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姓名 張茗鈞(Ming-Chun Chang)  查詢紙本館藏   畢業系所 機械工程學系在職專班
論文名稱 應用類神經網路預測COG製程對於中小尺寸TFT-LCD產生之應力狀態
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摘要(中) 在LCM Chip-on-Glass (COG) 封裝製程,因玻璃基板和驅動IC之間熱膨脹係數的差異,當驅動IC藉由異方性導電薄膜(ACF)與玻璃基板黏合後,將會使面板產生應力進而導致漏光Mura發生。隨著市場產品的需求,對應的面板有越來越薄的趨勢,而使用更薄的玻璃基板將使得漏光Mura缺陷更加嚴重。 本研究首先以光測力學方法中的光彈法,量測玻璃基板封裝矽晶片周圍區域的相位差(Retardation),並經由應力-光學定律求得玻璃基板應力值。但玻璃是一種低雙折射性材料,意即玻璃在受到應力時所產生的雙折射效應非常微弱,在量測薄化玻璃基板十分不易。
因此,利用類神經網路模擬出COG封裝產生之應力的預測模型,作為薄化玻璃基板應力值預測,並且藉由模型預測了解改善對策對於降低應力值之效益,以節省大量材料、人力與時間成本。
摘要(英) In the LCM Chip-on-Glass (COG) packaging process, there are differences in the thermal expansion coefficient between the glass substrate and the driver IC, when the driver IC uses anisotropic conductive film (ACF) to bond to the glass substrate, the panel produces stress which leads to the occurrence of light leakage through Mura defects. The market demand trend is for increasingly thinner production panels, but the use of thinner glass substrates increases the likelihood of more serious light leakage Mura defects.
The first phase of the research was to measure the light mechanics by the photoelasticity method through measuring retardation in the area surrounding the glass substrate packaged silicon chip, and adding stress to obtain the optical glass substrate stress values. However, as glass is a material with low birefringence, the birefringence effect generated when glass is subjected to stress is very weak, and so the measurement of thin glass substrate is extremely difficult.
Therefore, a neural network model is used to simulate COG package production stress prediction. Thin glass substrate stress values were predicted and used by the model to predict and understand the improvement efficiency measures in reducing stress values. This could save a lot of materials, manpower and time costs.
關鍵字(中) ★ 類神經網路
★ COG封裝
★ 光彈
★ 應力量測
關鍵字(英) ★ neural networks
★ COG packing
★ photoelasticity
★ stress measurement
論文目次 目錄
中文摘要i
英文摘要ii
致謝iii
目錄iv
圖目錄vii
表目錄x
第一章 緒論1
1-1 前言1
1-2 COG封裝製程介紹2
1-2-1 作業流程3
1-2-2 異方性導電膜3
1-3 研究動機與目的5
1-4 研究範圍與限制6
1-5 論文架構7
第二章 原理與文獻探討8
2-1 漏光Mura8
2-1-1 Mura介紹8
2-1-2 漏光Mura判定方式9
2-2 COG封裝應力分析10
2-2-1 熱應力原理11
2-3 光彈法13
2-3-1 光彈原理16
2-3-2 光-應力定律 16
2-4 類神經網路17
2-4-1 人工神經元模型19
2-4-2 類神經網路學習方式20
2-4-3 倒傳遞類神經網路基本架構22
2-4-4 倒傳遞網路之學習演算法25
第三章 實驗方法與步驟30
3-1 應力量測30
3-1-1 光彈檢測系統31
3-1-2 光彈檢測精度驗證32
3-1-3 樣本檢測說明34
3-2 樣本製備35
3-2-1 DOE(Design of Experiment)實驗計畫法 35
3-2-2 COG樣本製作38
3-2-3 玻璃基板實際溫度量測38
3-2-4 翹曲量測39
3-2-5 翹曲與應力迴歸分析40
3-3 類神經網路建構COG封裝應力預測器42
3-3-1 訓練資料42
3-3-2 訓練數據輸入因子43
3-3-3 網路訓練函數選擇44
3-3-4 網路訓練參數46
第四章 實驗結果與討論50
4-1 COG封裝產生之應力預測與實際量測結果驗證50
4-2 COG封裝對薄化玻璃基板造成應力值預測結果51
4-3 降低驅動IC厚度對應力改善效益分析52
4-4 對玻璃基板加熱對應力改善效益分析53
4-5 降低壓頭溫度對應力改善效益分析55
第五章 結論與建議57
5-1 結論57
5-2 未來研究與建議58
參考文獻 59
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指導教授 黃衍任(Yean-ren Hwang) 審核日期 2014-6-10
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