Data driven predictive model to compact a production stop-on-fail test set for an electronic device

Abstract

Decision Tree is a popular machine learning algorithm used for fault detection and classification in the industry. In this paper, the modelling technique is used to compact a production test set defined for quality assurance of an electronic asset. The novelty of this work is in the proposed method that builds in an iterative way decision trees until an accurate predictive model that meets classification accuracy target in a stop-on-fail test scenario. Generated test data is characterized with missing values which is a major challenge to the traditional use of decision trees. The developed computational procedure handles this application-specific data attribute. Exemplary results show that the method is able to significantly reduce a production test set with parametric and non-parametric tests, and generate a truthful prognostic model. In addition, the method is computationally efficient and easy to implement. It could also be combined with another test compaction strategies such as variables association analysis. Furthermore, the method proposed offers the flexibility of exploring the trade-off between the number of removed tests from the production test set and the prediction accuracy. The results can enable production costs reduction without impacting quality detection accuracy. The paper details and provides discussions on the advantages and limitations of the proposed algorithm