Wavelet-based approach for ball grid array (BGA) substrate conduct paths inspection

The aim was to detect boundary defects such as open, short, mousebite and spur on ball grid array (BGA) substrate conduct paths using machine vision. The 2-D boundaries of BGA substrate conduct paths are initially represented by the 1-D tangent curve. The tangent angles were evaluated from the eigenvector of a covariance matrix constructed by the boundary coordinates over a small boundary segment. Since defective regions of boundaries result in irregular tangent variations, the wavelet transform was used to decompose the 1-D tangent curve and capture the irregular angle variations. A boundary defect can then be easily located by evaluating the wavelet coefficients of the 1-D tangent curve in its high-pass decomposition. The proposed method is invariant with respect to the rotation of the BGA substrates and does not require prestored templates for matching. Real BGA substrates with various boundary defects were 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 by selecting appropriate wavelet basis and decomposition level.