3D Rigorous Simulation of Defective Masks Used for EUV Lithography Via Machine Learning-Based Calibration

This study proposes a fast simulation method that employs machine learning-based parameter calibration for three-dimensional (3D) rigorous simulation of defective masks in extreme ultraviolet lithography. The parameters of the structure-decomposed fast simulation model for defective mask diffraction arc calibrated using machine learning methods, such as random forest and K-nearest neighbors, to improve the simulation accuracy and adaptivity. Herein, rigorous simulation is used as a benchmark standard for the calibration of model parameters. Simulation results of 50 validation contact masks set randomly reveal that the average simulation accuracy of aerial images is increased by 159 after calibration; both calibrated and uncalibrated fast models display better simulation accuracy (improved by 1.3 and 8.7 times, respectively) compared with an advanced single-surface approximation model. By applying defect-compensation simulation to a mask of 11-nm period, the simulation speed of single diffraction of the corrected fast model is 97 times faster than that of the rigorous simulation when the simulation results arc consistent (error is 0.8 nm).