With the introduction of the multi-beam mask writing (MBMW) technology, efficient processing and precise patterning of curvilinear mask shapes are becoming increasingly important due to the wafer lithography advantages associated with the shapes. However, as the complexity of the curvilinear mask shapes increases, it becomes difficult to precisely characterize the curvilinear mask shapes. Barrier to this is prediction and reflection of the nature of curvilinear mask shapes. Therefore, in the industry, a novel algorithm method for accurate patterning is a major concern. In this study, we discuss the status of curvilinear mask shapes and patterning technology. By adopting machine learning, we develop a novel algorithm with considering the nature of curvilinear mask shapes. To evaluate practical use and accuracy of model, we demonstrate that the algorithm has significant value to guarantee the mask critical dimension (CD).
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