Defect Contour Detection of Complex Structural Chips

In the manufacture of chips, it is important to detect defects to assess whether the chip is potentially damageable that could cause unnecessary cost. Most assessment rules are set in light of characteristics determined by defect contours, such as area and range. However, conventional image process methods seldom show a satisfactory performance on chips with complex structures because they are difficult to distinguish defect contours from edges of structures. To solve this issue, this study proposes a method based on region segmentation search. The positions of structures in the image are calculated by edge matching to obtain the number of structure layers in each pixel. Regions whose pixels have the same number are divided into subregions which are coded by the two-pass algorithm. The edges in each subregion are then extracted by the Canny operator to construct edge information of the whole image. Interpolation is used to correct incomplete defect edges according to their endpoints. The remaining interference contours are eliminated on the basis of their shapes. A study of a certain kind of chips is presented. Different illumination situations were simulated to verify the robustness of the proposed method. Most bubbles in the images were detected successfully with their contours coded accurately. Because of this, more than 92% of assessment results of chips were identical to the ones in reality engineering, which proves that the method proposed by this study can efficiently detect the defect contours and improve the ability obviously relative to the current approaches.