Multibeam mask writers(MBMW) have been rapidly occupying on the field of leading edge EUV mask patterning for last several years. Thanks to outstanding ability of MBMW characteristics, sophisticated mask patterns and higher local pattern fidelity with low sensitivity E-beam resist can be realized in EUV era. Now most mask makers want to make good use of MBMW as a standard of making high-end grade masks such as Memory, Logic chips and etc. For this reason, they require higher pattern accuracy, faster writing time, higher data handling efficiency and matured machine stability aiming for the innovative mask making environment. Moreover, Larger coverage is needed as well not only for Low/High-NA EUV masks but also for even ARF masks.
In this paper, we touch key items with regard to comprehensive requirements from the mass production's point of view, for the versatile machines, several works and challenges to overcome on MBMW will be discussed.
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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