Rigorous EUV absorber model for the mask modeling with deep learning techniques

Abstract

The mask 3D effect is important even for deep ultraviolet lithography. After the wavelength becomes shorter in extreme ultraviolet (EUV) regime, it becomes even more important. We also need to consider the asymmetric effect as well as the shadow effects now. To model these effects correctly, it is critical to compute the electromagnetic near field around the EUV absorbers correctly. Though FDTD, FEM, and RCWA methods have been applied to do so, we are here trying to combine the FEM method with deep learning techniques to achieve a better computational competence in the speed and accuracy. We only compute the one-dimensional (1D) situation with TE type incident wave. With parts of the near field signal just below the absorber computed by the FEM method, 1D patch generative adversarial network (GAN) technique is used to learn the paired mapping between the distribution of the near field below the absorber and the geometry of the mask absorber. The scattering model of the EUV absorbers obtained this way can be combined with the reflector model afterward to form the whole EUV mask model.