Automated defect recognition of C-SAM images in IC packaging using Support Vector Machines

Ultrasonic C-mode scanning acoustic microscopy (C-SAM) is widely used in the semiconductor industry for reliability testing and product inspection due to its ability to nondestructively detect defects in IC packaging. However, image interpretation and defect identification depend largely on the experience of operators, and there is no defect recognition system; this is partly due to current recognition systems, which are based on computer vision algorithms and are not robust for C-SAM images. A new robust defect recognition system and its application to C-SAM images are described in this paper. The iconic domain of two-dimensional C-SAM grey-level image analysis based on the non-linear Mumford-Shah model is used, and defect recognition is achieved through the use of Support Vector Machines (SVMs). The system is verified through experiments on a sequence of C-SAM images corrupted by synthetically generated noise, bias and different shape. The remarkable defect recognition rates achieved indicate that Support Vector Machines (SVMs) are suitable for IC package defect identification.