Randomized general regression network for identification of defect patterns in semiconductor wafer maps

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 model along with a powerful randomization technique. We compare our proposed method with the widely used ML models in terms of model accuracy, stability, and time complexity. Our method has proved to be more accurate and stable as compared to any of the existing algorithms reported in the literature, achieving its accuracy of 99.8%, stability of 1.128, and TBM of 15.8 s.