Detecting Corrosion to Prevent Cracks in MLCCs with AI

The electronics industry faces a challenge posed by cracks in multilayer ceramic capacitors (MLCC), which can undermine device reliability and longevity. In this study, we investigate the multifaceted factors underpinning crack formation, unveiling their intimate connections with corrosion, contamination, and mold. We show that hygroscopic properties, humidity exposure, and ion migration, play a role as precursors triggering both the inception and escalation of cracks. The correlation between corrosion, contamination, and cracking mechanisms in MLCCs presents a unique opportunity, as the visibility of corrosion and contamination on the component’s exterior offers a distinct advantage for detection, unlike the elusive nature of cracks which are often challenging to identify. We introduce a solution—an encompassing visual inspection methodology designed to detect corrosion evidence on electronic components. This approach employs advanced AI algorithms and pick-and-place machine cameras already in-place to examine all components during assembly. The algorithm detects corrosion indicators, effectively neutralizing the detrimental effects of corrosion and mitigating its potential role in crack formation. Our work includes the presentation of the AI model, which showcases exceptional accuracy in identifying corrosion-associated concerns. This innovative tool is directly confronting a major root cause of cracks. This novel solution marks a substantial stride toward fortifying product reliability and extending the operational lifespan of electronic devices.