基於距離之向量量化演算法之自動缺陷偵測與分類

Abstract

對於半導體製造廠商而言，一個可靠且自動化的製造過程對於產品良率的提升以及降低耗損是十分重要的。過去人們通常仰賴人眼進行缺陷的偵測與分類，但是因利用人眼進行判斷容易受到人眼疲勞或是檢測者判斷上的差異，進而影響分析的結果。 本論文實現了一套自動化缺陷偵測與分類系統。在缺陷偵測的部分，利用MAD的方法，將待測影像與參考影像對齊，其中參考影像為不包含缺陷的影像。接著，我們將對齊後的待測影像與參考影像相減，並進行二值化的程序以獲得二值化差異影像。並且考量到在相減影像中會有一些微小的零星雜訊，我們設定一個最小相連接像素點個數閥值，過濾這些面積較小的零星雜訊，最後，從待測影像當中擷取出缺陷影像以進行缺陷分類。 參考Kohonen學習法，我們修改向量量化演算法。由於在初始的向量種子選取上是隨機選擇的，將導致各種不同的分類結果。實現缺陷分類模型的建立，本論文建立一套基於距離之向量量化演算法，利用初始向量種子選擇的計算以及各個缺陷影像間的距離計算，使得向量量化演算法具有相當好以及穩定的分類結果。

In general, a reliable and automatic semiconductor fabrication processes is of great importance to product yield and cost reduction. In the past, we made use of human vision to do die defect detection and classification, which is hindered by the easy fatigue and fuzziness of human eyes and the decision difference between inspectors. In this thesis, we implement a vision-based automatic defect classification system. In our defect detection component, we have used the MAD method to align the test image to the reference image. To acquire the binary defect images, we subtract the test image from the reference image, and then we convert the difference image into the binary image by setting a threshold. Moreover, we removed the scattering noises by setting a minimum number of connected noisy pixels required. Finally, we extract all defects in the test image in order to perform the defect classification. For defect classification, we revise the Vector Quantization Algorithm with Kohonen learning rule. Because of the initial seeds have been selected randomly, it will lead to various clustering outcomes. We derive distance based vector quantization with first seed selection measure and it can obtain high classification accuracy and consistent classification result.