EUV mask near-field synthesis

Due to the extreme short wavelength of EUV source compared to the size of pattern features, the 3D mask effects of the EUV mask is significant. The Oblique incidence of the Chief Ray and reflective nature of the optical system make the EUV optical model more complex. Thus, the Aerial image calculation for EUV masks becomes a time-consuming step. This paper develops a fast EUV aerial image calculation method based on machine learning for EUV lithographic system. First, some sparse sampling points are chosen from the source plane to represent the partially coherent illumination. Then, the training libraries of EUV mask diffraction near-fields are built up for all sampling points based on a set of representative mask features. For an arbitrary EUV mask, we calculate its aerial image using the nonparametric kernel regression technique and the pre-calculated training libraries. Subsequently, a post-processing method is applied to compensate for the estimation error and improve the computational accuracy. In addition, this paper also studies the impacts of several key factors on the accuracy and efficiency of the proposed method.