Deep learning-based detection of mask rule check violations in curvilinear mask

In recent years, Curvilinear Mask technology has emerged as a next-generation resolution enhancement method for photomasks, providing optimal margins by maximizing the degree of freedom in pattern design. However, this technology presents challenges in defining layout design rule limit based solely on geometric information rules based solely on geometric information such as width, space, and corner-to-corner. With the introduction of Multi Beam Mask Writers for Curvilinear pattern production, brand-new violations of Mask Rule Check(MRC) have occurred, which cannot be explained by geometry terms alone. In this study we propose a deep learning-based method for detecting MRC violations using the state-of-the-art Transformer architecture, drawing an analogy between the vertices of curvilinear patterns and words in natural language. The proposed MRC binary classifier demonstrates improved performance compared to traditional rule-based MRC and CNN-based MRC methods. Importantly, our method exhibits robustness in recall, ensuring that defective patterns are not misclassified as normal, preventing missed defects. Moreover, by employing attention maps to visualize deep learning results, we gain explainability and reveal that MRC violations may arise from issues related to the fabrication of specific designs, rather than being solely attributable to geometric features. This insight contributes to a better understanding of the challenges associated with Curvilinear Mask technology and offers an effective solution for detecting MRC violations.