Defect Detection in Patterned Wafers Using Dynamic Local Adaptive Sensitivity Adjustment

In the defect detection of patterned wafers, optical images captured by inspection systems are affected by various noises, resulting in low signal-to-noise ratios in the obtained images, making the detection task extremely challenging. Especially at the nanoscale pattern dimensions, optical image differences formed by different wafer patterns are significant. Designing an automatic algorithm to achieve good detection results for various defect types and wafer patterns is difficult. This article presents a novel dynamic local adaptive (DLA) sensitivity adjustment framework, which represents the first application of context-aware local region generation and pattern-based local threshold adjustment in semiconductor defect detection. DLA introduces a dual adaptive mechanism—dynamic window sizing and noise-aware threshold adjustment—that can be seamlessly integrated with existing thresholding frameworks, achieving state-of-the-art performance without hardware upgrades. DLA is validated on the data of a standard wafer designed by SIOK Technology Company Ltd. to demonstrate the proposed method’s effectiveness. Additionally, this method is compared with other well-known methods in defect detection. Experimental results show that the proposed method is adaptable, can achieve better detection results, and lower false nuisance rates for various defect types.