Development of Visual Inspection System for Detecting Surface Defects on Sensor Chip

This paper presents a visual inspection method based on image processing techniques. The method aims to detect surface defects found on pixel chip pads. Pixel chip is a tiny sensor used in Inner Tracking System (ITS) - a large particle detector of ALICE experiment (A Large Ion Collider Experiment). The chips record particle trajectories after collision event in the ITS system. In a mass production stage, the chip quality needs to be accurately inspected and assessed to ensure its technical specification satisfies. The chips are manufactured to build up the ITS system. Considering this large scale production, an inspection system based on imaging techniques is applied to provide fast and accurate chip surface assessment. This paper proposes a method to assess the quality of surface pad by using image processing techniques. The method consists of three main steps. Firstly, K-Means clustering is used to segment the surface pad into clean and defect areas. In the second step, the defect areas are extracted by applying Gabor filter. The last step is conducted by applying Canny Edge filter to detect surface defects on chip pad area. Experimental results show that the proposed method can significantly achieve high accuracy of 84.9% and recall 77.9%.