用神经网络加速极紫外光刻掩模衍射模拟，速度快且精度高。
Physics-Informed Neural Systems for the Simulation of EUV Electromagnetic Wave Diffraction from a Lithography Mask
- 提出混合波导神经算子，用神经网络替代计算耗时部分。
- 在13.5纳米和11.2纳米波长下，精度媲美传统数值求解器。
- 对未见参数仍具强泛化能力，适合光刻掩模设计优化。
针对当代光刻掩模的极紫外（EUV）电磁波衍射问题，提出物理信息神经网络（PINNs）与神经算子（NOs）。引入一种新型混合波导神经算子（WGNO），基于波导法并用神经网络替代其中最耗时的组件。为评估性能，将PINNs与NOs的精度和推理时间与现代数值求解器在具有已知解析解的问题上进行对比。重点研究13.5纳米和11.2纳米波长下的解的准确性。在真实2D与3D掩模上的数值实验表明，PINNs与神经算子达到竞争性精度并显著降低预测时间，所提出的WGNO架构达到当前最优水平。该神经算子表现出显著的泛化能力，对于训练数据中未见的参数，其解的精度仍接近训练参数下的表现。这些结果为下一代光刻掩模的设计与优化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 contemporary lithography masks are presented. A novel hybrid Waveguide Neural Operator (WGNO) is introduced, based on a waveguide method with its most computationally expensive components replaced by a neural network. To evaluate performance, the accuracy and inference time of PINNs and NOs are compared against modern numerical solvers for a series of problems with known exact solutions. The emphasis is placed on investigation of solution accuracy by considered artificial neural systems for 13.5 nm and 11.2 nm wavelengths. Numerical experiments on realistic 2D and 3D masks demonstrate that PINNs and neural operators achieve competitive accuracy and significantly reduced prediction times, with the proposed WGNO architecture reaching state-of-the-art performance. The presented neural operator has pronounced generalizing properties, meaning that for unseen problem parameters it delivers a solution accuracy close to that for parameters seen in the training dataset. These results provide a highly efficient solution for accelerating the design and optimization workflows of next-generation lithography masks.
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