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Defect detection and classification in semiconductor wafers has received an increasing attention from both industry and academia alike. Wafer defects are a serious problem that could cause massive losses to the companies' yield. The defects occur as a result of a lengthy and complex fabrication process involving hundreds of stages, and they can create unique patterns. If these patterns were to be identified and classified correctly, then the root of the fabrication problem can be recognized and eventually resolved. Machine learning (ML) techniques have been widely accepted and are well suited for such classification-/identification problems. However, none of the existing ML model's performance exceeds 96% in identification accuracy for such tasks. In this paper, we develop a state-of-the-art classifying algorithm using multiple ML techniques, relying on a general-regression-network-based consensus learning …