3.5 輪廓檢測 20
3.6 影像特徵提取 22
第四章 機器學習 26
4.1 類神經網路 26
4.2 支持向量機 30
第五章 實驗結果與分析 35
5.1 電腦視覺運算結果 35
5.2 檢測分析 43
5.3 類神經網路結果分析 44
5.4 支持向量機結果分析 46
5.5 綜合分析 48
5.6 跨平台能力驗證 48
5.7 模擬應用測試 50
第六章 結論與展望 51
6.1 研究貢獻 51
6.2 研究限制 51
6.3 未來展望 52
參考文獻 53
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