AI Detection of Body Defects and Corrosion on Leads in Electronic Components, and a study of their Occurrence

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 component, 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. The effect of body defects and corrosion in the soldering leads on the reliability of the component bond to the board is reviewed. 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 presented AI algorithm. It is 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 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 for 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, and poor storage or handling conditions.