Abstract
The aim of this paper is to locate and classify boundary defects such as open and short circuits, mousebites, and spurs on ball grid array (BGA) substrate conducting paths using machine vision. Boundary defects are detected by a boundary-based corner detection method using covariance matrix eigenvalues. Detected defects are then classified by discrimination rules derived from variation patterns of eigenvalues and the geometrical shape of each defect type. Real BGA substrates with both synthetic and real boundary defects are used as test samples to evaluate the performance of the proposed method. Experimental results show that the proposed method achieves 100% correct identification for BGA substrate boundary defects under a sufficient image resolution. The proposed method is invariant with respect to the orientation of the BGA substrates, and it does not require prestored templates for matching. This method is suitable for various types of BGA substrate in small-batch production because precise positioning of BGA substrates and the prestored templates are not necessary.
Published Version
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