用神经网络加速光刻掩模的极紫外波衍射模拟
Physics-informed neural networks and neural operators for a study of EUV electromagnetic wave diffraction from a lithography mask
- 用神经算子替代传统波导法中耗时部分
- 2D/3D真实掩模测试中精度与速度均达顶尖水平
- 适合光刻设计优化和半导体制造领域研究者
本文提出物理信息神经网络（PINNs）与神经算子（NOs），用于求解极紫外（EUV）电磁波在光刻掩模上的衍射问题。引入一种新型混合波导神经算子（WGNO），其基于波导方法，将其中计算量最大的部分替换为神经网络。在真实二维和三维掩模上的数值实验表明，WGNO在精度和推理时间上均达到当前最优水平，为光刻掩模设计流程提供了高效解决方案。
原文摘要 · Abstract (English)
Physics-informed neural networks (PINNs) and neural operators (NOs) for solving the problem of diffraction of Extreme Ultraviolet (EUV) electromagnetic waves from a mask are presented. A novel hybrid Waveguide Neural Operator (WGNO) is introduced, which is based on a waveguide method with its most computationally expensive part replaced by a neural network. Numerical experiments on realistic 2D and 3D masks show that the WGNO achieves state-of-the-art accuracy and inference time, providing a highly efficient solution for accelerating the design workflows of lithography masks.
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