Image analysis methods for solderball inspection in integrated circuit manufacturing

Machine vision methods are presented for the analysis of solder balls in integrated circuits. The algorithms are founded on counter fitting using a multiparameter Hough transform and on polynomial-classifier-based pattern recognition. The first method is used to show the complexity of the inspection problem, especially in the presence of high-precision requirements. In this connection, it is shown that subpixel accuracy is not obtainable even under the assumption of a perfect camera system which determines the resolution necessary for the measurement of a given maximum-volume distortion. The second method is carried out by computing a large number of features on the original image after individual solder balls are segmented by a projection technique. This approach can be considered as a control-free image segmentation paradigm, since it does not rely on properly sequencing several image-analysis modules. Further experimentation with a large pool of defective solder balls is necessary to confirm the applicability of these machine vision algorithms to a real-world manufacturing inspection systems. A general image-segmentation architecture is proposed, which enables the computation of the necessary low-level image features as well as pixel classification at video-rate speed