Preventing Corrosion-Related Failures in Electronic Assembly: A Multicase Study Analysis

Abstract

Corrosion is a prevalent failure mode in electronic products. The initiation of failure often stems from pre-existing corrosion contamination on soldering terminations prior to assembly. This corrosion is further accelerated by environmental factors such as humidity, temperature, and acidity, ultimately leading to degradation of the board and failure during both post-assembly testing and the product’s lifespan. This study presents a method for the real-time, early detection of corrosion contamination on electronic components during the mounting process using pick-and-place technology. The method utilizes the correlation between light reflectance from soldering terminations during placement photography and the degree of corrosion present. Corroded terminations possess a rougher surface and pitting spots which result in different light reflectance compared to pristine terminations. This difference can be detected through AI forensic analysis of component images. The study presents an AI model that correlates termination finish with corrosion content and progression, and evaluates its performance on large-scale data. This study also presents a real-world case where corroded components were identified during the pick-and-place process, but later failed during in-circuit testing (ICT). The post-failure analysis, using scanning electron microscopy/energy-dispersive spectroscopy (SEM/EDS) and cross-section analysis, confirms the accuracy of the AI failure predictions on multiple components with corrosion, during large-scale production. The proposed method has been implemented in multiple production lines, where it inspects all components without compromising throughput, and identifies contaminated components that are unsafe. The method has been tested on over 3.5 billion components, and has achieved an accuracy rate of over 99.5% in its predictions.