彩色濾光膜及微透鏡製程為製造CMOS影像感測器的一環，利用於相機、手機鏡頭。影像感測元件彩色濾光膜廠進行製造時可能會在感測區或非感測區造成缺陷樣型導致缺陷率提升，為了提升產品良率，希望於製造過程找出造成缺陷樣型的機台等原因，即時進行修復並減少重工情形。
目前多半是憑藉工程師的經驗來做故障排除的問題，錯誤及試驗法不夠快速且準確度不高，很容易造成人為對照的失誤及因為經驗不足而誤判，故本研究目的為發展一套彩色濾光膜及微透鏡資料挖礦架構模式，以協助工程師診斷造成缺陷樣型的原因。透過蒐集影像感測元件彩色濾光膜廠的缺陷樣型相關資料，結合列聯表分析的卡方獨立性檢定與Cramer’s V相關係數，利用關聯規則切割訓練集資料建立模型，測試集資料計算正確率篩選合適模型，配合演算法的支持度、信賴度與增益三個指標，定義篩選規則門檻值整理造成缺陷樣型的潛藏規則以進行規則評估。

CMOS image sensor includes color filter and microlens process, which is used to manufacture cameras and phone lens. In color filter and image sensor manufacturing company’s manufacturing process, it may cause various defect types and defect rate in sensing or non-sensing area. To improve product’s yield and find causes of defect type, we should repair the tools in time and reduce the rework rate.
Now it almost uses engineers’ experience for trouble shooting. Try and error method is not quick enough and may cause errors because of less of experience. This research is aim for constructing a data mining framework of color filter and microlens to help engineers detecting causes of defect types. By using defect types’ data in fab, we could combine Chi-square test for independence, Cramer’s V correlation coefficient and divide training data set of Association Rules to build model. Using the correct rate of testing data set to select suitable model and setting threshold of three indexes：support, confidence and lift to screen useful rules before executing evaluation.

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