LAUSR

Semiconductor manufacturers aim to fabricate defect-free wafers in order to improve product quality, increase yields, and reduce costs. Typically, wafer defects form spatial patterns that provide useful information, helping to identify problems and faults during the fabrication process. Machine learning (ML) methods have been used to classify these defects in order to locate the root causes of failure. This paper proposes a novel deep-structured ML approach as an extension of our previous randomized general regression network (RGRN) model, to identify and classify both single-defect and mixed-defect patterns. The principal motivation for this paper is that a shallow-structured RGRN performs well on single-pattern defects, achieving an accuracy of 99.8%, but performs poorly when a wafer has mixed-defect patterns. The proposed approach improves RGRN performance, particularly on mixed-pattern defects, by incorporating a novel information gain (IG)-based splitter as well as deep-structured ML. A spatial filter is applied to remove random noise and reduce model bias during training. During the first detection stage, the splitter generates unique rules that are built using the IG theory and splits the defects data into single-defect and mixed-defect patterns. Single-defect patterns are then classified by RGRN, whereas mixed-defect patterns are fed into the deep-structured ML model for further classification. This combination improves the ability of the proposed approach to classify diverse defect patterns and achieve a better overall performance. Our experimental results demonstrate that the proposed approach achieves an overall detection accuracy of 86.17% on a dataset that contains real data representing both single-defect and mixed-defect patterns, as commonly found in real manufacturing scenarios, outperforming existing ML-based models.