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Revolutionizing Electronic Component Reliability: Detecting Corrosion to Prevent Cracks with AI

December 17, 2023

The electronics industry’s rapid evolution has increased the demand for multilayer ceramic capacitors (MLCCs), indispensable components in electronic devices and circuits. However, the industry faces a critical challenge – the occurrence of cracks in MLCCs, which can jeopardize device reliability and longevity. 

The newest study by E. Weiss, published in ASM International in November 2023, explores the complex relationship between corrosion, contamination, and crack formation in MLCCs. 

The article introduces an innovative solution that employs advanced artificial intelligence (AI) algorithms for visual inspection during the assembly process, aiming to detect corrosion evidence and mitigate crack-related issues.

The Significance of MLCCs in Modern Electronics

MLCCs play a pivotal role in electronic assemblies, becoming increasingly crucial with the rising adoption of electric vehicles and sustainable energy solutions. 

The automotive industry, in particular, relies heavily on MLCCs, with electric vehicles incorporating over 10,000 MLCCs per vehicle. The exponential increase in MLCC usage emphasizes the need to address potential issues, such as cracks, that could impede their functionality and overall reliability.

The Challenge of Cracks in MLCCs

Cracks in MLCCs have far-reaching consequences, affecting performance, lifespan, and reliability. These cracks can manifest internally or externally during various stages of the component’s life cycle – from manufacturing to assembly and operational use. While manufacturers have implemented measures to reduce manufacturing-related cracks, challenges persist in addressing environmental stressors and mechanical wear that can lead to cracks during the operational lifespan of electronic devices.


Corrosion, Contamination, and Crack Formation

The article highlights the intricate factors contributing to crack formation in MLCCs, emphasizing the correlation between corrosion, contamination, and mold. Factors such as hygroscopic properties, humidity exposure, and ion migration are identified as precursors to cracks. 

Corrosion is pinpointed as a critical player, gradually degrading materials and creating vulnerabilities that pave the way for crack initiation and propagation.

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Innovative Solution: AI-Driven Visual Inspection

The core of the proposed solution lies in an encompassing visual inspection methodology that utilizes advanced AI algorithms. Integrated with pick-and-place machine cameras during assembly, this solution aims to detect corrosion indicators on electronic components. 

Unlike cracks, which are often challenging to identify, corrosion and contamination on the component’s exterior provide a visible advantage for detection. The AI model showcased in the article exhibits exceptional accuracy in identifying corrosion-associated concerns, presenting a revolutionary tool for confronting a significant root cause of cracks in MLCCs.


Weiss’s research offers a comprehensive understanding of the factors influencing crack formation in MLCCs and introduces an innovative AI-driven solution for corrosion detection during the assembly process. This tool marks a substantial step toward fortifying product reliability, extending the operational lifespan of electronic devices, and advancing the electronic component manufacturing industry. 

As technology continues to evolve, the integration of AI in quality assurance practices promises a future where cracks in MLCCs can be effectively mitigated, ensuring the continued progress and reliability of electronic devices across various industries.

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