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

Corrosion is a prevalent failure mode in electronic products. The initiation of failure often stems from preexisting 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 the degradation of the board and failure during both postassembly 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 (PNP) 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 artificial inteligence (AI) forensic analysis of component images. This 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 PNP process, but later failed during in-circuit testing (ICT). The postfailure analysis, using scanning electron microscopy/energy-dispersive spectroscopy (SEM/EDS) and cross-sectional 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.