Image Segmentation Based on Island Model Genetic Algorithms for Wafer Defects Process
The development of circuit design technology has leaded to an extreme grow in number of transistors in electronic systems, and has made the chip density continue to increase. The high performance chip 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 segmentation 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. One subpopulation executes by the genetic operations with sharing strategy, and another subpopulation executes by evolutionary programming. 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, thus can provide better data information for the diagnosis of the defects on a wafer.
Integrated circuit wafer defect detection image processing genetic algorithms
Pan Zhongliang Chen Ling Zhang Guangzhao
Department of Electronics, South China Normal University, Guangzhou 510006, China Department of Electronics and Communications, Sun Yat-sen University, Guangzhou 510275, China
国际会议
2008 Sino-European Workshop on Intelligent Robots and Systems(SEIROS08)(第一届中欧智能系统及机器人国际学术研讨会)
重庆
英文
1-6
2008-12-11（万方平台首次上网日期，不代表论文的发表时间）