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.
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