In Electron Beam (EB) exposure for Extreme Ultraviolet (EUV) masks, it is well known that the backscattering behavior differs significantly from conventional photomasks due to their film structure. In particular, short-range scattering derived from the Mo/Si multilayer film increases, causing the resist film to be more strongly affected within narrower range. To compensate for the error in Critical Dimension (CD), Proximity Effect Correction (PEC) in EB writer must be aware of this short-range scattering. While PEC calibration is typically done using the expertise of skilled engineers, adjusting the parameters of the multi-gaussian model significantly increases the burden on engineers due to the complexity of the phenomenon. In this paper, we introduce a method that automates the procedure of PEC parameter optimization by applying Mask Process Correction (MPC) model calibration techniques and providing feedback on backscattering components from empirically fitted model. Through demonstration of exposure experiments, we confirmed that accurate PEC optimization can be achieved by calibrating the MPC model using well-designed gauge patterns and exposure conditions.
Since the design nodes gradually decreased and EUV production became reality, the data volume is continuously increasing due to Hard OPC & Flare Correction. Multi-Beam Mask Writers (MBMW) enabled mask exposures with curvilinear and circle pattern that have not been possible before. This soon led to an increase in the number of vertexes of design data and an increase in Mask Data Preparation turnaround times (MDP TAT). A data flow based on the newly developed MBW-2 file format was developed jointly with Nippon Control System and IMS nanofabrication and significantly improved MDP TAT. The effect was confirmed by verifying it with actual data using large-volume data and curvilinear data EUV masks exposed on MBMW. In addition, the MDP TAT was further improved by studying file write method. In this paper, we introduce the concept and application of the new data flow. Furthermore we will present the results on TAT and output file sizes. Finally, we will discuss each step in the data flow in detail.
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