彩色濾光膜(color filter, CF)為CMOS（互補式金屬氧化物半導體，complementary metal-oxide-semiconductor）影像感測器（CMOS image sensor, CIS）的關鍵組件，對CMOS影像感測器製造商而言，彩色濾光膜製程良率是品質維護、提高收益的重要因素。自動光學檢測（automated optical inspection, AOI）為檢測晶圓之表面缺陷的關鍵設備，且已廣泛應用於半導體領域，AOI能提供晶圓的高解析度圖像，在取得影像資訊上具重要影響性。AOI雖然能檢測出缺陷影像進而降低人力成本，但卻無法有效識別出缺陷之類型，以至於無法協助專家追溯缺陷原因。本研究發展一套結合相似度匹配（similarity matching）功能之彩色濾光膜缺陷影像分類模式，除了整合影像分析與資料挖礦技術進行缺陷分類之外，相似度匹配進一步降低影像預測誤判率（false alarm），進而有效達到製造商的實際目標。本研究以新竹科學園區某CMOS影像感測器製造商為例，收集彩色濾光膜製程缺陷影像進行分析與實證，所提出方法的有效性和結果也證明了其實用價值。

For CMOS image sensor (CIS) manufacturing, defect reduction is a key taskforce for quality assurance and yield enhancement. Indeed, automatic optical inspection (AOI) is the critical equipment for defect inspection. Although AOI can capture possible defect images with high throughput and low manual labor, it cannot identify defect types for troubleshooting purpose. In particular, the advanced AOI equipment can provide a high resolution defect image of a whole wafer for overall judgments. This study aims to develop a hybrid data mining approach for defect classification for whole wafer images based on the result of classifier. The proposed approach consists of two stages similarity matching to rearrange the order of features from different images of CMOS. This concept could not only reduce the false alarm rate but enhance the correct rate. An empirical study was conducted with a leading CIS manufacturing company in Taiwan to estimate the validity and the results also demonstrated the practical value of the proposed approach.

簡禎富、許嘉裕，資料挖礦與大數據分析，前程文化，新北（2014）。
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