Abstract
The Multi-Layer Ceramic Capacitor (MLCC) lamination inspection process is an essential step in determining whether the dielectric in an MLCC image, which allows a constant flow of current, is well stacked or not at an early stage. Nevertheless, in the conventional inspection process, the inspector manually measures the margin ratio of the dielectric in the MLCC image and then conducts a simple visual inspection. This process is time and human resources consuming. Additionally, it causes noise in the determined result because it depends on each inspector’s subjective judgment. In this paper, we propose an MLCC lamination alignment inspection system using a deep learning-based segmentation model to solve this issue. Using various experiments, we demonstrated that the proposed system can help inspectors and improve work efficiency.
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