Phase defect characterization using generative adversarial networks for extreme ultraviolet lithography

Abstract

The multilayer defects of mask blanks in extreme ultraviolet (EUV) lithography may cause severe reflectivity deformation and phase shift. The profile information of a multilayer defect is the key factor for mask defect compensation or repair. This paper introduces an artificial neural network framework to reconstruct the profile parameters of multilayer defects in the EUV mask blanks. With the aerial images of the defective mask blanks obtained at different illumination angles and a series of generative adversarial networks, the method enables a way of multilayer defect characterization with high accuracy.

Analysis of extreme ultraviolet mask defect inspection based on complex amplitudes of the aerial images

“…At Lawrence Berkeley National Laboratory, FP has been used to reconstruct the complex amplitude of the aerial image of the mask obtained by SHARP, the accuracy of the reconstruction and the improvement of the resolution of the reconstructed image have been proved [15]. Cheng [16] and Zheng [17] et al simulate the imaging system of SHARP devices and uses FP technology to reconstruct complex amplitudes of the aerial images of the mask blank with phase defects, obtain more comprehensive information of the aerial images and accurately reconstruct three-dimensional parameters of defects. In the above studies, various factors affecting the acquisition of aerial images by SHARP are not taken into account.…”