Solder bump/ball technology has been extensively applied in microelectronic packaging industry. However, the size of solder balls/bumps as well as the pitch are getting smaller and smaller, conventional inspection techniques are insufficient for diagnosis of the defect. It is indispensable to explore new methods for solder joint inspection. In this paper, a nondestructive diagnosis system based on active thermography was proposed. The test vehicles, named as SFA1 and SFA2, were excited by the laser pulse, and the consequent thermal response of the packages was captured by a thermal imager. In order to improve the signal-to-noise ratio, the polynomial fit and differential absolute contrast techniques were utilized to reconstruct the thermal images. Then, the statistical features corresponding to each solder ball were extracted from the reconstructed thermal images, and used for clustering analysis with K-means algorithm. The results show that all the solder balls were recognized accurately, which demonstrates that the intelligent system using active thermography and K-means algorithm is effective for defects inspection in microelectronic packaging industry.

中文翻译：

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使用主动热成像技术和 K-Means 算法检测微型焊球


焊料凸块/球技术已广泛应用于微电子封装行业。然而，焊球/凸块的尺寸以及间距越来越小，传统的检测技术不足以诊断缺陷。探索新的焊点检测方法势在必行。本文提出了一种基于主动热成像的无损诊断系统。名为 SFA1 和 SFA2 的测试车辆由激光脉冲激发，热成像仪捕获封装随之产生的热响应。为了提高信噪比，采用多项式拟合和微分绝对对比度技术来重建热图像。然后，从重建的热图像中提取每个焊球对应的统计特征，并使用K-means算法进行聚类分析。结果表明，所有焊球均被准确识别，这表明采用主动热成像和K-means算法的智能系统对于微电子封装行业的缺陷检测是有效的。