Method for Profile Reconstruction of Phase Defects in Extreme Ultraviolet Lithography Mask

This paper proposed a new method for profile reconstruction of phase defects in extreme ultraviolet lithography mask multilayer films. Three-dimensional profiles of phase defects were characterized using the top and bottom profile parameters. The top profile parameters of defects were measured using an atomic force microscope. Moreover, Fourier ptychography technology was used to retrieve the complex amplitudes of aerial images of the defected mask blanks. Using deep learning models, the bottom profile parameter reconstruction model of defects was constructed by determining the relationship between the amplitudes/phases of aerial images and the bottom profile parameters of defects. The deep learning models used herein include a convolutional neural network and multilayer perceptron. The bottom profile parameters of defects can be reconstructed from the amplitudes/phases of the aerial images using the trained models. The simulation results show that the trained models can accurately reconstruct the bottom profile parameters of phase defects. The root-mean-square errors of bottom full-width-halfmaximum reconstruction results of bump and pit defects arc 0.51 and 0.13 nm, respectively. The root-mean-square errors of bottom height reconstruction results arc 3.35 and 1.73 nm, respectively. The proposed method is immune to the deposition conditions because it captures aerial images as an information carrier.