SolVisionCase Study
MLCC Defect Detection Using AI
Customer
The client is a prominent global player in the electronics industry, specializing in the production of advanced components for various applications. With a focus on high-quality manufacturing processes, they provide essential components used across industries, including telecommunications, automotive, and consumer electronics.
Case
MLCC Production Quality Control
Multilayer ceramic capacitors (MLCCs) are essential for controlling current and voltage in modern electronics, offering reliability, high-frequency performance, and cost-effectiveness. Used in devices like computers, mobile phones, and radar systems, MLCC production involves precise steps to ensure dimensional accuracy, shape consistency, and uniform electrode distribution. Even minor deviations can affect performance and reliability, making strict quality control crucial.
Challenge
Manual Inspection Limitations in MLCC Production
The customer’s MLCC production relied on manual inspection, prone to inconsistency from human error and fatigue. Critical defects, such as dimensional inaccuracies, micro-cracks, and misaligned electrodes, were often missed or falsely rejected. As production scaled, the limitations of manual checks became clearer. While adopting AI for defect detection, the initial implementation faced a high false rejection rate, increasing costs and disrupting efficiency. This highlighted the need for a more accurate, scalable AI inspection solution.
Solution
Precision MLCC Inspection with AI
SolVision AI inspection technology enhances MLCC defect detection by using deep learning and instance segmentation to accurately identify dimensional inconsistencies, surface defects, and misaligned electrodes. The system requires minimal training data, allowing quick adaptation and rapid deployment. Each MLCC is inspected in milliseconds, ensuring high-speed, continuous production. Real-time defect analysis provides actionable insights to optimize processes, reducing defects, improving product consistency, and increasing production efficiency.
MLCC Defect Detection
