AI Visual Detection of Defects and Corrosion on Leads in Electronic Components

Join the CALCE/SMTA Counterfeit Parts and Materials Symposium 2022 for this and other informative presentations

Zeev Efrat

Cybord

Abstract: A large-scale evaluation of the quality of electronic components at the time of the electronic board assembly is presented. Counterfeit components are often recycled or old components. Therefore, the quality of components and the soldering leads is a good indicator of the component’s authenticity. The quality of the components is evaluated based on their visual appearance by quantifying their visual defects and the corrosion evidence as they appear on the component and its soldering leads. A machine learning method to detect body defects and evidence of corrosion on soldering leads is presented. Over 11 million components images were inspected by the AI algorithm. We have shown that 290 components out of a million had body visual defects that cannot be seen by conventional AOI. In addition, over 1,100 out of a million had visible corrosion evidence on their soldering leads. Corrosion on the soldering not only affects the production yield but is the most common cause of random statistical failures in the field resulting in products failure. The presented method allows inspection of all the components used in production thus reducing the risk of failures in the field caused by poor quality electronic components originating from counterfeit, bad storage, or handling.

Bio: With a strong passion for helping technology start-ups become profitable businesses, Zeev Efrat is an experienced CEO with a background in business development and disruptive technologies. Zeev founded and managed Frost & Sullivan’s Israel venture, launched and managed several start-ups in IoT technologies, and served as a board member for various electronic companies. He is currently the CEO at Cybord.