用物理神经算子优化EUV光刻掩模，实现高精度成像。
Gradient-based inverse lithography for EUV masks via the waveguide method and a physics-informed neural operator
- 融合波导方法与物理神经算子，构建可微分的光学仿真引擎。
- 在11.2nm波长下，成功恢复出目标图案的掩模结构。
- 适用于纳米级光刻工艺优化，适合芯片制造研究人员。
提出一种基于梯度的极紫外（EUV）掩模逆光刻技术（ILT）。通过将可微分波导方法与近期提出的波导神经算子（WGNO）作为端到端物理引擎，利用完整前向衍射模型的自动微分，反演掩模吸收层的介电常数分布。针对真实二维和三维吸收层材料（TaBN、La、U）在λ=11.2 nm波长下的数值实验表明，该方法能够实现晶圆上期望光场的精确重构，验证了其在高精度掩模设计中的可行性与有效性。
原文摘要 · Abstract (English)
Gradient-based inverse lithography technology~(ILT) for extreme ultraviolet~(EUV) masks is presented. A novel framework treats the differentiable waveguide method and the recently proposed waveguide neural operator~(WGNO) as end-to-end physics engines, recovering the permittivity of the absorber of the mask through automatic differentiation of the full forward diffraction model. Numerical experiments on realistic 2D and 3D absorbers of the mask (TaBN, La, U) at $λ{=}11.2$~nm show that the considered ILT methods make it possible to obtain a mask structure that achieves the desired field on the wafer.
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