Defect Classification Dataset and Algorithm for Magnetic Random Access Memory

Defect categorization is essential to product quality assurance during the production of magnetic random access memory (MRAM). Nevertheless, traditional defect detection techniques continue to face difficulties in large-scale deployments, such as a lack of labeled examples with complicated defect shapes, which results in inadequate identification accuracy. In order to overcome these problems, we create the MARMset dataset, which consists of 39,822 photos and covers 14 common defect types for MRAM defect detection and classification. Furthermore, we present a baseline framework (GAGBnet) for MRAM defect classification, including a global attention module (GAM) and an attention-guided block (AGB). Firstly, the GAM is introduced to enhance the model’s feature extraction capability. Secondly, inspired by the feature enhancement strategy, the AGB is designed to incorporate an attention-guided mechanism during feature fusion to remove redundant information and focus on critical features. Finally, the experimental results show that the average accuracy rate of this method on the MARMset reaches 92.90%. In addition, we test on the NEU-CLS dataset to evaluate cross-dataset generalization, achieving an average accuracy of 98.60%.