【摘 要】
：
半导体芯片表面缺陷的存在直接影响到芯片的运行效能及寿命,因此本文提出针对QFN(Quad Flat No-lead,方形扁平无引脚)芯片塑封表面缺陷的快速视觉检测与分类方法,实现塑
【出 处】
：
2017全国第十三届精密工程学术研讨会 暨精密机械分会理事会七届一次会议
论文部分内容阅读
半导体芯片表面缺陷的存在直接影响到芯片的运行效能及寿命,因此本文提出针对QFN(Quad Flat No-lead,方形扁平无引脚)芯片塑封表面缺陷的快速视觉检测与分类方法,实现塑封表面擦痕、划痕、孔洞、缺损、异物等缺陷的快速检测与分类。首先,针对单幅QFN 塑封图像,采用广义反向粒子群与引力搜索混合算法[1]优化基于灰度-梯度共生矩阵的二维最大熵求解过程,对塑封图像进行有效二维阈值分割；其次,提出基于数学形态学的缺陷区域提取方法,获得塑封缺陷区域灰度图像,以此提取缺陷的几何、灰度、纹理共计24 个特征；再次,采用支持向量机递归特征消除SVM-RFE 算法[2]对缺陷特征本进行特征选择,获得缺陷样本的最优特征子集；最后,提出基于高斯核支持向量机的塑封表面缺陷分类方法,采用广义反向粒子群与引力搜索混合算法对支持向量机参数进行寻优,对塑封表面的5 类缺陷进行有效分类。
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