3 October 1994 Visual flip chip location for automatic electronic assembly
Author Affiliations +
Proceedings Volume 2347, Machine Vision Applications, Architectures, and Systems Integration III; (1994); doi: 10.1117/12.188721
Event: Photonics for Industrial Applications, 1994, Boston, MA, United States
Direct attachment of semiconductor die to circuit substrates enables the manufacture of smaller sized, high performance electronic packages than has been historically possible. In the preferred 'flip chip' assembly process, the die and the substrate connect through a set of solder bumps on the die. The pattern of these bumps must align accurately with the corresponding attachment sites on the substrate. For reasons to be discussed direct determination of the bump pattern location is required for quality assembly. We present a new and robust method for accurately locating the solder bump pattern directly. Individual solder bumps are isolated from the background and each other using vector correlation (or generalized Hough transform) image segmentation. A two-tier classification process aligns the sample's representation with blueprint vector models. These vectors represent the individual bumps in the test die and the blueprint. The die location is referenced through two arbitrary and convenient anchor points in the model. The displacement vectors between each bump model (class) and anchor points are determined. After classification each sample bump is collapsed through two rotated vectors, the vectors being those of the corresponding class to the two anchor points. The result yields two clusters of points whose centers are viewed as the anchor points for the sample bump pattern. Correspondence of those points in the sample and blueprint spaces yields the desired location.
© (1994) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Fereydoun Maali, Leonard J. Poch, George Hickok, "Visual flip chip location for automatic electronic assembly", Proc. SPIE 2347, Machine Vision Applications, Architectures, and Systems Integration III, (3 October 1994); doi: 10.1117/12.188721; https://doi.org/10.1117/12.188721


Image segmentation




Statistical modeling


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