Image boundary extraction based on island model genetic algorithms for integrated circuit defect detection

The integrated circuit chip with high performance has a high sensitivity to the defects in manufacturing environments. When there are defects on a wafer, the defects may lead to the degradation of chip performance. It is necessary to design effective detection approaches for the defects in order to ensure the reliability of wafer. In this paper, a new method based on image boundary extraction is presented for the detection of defects on a wafer. The method uses island model genetic algorithms to perform the segmentation of wafer images, and gets the optimal threshold values. The island model genetic algorithm uses two distinct subpopulations, it is a coarse grain parallel model. The individuals migration can occur between the two subpopulations to share genetic materials. A lot of experimental results show that the defect detection method proposed in this paper can obtain the features of defects effectively.