The manufacturing of semiconductor wafers is a complex process that is prone to defects. This paper introduces DefectClassifierX, an automated defects pattern classification system that uses a convolutional neural network model based on the ResNet-152 architecture. The main objective of the approach is to improve defect classification in semiconductor manufacturing, which precisely classifies wafer defect patterns. To validate the approach taken, several experiments were performed using a dataset named “WM-300K+ wafer map [single and mixed]”. For single and mixed, the dataset contained 36 different defect patterns. The performance measures extracted from the developed model demonstrated truly outstanding accuracy with precision, recall, and f1 score of 0.9. These results reflect an exceptional average classification accuracy of 97.74% in both single and mixed defect types and outperforms previous studies in wafer defect pattern classification. It is expected to offer a giant leap in increasing the efficiency and effectiveness of wafer defect analysis in semiconductor manufacturing.