A differential geometry-based method for detecting etching defects in high-density interconnect IC substrates

With the increasing precision and complexity of high-density interconnect integrated circuit (IC) substrates, automated visual inspection encounters significant challenges in accurately detecting etching defects on metallographic substrate images. Factors such as grayscale variations, noise interference, and rich textures further complicate the process. To address this issue, a novel detection method based on differential geometry theory is proposed, encompassing defect detection between circuits and on circuits. Firstly, the variational Chan-Vese model and morphological closing operation are employed to obtain highly accurate substrate segmentation images. For defect detection between substrate circuits, contour regions between circuits are extracted by differencing the original image with the segmented image. Next, a lightweight compressed MobileNet (CMNet) network is constructed using depth-weighted compression to rapidly identify defect regions between circuits. For defects on substrate circuits, the contour of the segmented image is utilized to determine candidate regions of etching defects by evaluating abrupt changes in angles between adjacent contour points. Subsequently, the proposed discrete curvature calculation method based on the Frenet frame of differential geometry theory is employed to detect and measure defect candidates on the circuits. Experimental results demonstrate the effectiveness of the proposed method in detecting etching defects, outperforming other advanced techniques in screening and identifying defect regions.