| Summary: | 博士 === 元智大學 === 工業工程研究所 === 89 === The aim of this study is to exploit automatic visual approaches for detecting the boundary defects such as open, short, mousebite, and spur on Ball Grid Array (BGA) substrate conduct paths. Two proposed approaches are respectively based on the detection of local deviations of path boundaries using covariance matrix and 1-D wavelet transform. In the first approach, boundary defects are detected by a boundary-based corner detection method using eigenvalues of the covariance matrix obtained from the coordinates of neighboring boundary points. Detected defects are then classified by discrimination rules derived from variation patterns of eigenvalues and the geometrical shape of each defect type. Experimental results achieve 100% correct identification for BGA substrate boundary defects under a sufficient image resolution.
In the second approach, the 2-D boundaries of BGA substrate conduct paths are initially represented by a 1-D tangent curve. The tangent angles are 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 1-D wavelet transform can decompose the 1-D tangent curve and capture irregular angle variations. Boundary defects can be located easily by evaluating the wavelet coefficients of the 1-D tangent curve in its high-pass decomposition. Experimental results also show 100% correct identification for numerous defective BGA substrate samples by selecting appropriate wavelet basis, decomposition level and image resolution.
Both defect detection approaches proposed in this study are invariant with respect to the orientation of BGA substrates, and do not require pre-stored templates for matching. These methods are suitable for inspecting various types of BGA substrates in small batch production because precise alignment of BGA substrates and the prestored templates are not required.
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