Model-based Mask Process Correction (MPC) is an indispensable data processing step for producing masks for advanced wafer production nodes. Typically, calibration of an MPC model may require several thousands SEM measurements. However, due to metrology tool-time constraints, there is an increasing requirement to minimize the number of measurements for building an MPC model, without impacting the model quality significantly. This work presents the development of a down-sampling approach using a combination of sensitivity analysis and clustering to reduce a larger set of diverse measurement locations to a smaller subset according to the metrology budget of photomask engineers.
Advanced photomasks exploit complex patterns that show little resemblance to the target printed wafer
pattern. The main mask pattern is modified by various OPC and SRAF features while further complexity is
introduced as source-mask-optimization (SMO) technologies experience early adoption at leading
manufacturers. The small size and irregularity of these features challenge the mask inspection process as well
as the mask manufacturing process.
The two major concerns for mask inspection and qualification efficacy of advanced masks are defect
detection and photomask inspectability. Enhanced defect detection is critical for the overall mask
manufacturing process qualification which entails characterization of the systematic deviations of the pattern.
High resolution optical conditions are the optimal solution for manufacturing process qualification as well as
a source of additional information for the mask qualification. Mask inspection using high resolution
conditions operates on an optical image that differs from the aerial image. The high resolution image closely
represents the mask plane pattern. Aerial imaging mode inspection conditions, where the optics of the
inspection tool emulates the lithography manufacturing conditions in a scanner, are the most compatible
imaging solution for photomask pattern development and hence mask inspectability. This is an optimal
environment for performing mask printability characterization and qualification.
In this paper we will compare the roles of aerial imaging and high resolution mask inspection in the mask
house.
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