本研究提出一套發光二極體自動瑕疵檢測系統，利用各種數位影像處理技術於晶粒瑕疵辨識中。檢測項目包含正常晶粒、破晶、電極區刮痕瑕疵、電極區殘金瑕疵、發光區刮痕瑕疵與發光區剝落瑕疵。
本研究主題分為三部分：發光二極體晶粒的定位與擷取、各個結構的特徵值抽取以及發光二極體瑕疵分類。在發光二極體晶粒擷取與定位方面，使用正規化相關係數法，將發光二極體每顆晶粒擷取出來。在取得晶粒各結構部分，利用K-means分群法將晶粒的外觀、電極區與發光區分開檢視。在晶粒外觀檢測方面，使用統計式門檻值二值化後，計算內部黑色區塊的個數，再以型態學區域填充，填補晶粒內不必要的雜點，接著取得外型的特徵值。在電極區瑕疵檢測方面，使用直方圖等化改變電極影像的對比度，利用統計式門檻值二值化後，運用型態學封閉運算修飾，之後計算電極區特徵值。最後在發光區瑕疵檢測方面，利用統計式門檻值三值化將發光區內的基板，發光區和背景的像素分隔開，使用型態學封閉運算修飾後，之後將發光區特徵值抽取出來。最後將所得到的各部分特徵值經過訓練之後利用倒傳遞類神經網路與支援向量機兩種分類器作分類，並將分類器拆成兩層來辨識。以本研究選取468顆晶粒影像進行辨識，在兩種分類器總辨識率都高達95%以上，證明本研究所提出的檢測方法為一種極適合於發光二極體瑕疵之檢測。

This study presents our proposed automatic defect inspection system for LEDs that applies various image processing methods on items including normal chips, chip fragment, scratch mark defect of the pad area, remained gold defect of pad areas, scratch mark defects of luminescent areas, and missing luminescent areas.
The inspection system included three steps: positioning and capture of LEDs, identification of LED components, and defect classification of LEDs. We first used the normalized correlation-coefficient method to capture every LED chip. We then applied K-means clustering method to separate each LED’s external surface, pad, and lighting area. We inspected the chip’s surface by counting black blocks after using statistical threshold binarization. Region filling of morphology was used to fill with unnecessary noises, and the features of externals were obtained. Regarding the inspection of pad areas, we used histogram equalization to change the contrast of each area’s image. After using statistical threshold binarization, we applied closing of morphology to modifying means and obtained features of pad areas. With inspecting lighting areas, we first used statistical multi-threshold to separate substrates, lighting areas, and backgrounds. We obtained features of lighting areas after using closing of morphology. We also adopted the back-propagation neural network and support vector machines to classify five defects.
Experimentally, we collected 486 LED chips for defect inspection. The total recognition rates of both classifiers reach 95%, indicating that we had successfully developed a set of defect inspection system that is applicable to LEDs.

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