As wafer manufacturing shrinks size and pitch of features, and EUV lithography introduces high NA, the control of photomask pattern placement error that contributes to wafer overlay becomes a critical requirement for leading-edge devices. For sub-3nm node devices, the pattern complexity has increased and the exposure dose has also risen due to the use of low-sensitivity resist. Accordingly, to improve the pattern fidelity and reduce the exposure time, masks are manufactured using Multi-Beam Mask Writer (MBMW). As a result of analyzing the mask pattern placement error budget for the main EUV resist of sub-3nm node device, e-beam resist charging was found to be the most significant factor. This is primarily due to the inability to use a charging dissipation layer (CDL), caused by defect issues and degradation of critical dimension (CD) linearity. In this paper, we conduct an in-depth analysis of mask pattern placement errors induced by the charging effect in the MBMW and present a charging control methodology to mitigate these pattern-density-dependent errors. We test the charging effect reduction, an integrated solution of hardware and software for charging control in the MBMW, and showcase its performance for two resists. When applied to mass productions, the charging effect correction (CEC) significantly reduces mask pattern placement errors in a single cell and improves mask overlay between two critical layers aligned in an overlay alignment scheme. Ultimately, this leads to a reduction of wafer in-field overlay error.
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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