Detection of Micro Solder Balls Using Active Thermography Technology and K-Means Algorithm

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.