Detection and segmentation framework for defect detection on multi-layer ceramic capacitors

Abstract

Detecting defective multi-layer ceramic capacitors (MLCCs) during the inspection stage is a crucial production task to effectively manage production yield and maintain quality. However, this task presents two challenges: the necessity of pixel-level segmentation in high-resolution images and unexplored defect patterns. To address these challenges, this paper introduces an MLCC defect-detection framework based on deep learning with an MLCC dataset we constructed and a comprehensive analysis of MLCC images. Our framework employs an object-detection model to identify dielectric regions in input MLCC images, followed by a semantic segmentation model to create dielectric masks for calculating the margin ratio. This approach follows the traditional inspection process but can be performed without specialized personnel. Furthermore, we generated pseudo-defect images using generative adversarial networks to obtain sufficient training data. Experiments demonstrate the effectiveness of our framework, which achieved a defect-detection accuracy of 93.1%, as revealed by an in-depth error analysis.