摘要
在工业应用中,需要对方形扁平无引脚封装(Quad flat no-lead package,QFN)芯片表面划痕实时准确检测,提出了一种快速的芯片表面划痕检测定位方法.通过图像分割算法获取缺陷图像,结合轮廓提取算法可以较好地实现芯片表面划痕定位.同时,为了保证对芯片表面划痕实时检测,采用基于粒子群的Otsu多阈值算法进行图像分割,不仅使得图像中缺陷区域更加明显,而且缩短了芯片表面划痕检测时间.与直接采用Otsu算法相比,芯片表面划痕检测时间由秒级缩短至毫秒级,提高了芯片质量检测效率.该划痕快速定位检测方法对芯片检测设备软件系统开发与应用具有重要的参考价值.
In industrial applications,it is necessary to accurately detect scratches on the surface of a quad flat no-lead package(QFN)chip in real time.A rapid chip surface scratch detection and location method was proposed.According to image segmentation algorithm,a defect image can be acquired firstly.Then by combining with the contour extraction algorithm,the chip surface scratch location can be achieved.At the same time,in order to ensure real-time detection of scratches on the chip surface,image segmentation is further completed by using the Otsu multi-threshold algorithm based on particle swarm optimization(PSO)algorithm,which not only makes the defect area in the image more obvious,but also shortens the scratch detection time on the chip surface.Compared with the direct use of the Otsu algorithm,the scratch detection time on the chip surface is reduced from seconds to milliseconds,and the chip quality detection efficiency has been improved greatly.It is shown that the method has important reference value for the development and application of software systems for chip detection equipment.
作者
张若楠
沐超
张固
刘小勤
ZHANG Ruonan;MU Chao;ZHANG Gu;LIU Xiaoqin(Key Laboratory of Atmospheric Optics,Anhui Institute of Optics and Fine Mechanics,Chinese Academy of Sciences,Hefei 230031,China;University of Science and Technology of China,Hefei 230026,China)
出处
《大气与环境光学学报》
CAS
CSCD
2019年第4期313-320,共8页
Journal of Atmospheric and Environmental Optics
基金
中国科学院战略性先导科技专项,XDB05040300~~
关键词
方形扁平无引脚封装芯片
划痕检测
多阈值分割
粒子群优化算法
quad flat no-lead package chip
scratch detection
multi-threshold segmentation
particle swarm optimization algorithm
作者简介
张若楠(1992-),女,陕西省渭南市人,研究生,主要从事计算机应用技术方向的研究.E-mail:zm@mail.ustc.edu.cn;导师:刘小勤(1967-),女,安徽省合肥市人,研究员,博士,主要从事激光大气传输和大气光学探测方面的研究.E-mail:xqliu@aiofm.ac.cn
