Application of Machine Learning for Quality Risk Factor Analysis of Electronic Assemblies

The rapid identification of contributing factors to failures in a high-volume electronic product manufacturing environment is critical to reduce disruption to production and mitigate potential quality and reliability risks. As system complexity and component usage continues to increase, it is becoming more and more challenging to manually process the large volume of data that are continuously generated by production processes. In this paper, we utilize various machine learning (ML) techniques to classify components on the printed circuit board assemblies (PCBAs) as defective or non-defective based on an input feature map including features like the date the component is manufactured, the side of board on which the component is placed, the location of the component on the board, etc. We then implement a feature importance algorithm to detect the underlying cause of the component failure. Three ML models including support vector machine, random forest, and neural network are trained and implemented for feature importance analysis using a dataset obtained from over 10 million components on various PCBA boards. Due to the intrinsic characteristics of the dataset, such as a significant imbalance between defective and non-defective cases, pre-processing techniques such as upsampling and downsampling are necessary to increase the performance of the models. The results show that all the developed ML models can achieve more than 99% accuracy. Finally, we show that our proposed feature importance approach is capable of correctly identifying the main cause of defects for given components.