arXiv:2607. 25330v1 Announce Type: cross Abstract: We present a physics-informed neural operator (PINO) trained with pseudo-spectral frequency-domain (PSFD) equations for electromagnetic (EM) scattering problems in EUV lithography.
By Doyun Kim, Werner Gillijns
The paper proposes an all‑reflective two‑mirror projection system for EUV lithography that achieves a 4× demagnification at a numerical aperture close to unity (NA≈0.993). Unlike conventional EUV objectives that use 6–10 aspheric mirrors and have <15 % throughput, the design uses a fixed two‑reflection path for each accepted diffraction order, retaining 50–60 % of the power and eliminating order‑dependent phase shifts. The authors optimize 30‑bilayer Bragg coatings for each mirror facet, formulate a 3‑D vector model for a two‑dimensionally periodic mask, and use inverse lithography with a differentiable modal solver to demonstrate simulated sub‑10‑nm aerial images with resolved peaks up to 5 nm defocus.
By Vasiliy A. Es'kin, Egor V. Ivanov, Olga V. Martynova
arXiv:2606. 28119v1 Announce Type: cross Abstract: We introduce a physics-constrained neural network (PCNN) for the rapid prediction of rigorous coupled-wave analysis (RCWA) outputs in the form of Jones matrices.
By Eric Prehn, Peter Jung
arXiv:2606. 26713v1 Announce Type: new Abstract: As semiconductor technology nodes scale, computational lithography is essential for ensuring yield and performance.
By Yuqi Jiang, Yumeng Liu, Zimu Li, Jinyuan Deng, Qian Jin, Yucheng Cui, Yu Li, Xunzhao Yin, Qi Sun, Cheng Zhuo
arXiv:2606. 00228v1 Announce Type: new Abstract: In semiconductor manufacturing, lithography projects circuit layouts onto silicon wafers through an optical mask.
By Yao Lai, Xuyuan Xiong, Zeyue Xue, Guojin Chen, Jing Wang, Xihui Liu, Rui Zhang, Robert Mullins, Bei Yu, Ping Luo
The paper presents a conditional diffusion framework for the inverse design of dielectric resonator metasurfaces based on target angular scattering patterns. Trained on T‑matrix simulated geometry‑response pairs, the model learns a distribution of feasible geometries, allowing multiple candidate designs for the ill‑posed inverse problem. The best generated metasurface achieves a mean percentage error of 1.39%, outperforming CMA‑ES optimization and deterministic neural baselines, and can be inferred in about one minute after training.
By M. Tsukerman, K. Grotov, D. Vovchuk, P. Ginzburg