The monitoring of bonded joints in microelectronics packaging is generally done manually and offline. However, this method is inefficient and of low precision due to limited personal sensing and experience. This article proposes a hybrid cascade approach with an improved level set and a random forest to locate and automatically classify defective joints in microelectronics packaging. A grayscale variance-based pixel neighborhood is introduced to accurately locate the joint, and an improved gray projection is used to remove the redundant nonjoint area. We have used an improved level set algorithm to segment joint defects and extract their dominant features using KPCA. Finally, a random forest is used to classify the features extracted by KPCA and determine the defect categories. The results have indicated that the grayscale variance-based pixel neighborhood could effectively locate the joint and that KPCA could identify effective joint features. The accuracy of the random forest classification has reached 0.91, offering a novel solution for joint quality monitoring in microelectronics manufacturing.

中文翻译：

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微电子接合处缺陷定位和分类的级联方法：改进的水平集和随机森林


微电子封装中粘合接头的监测通常是手动且离线完成的。但由于个人感知和经验有限，这种方法效率低、精度低。本文提出了一种混合级联方法，采用改进的水平集和随机森林来定位和自动分类微电子封装中的缺陷接头。引入基于灰度方差的像素邻域来精确定位关节，并使用改进的灰度投影来去除冗余的非关节区域。我们使用改进的水平集算法来分割接头缺陷并使用 KPCA 提取其主要特征。最后，使用随机森林对KPCA提取的特征进行分类并确定缺陷类别。结果表明，基于灰度方差的像素邻域可以有效地定位关节，KPCA可以识别有效的关节特征。随机森林分类精度达到0.91，为微电子制造联合质量监控提供了一种新颖的解决方案。