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 the layout design rule limits 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, a distinct set of defects that are difficult to pre-detect by conventional mask rule check have occurred, as these cannot be explained by geometry terms alone. In this study, we propose a deep learning-based mask check method, named mask deep check (MDC) for pre-detect defects in inspection. The proposed vector graphics transformer (VGT) uses the state-of-the-art transformer architecture, drawing an analogy between the vertices of curvilinear patterns and words in natural language. We demonstrate improved performance of VGT-based MDC compared to a traditional rule-based approach and a convolutional neural network-based MDC method. Importantly, VGT exhibits robustness in recall, ensuring that defective patterns are not misclassified as normal, thereby preventing missed defects. Moreover, by employing attention maps to visualize VGT results, we gain explainability and reveal that mask defects may arise from issues related to the fabrication of specific designs, rather than being solely attributable to geometric features. VGT-based MDC contributes to a better understanding of the challenges associated with curvilinear mask technology and offers an effective solution for detecting mask defects.
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.
Patterning, a major process in semiconductor manufacturing, aims to transfer the design layout to the wafer. Accordingly, the "process proximity correction" method was developed to overcome the difference in after-cleaninginspected CD (critical dimension) between patterns of similar shapes. However, its physical model is often limited in the predictive performance. Therefore, recent studies have introduced ML (machine learning) technology to supplement model accuracy, but this approach often has an inherent risk of overfitting depending on the type of sampled pattern. In this study, we present a newly invented flow capable of stable etch-process-aware ML modeling by model reconstruction and large amounts of measurement data. The new modeling flow can also be performed within a reasonable runtime through efficient feature extraction. Based on the new model and its related layout targeting platform, intensive improvements were made to CD targeting and spread; for a given layout, in comparison with delicate rule-based modification, the CD targeting accuracy was improved by 4 times and approaches the limit of metrology error.
Accurate prediction of Jacobian is essential for multi-variable optical proximity correction (OPC). The Jacobian means the small variation of edge placement error (EPE) induced by small mask bias of nearby segments under optical proximity effects. If the Jacobian can be accurately calculated, it is helpful for OPC iteration reduction, or EPE improvement for 2D shape mask patterns. Moreover, if this can be cost-effective, this approach can be easily extended to Full chip level. We changed expensive Jacobian matrix procedure into simple ML based Jacobian model inference. Thanks to efficiently chosen geometric and optical features and light ANN structure, our method can predict Jacobian 76% faster and 81% more accurate than intensity distribution function method. We also improved mask optimization algorithm by inserting small gradient iterations. Our mask optimization solver was 2 times faster than vanilla mask optimization solver. Through this effort, we constructed fast and accurate machine learning assisted mask optimization solver.
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