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

January 2022

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


AI-Detection of Body Defects and Corrosion— A large-scale evaluation of the quality of electronic components at the time of the electronic board
assembly is presented. The quality of components and soldering leads is a strong indicator of the authenticity of a component, as counterfeit components are often recycled or old. 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. This review examines the impact of body defects and corrosion in the soldering leads on the reliability of the component bond to the board. This study presents a machine learning method that detects body defects and provides evidence of corrosion on soldering leads.


The presented AI algorithm inspected over 11 million component images.  The analysis reveals that out of a million components, 290 exhibited body visual defects that conventional AOI methods were unable to detect.  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.


Electronics manufacturing does not leave quality to chance.At every stage along the production process
parts and materials are tightly inspected and controlled before allowing them to proceed to the next check-point [1]. Nevertheless, the quality chain is as strong as its weakest link.
Electronic components are the most expensive part of the Bill of Materials (BOM). Nevertheless, conventional
production methods don’t perform systematic inspection, and seldom any tests are being done at all [4]. This is despite the fact that electronic components are the vehicle of most of the failures in electronic products [5]. The unsafe components may be counterfeit, recycled, remarked, copies, cloned, etc. However, there may also be external defects, temperature, or moisture exposure affected during handling and transport [6].


AI-Detection of Body Defects and Corrosion

Unsafe components are more common now because of the shortage of electronic components that have made the supply chain more elaborate, complex, and sensitive to fraudulent
manipulations [7]. The conventional method to mitigate unsafe components
is by procuring components from authorized distributors and thus to trusting the outgoing inspection of the components by
the original components manufacturer (OCM). The underlying assumption is that the ratio of defective
components after OCM outgoing inspections is negligible and the anti-counterfeiting detection techniques are optimal.





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