Fast and Accurate EUVL Thick-Mask Model Based on Multi-Channel Attention Network

Abstract

Simulation of thick-mask effects is an important task in computational lithography within extreme ultraviolet (EUV) waveband. This paper proposes a fast and accurate learning-based thick-mask model dubbed multi-channel block attention network (MCBA-Net) to solve this problem for EUV lithography. The proposed MCBA-Net introduces geometric feature attention module and structural feature attention module to improve the computation accuracy of thick-mask diffraction near field. During the training process, the proposed attention modules can effectively learn the impact of the three-dimensional mask diffraction behavior. In addition, the multi-channel network architecture is used to simultaneously synthesize the thick-mask diffraction matrices under different polarization states, and the coupling between different diffraction matrices is addressed. Numerical experiments show that the proposed model improves the computational efficiency by more than 20-fold over the rigorous simulator, and reduces the prediction error by 25%~50% compared with the state-of-the-art deep learning models. In addition, the generalization ability of the proposed method is proved using a complex testing pattern.