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Image analysis methods for solderball inspection in integrated circuit manufacturing

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3 Author(s)
Blanz, W.E. ; IBM Almaden Res. Center, San Jose, CA, USA ; Sanz, J.L.C. ; Hinkle, E.B.

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

Published in:

Robotics and Automation, IEEE Journal of  (Volume:4 ,  Issue: 2 )

Date of Publication:

Apr 1988

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