Li Ziqi, Jing Xuyu, Dong Lisong, Ma Xu, Wei Yayi
Appl Opt. 2023 Sep 1;62(25):6561-6570. doi: 10.1364/AO.499361.
Calculating the diffraction near field (DNF) of a three-dimensional (3D) mask is a key problem in the extreme ultraviolet (EUV) lithography imaging modeling. This paper proposes a fast DNF model of an EUV mask based on the asymmetric patch data fitting method. Due to the asymmetric imaging characteristics of the EUV lithography system, a DNF library is built up including the training mask patches posed in different orientations and their rigorous DNF results. These training patches include some representative local mask features such as the convex corners, concave corners, and edge segments in four directions. Then, a convolution-based compact model is developed to rapidly simulate the DNFs of 3D masks, where the convolution kernels are inversely calculated to fit all of the training data. Finally, the proposed model is verified by simulation experiments. Compared to a state-of-the-art EUV mask model based on machine learning, the proposed method can further reduce the computation time by 60%-70% and roughly obtain the same simulation accuracy.
计算三维（3D）掩膜的衍射近场（DNF）是极紫外（EUV）光刻成像建模中的一个关键问题。本文提出了一种基于非对称面片数据拟合方法的EUV掩膜快速DNF模型。由于EUV光刻系统的非对称成像特性，构建了一个DNF库，其中包括以不同方向摆放的训练掩膜面片及其精确的DNF结果。这些训练面片包括一些具有代表性的局部掩膜特征，如凸角、凹角以及四个方向的边缘段。然后，开发了一种基于卷积的紧凑模型来快速模拟3D掩膜的DNF，其中卷积核通过反向计算以拟合所有训练数据。最后，通过仿真实验对所提出的模型进行了验证。与基于机器学习的先进EUV掩膜模型相比，所提方法可进一步将计算时间减少60%-70%，并大致获得相同的仿真精度。
