An advanced auto-inspection system for micro-router collapse

In this paper, we propose an auto-optical inspection (AOI) system that can inspect micro-router (router) collapse automatically. The router is a tool used to cut a printed circuit board (PCB). A few types of defects could occur in the routers and cause unexpected damage to the PCBs. Among these defects, collapse is the most critical defect that must be detected. Currently, router manufacturing companies rely on human inspectors to control the router quality. We first extract the silhouette edges and associated features (peaks and valleys) of a router's silhouette image by computer vision technique. Then, these silhouette edges and associated features are used to reconstruct a set of 2D isograms that correspond to the router surface. Finally, a pattern recognition method is devised to identify and classify some features of the pattern in the 2D isograms. In this study, two types of routers with different diameters are used for inspection experiments. There are 15 routers of each type. The experimental results reveal that the proposed AOI system can robustly and successfully detect the collapse of diamond-patterned routers with different sizes. The successful detection rate is above 96%. The proposed AOI system can assist in determining the quality of the routers.