Fuzzy Inference System for Interpretable Classification of Wafer Map Defect Patterns

Accurate classification of wafer map defect patterns is crucial for enhancing yield in semiconductor manufacturing. To address the problem of deep learning model over-fitting to label noise present in real industrial data, this study proposes a fuzzy logic-based framework for identifying both single and composite-type defect patterns. To demonstrate the robustness of our approach, we utilized the public dataset WM-811K and developed a Fuzzy Inference System (FIS) that leverages quantitative metrics such as the Center Zone Density (CZD). Data quality was also improved through preprocessing steps, including resolving class imbalances and refining labels via expert review. The performance of the proposed FIS was evaluated against a quantitative feature-based neural network, an FIS-neural network hybrid, and a CNN model. Experimental results showed that in single-pattern classification, the proposed FIS model achieved the highest accuracy of 99.20%, followed by the feature-based neural network (91.63%), the FIS-neural network hybrid model (88.55%), and the CNN (81.06%). These results prove that the proposed FIS approach maintains high classification accuracy while offering the advantages of interpretability and rule-based adjustability. This framework presents a practical solution that can effectively integrate domain knowledge to reduce the risk of overfitting in data environments with imperfect labels.