[1] Li, Z. Q. et al. Fast source mask co-optimization method for high-NA EUV lithography. Opto-Electronic Advances 7, 230235 (2024). doi: 10.29026/oea.2024.230235
[2] Cecil, T. et al. Advances in inverse lithography. ACS Photonics 10, 910-918 (2023).
[3] Roesch, M. et al. Quantitative access to phase effects in High-NA photomasks using AIMS EUV. Proceedings of SPIE 13273, 39th European Mask and Lithography Conference (EMLC 2024). Grenoble: SPIE, 2024.
[4] Erdmann, A. et al. Characterization and mitigation of 3D mask effects in extreme ultraviolet lithography. Advanced Optical Technologies 6, 187-201 (2017). doi: 10.1515/aot-2017-0019
[5] Kazazis, D. et al. Extreme ultraviolet lithography. Nature Reviews Methods Primers 4, 84 (2024). doi: 10.1038/s43586-024-00361-z
[6] Lafferty, N. et al. EUV full-chip curvilinear mask options for logic via and metal patterning. Proceedings of SPIE 12495, DTCO and Computational Patterning Ⅱ. San Jose: SPIE, 2023.
[7] Hooker, K. et al. Curvilinear mask solutions for full-chip EUV lithography. Proceedings of SPIE 12054, Novel Patterning Technologies 2022. San Jose: SPIE, 2022.
[8] Pang, L. & Fujimura, A. Why the mask world is moving to curvilinear. Journal of Micro/Nanopatterning, Materials, and Metrology 23, 041503 (2024).
[9] Zhang, Z. N. et al. Fast rigorous mask model for extreme ultraviolet lithography. Applied Optics 59, 7376-7389 (2020). doi: 10.1364/AO.399323
[10] Lam, M. Adam, K. & Neureuther, A. R. Domain decomposition methods for simulation of printing and inspection of phase defects. Proceedings of SPIE 5040, Optical Microlithography XVI. Santa Clara: SPIE, 2003.
[11] Liu, P. et al. Fast 3D thick mask model for full-chip EUVL simulations. Proceedings of SPIE 8679, Extreme Ultraviolet (EUV) Lithography IV. San Jose: SPIE, 2013.
[12] Yee, K. Numerical solution of initial boundary value problems involving Maxwell's equations in isotropic media. IEEE Transactions on Antennas and Propagation 14, 302-307 (1966). doi: 10.1109/TAP.1966.1138693
[13] Teixeira, F. L. et al. Finite-difference time-domain methods. Nature Reviews Methods Primers 3, 75 (2023). doi: 10.1038/s43586-023-00257-4
[14] Fühner, T. et al. Dr.LiTHO: a development and research lithography simulator. Proceedings of SPIE 6520, Optical Microlithography XX. San Jose: SPIE, 2007.
[15] He, P. X. et al. EUV mask model based on modified Born series. Optics Express 31, 27797-27809 (2023). doi: 10.1364/OE.498260
[16] Osnabrugge, G. Leedumrongwatthanakun, S. & Vellekoop, I. M. A convergent Born series for solving the inhomogeneous helmholtz equation in arbitrarily large media. Journal of Computational Physics 322, 113-124 (2016). doi: 10.1016/j.jcp.2016.06.034
[17] Krüger, B. Brenner, T. & Kienle, A. Solution of the inhomogeneous Maxwell’s equations using a Born series. Optics Express 25, 25165-25182 (2017). doi: 10.1364/OE.25.025165
[18] He, P. X. et al. Modified Born series with virtual absorbing boundary enabling large-scale electromagnetic simulation. Communications Physics 7, 383 (2024). doi: 10.1038/s42005-024-01882-5
[19] Levinson, H. J. Extreme Ultraviolet Lithography. (Bellingham: SPIE, 2020).
[20] Hopkins, H. H. On the diffraction theory of optical images. Proceedings of the Royal Society of London. Series A. Mathematical, Physical and Engineering Sciences 217, 408-432 (1953).
[21] Johnson, S. Oskooi, A. & Taflove, A. Advances in FDTD Computational Electrodynamics: Photonics and Nanotechnology. (Boston: Artech, 2013).
[22] Adam, K. et al. Application of the hybrid Hopkins–Abbe method in full-chip OPC. Microelectronic Engineering 86, 492-496 (2009). doi: 10.1016/j.mee.2008.11.100
[23] Guo, S. P. et al. Mask3D-compatible full-vectorial Hopkins imaging for lithographic modeling. Optica 12, 924-934 (2025). doi: 10.1364/OPTICA.565511
[24] Horisaki, R. et al. Compressive propagation with coherence. Optics Letters 47, 613-616 (2022). doi: 10.1364/OL.444772
[25] Igarashi, T. Naruse, M. & Horisaki, R. Incoherent diffractive optical elements for extendable field-of-view imaging. Optics Express 31, 31369-31382 (2023). doi: 10.1364/OE.499866
[26] Yang, Y. X. et al. Advancements and challenges in inverse lithography technology: a review of artificial intelligence-based approaches. Light: Science & Applications 14, 250 (2025). doi: 10.1038/s41377-025-01923-w
[27] Yu, C. Z. & Ma, X. Thick-mask model based on multi-channel U-Net for EUV lithography. Proceedings of SPIE 12495, DTCO and Computational Patterning Ⅱ. San Jose: SPIE, 2023.
[28] Zhang, J. B. & Ma, X. Fast diffraction model of lithography mask based on improved pixel-to-pixel generative adversarial network. Optics Express 31, 24437-24452 (2023). doi: 10.1364/OE.489770
[29] Medvedev, V. Erdmann, A. & Rosskopf, A. 3D EUV mask simulator based on physics-informed neural networks: effects of polarization and illumination. Proceedings of SPIE 13023, Computational Optics 2024. Strasbourg: SPIE, 2024.
[30] Zheng, X. Q. et al. Model-informed deep learning for computational lithography with partially coherent illumination. Optics Express 28, 39475-39491 (2020). doi: 10.1364/OE.413721
[31] Zhu, B. W. et al. L2O-ILT: learning to optimize inverse lithography techniques. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems 43, 944-955 (2024). doi: 10.1109/TCAD.2023.3323164
[32] Zhang, Z. N. et al. Source mask optimization for extreme-ultraviolet lithography based on thick mask model and social learning particle swarm optimization algorithm. Optics Express 29, 5448-5465 (2021). doi: 10.1364/OE.418242
[33] Wu, R. X. et al. Compensation of EUV lithography mask blank defect based on an advanced genetic algorithm. Optics Express 29, 28872-28885 (2021). doi: 10.1364/OE.434787
[34] He, P. X. et al. Linearized EUV mask optimization based on the adjoint method. Optics Express 32, 8415-8424 (2024). doi: 10.1364/OE.517783
[35] Pang, L. Y. et al. Study of mask and wafer co-design that utilizes a new extreme SIMD approach to computing in memory manufacturing: full-chip curvilinear ILT in a day. Proceedings of SPIE 11148, Photomask Technology 2019. Monterey: SPIE, 2019.
[36] Lin, B. J. Image formation. in Optical Lithography: Here is Why 2nd edn (ed Lin, B. J.) (Bellingham: SPIE, 2021).
[37] Neim, L. Smith, B. W. & Fenger, G. EUV mask polarization effects on sub-7nm node imaging. Proceedings of SPIE 11323, Extreme Ultraviolet (EUV) Lithography XI. San Jose: SPIE, 2020.
[38] Fujimura, A. Choi, Y. & Shendre, A. Curvilinear masks overview: manufacturable mask shapes are more reliably manufacturable. Journal of Micro/Nanopatterning, Materials, and Metrology 23, 041502 (2024). doi: 10.1117/1.jmm.23.4.041502
[39] Mackay, T. G. & Lakhtakia, A. Bianisotropic slab with planar interfaces, in The Transfer-Matrix Method in Electromagnetics and Optics (eds Mackay, T. G. & Lakhtakia, A.) (Cham: Springer, 2020), 33-49.
[40] Yu, C. Z. Ma, X. & Zhang, J. B. Mask 3D model based on complex-valued convolution neural network for EUV lithography. Proceedings of 2022 International Workshop on Advanced Patterning Solutions (IWAPS). Beijing: IEEE, 2022, 1-4.
[41] Kanmaz, T. B. et al. Deep-learning-enabled electromagnetic near-field prediction and inverse design of metasurfaces. Optica 10, 1373-1382 (2023). doi: 10.1364/OPTICA.498211
[42] Wiecha, P. R. & Muskens, O. L. Deep learning meets nanophotonics: a generalized accurate predictor for near fields and far fields of arbitrary 3d nanostructures. Nano Letters 20, 329-338 (2020). doi: 10.1021/acs.nanolett.9b03971
[43] Khaireh-Walieh, A. et al. A newcomer’s guide to deep learning for inverse design in nano-photonics. Nanophotonics 12, 4387-4414 (2023). doi: 10.1515/nanoph-2023-0527
[44] Philipsen, V. et al. Actinic characterization and modeling of the EUV mask stack. Proceedings of SPIE 8886, 29th European Mask and Lithography Conference. Dresden: SPIE, 2013.
[45] Poonawala, A. & Milanfar, P. Mask design for optical microlithography—an inverse imaging problem. IEEE Transactions on Image Processing 16, 774-788 (2007). doi: 10.1109/TIP.2006.891332
[46] Ma, X. et al. Gradient-based source mask optimization for extreme ultraviolet lithography. IEEE Transactions on Computational Imaging 5, 120-135 (2019). doi: 10.1109/TCI.2018.2880342
[47] Georgieva, N. K. et al. Feasible adjoint sensitivity technique for EM design optimization. IEEE Transactions on Microwave Theory and Techniques 50, 2751-2758 (2002). doi: 10.1109/TMTT.2002.805131
[48] Takahata, Y. et al. Study of mask error enhancement factor improvement with low-n absorber extreme ultraviolet lithography mask. Journal of Micro/Nanopatterning, Materials, and Metrology 23, 044401 (2024). doi: 10.1117/1.jmm.23.4.044401
[49] Osher, S. & Sethian, J. A. Fronts propagating with curvature-dependent speed: Algorithms based on hamilton-jacobi formulations. Journal of Computational Physics 79, 12-49 (1988). doi: 10.1016/0021-9991(88)90002-2
[50] Pang, L. Inverse lithography technology: 30 years from concept to practical, full-chip reality. Journal of Micro/Nanopatterning, Materials, and Metrology 20, 030901 (2021). doi: 10.1117/1.jmm.20.3.030901
[51] Choi, M. et al. Realization of high-performance optical metasurfaces over a large area: a review from a design perspective. npj Nanophotonics 1, 31 (2024). doi: 10.1038/s44310-024-00029-2
[52] Phan, T. et al. High-efficiency, large-area, topology-optimized metasurfaces. Light: Science & Applications 8, 48 (2019). doi: 10.1038/s41377-019-0159-5
[53] Pestourie, R. et al. Inverse design of large-area metasurfaces. Optics Express 26, 33732-33747 (2018). doi: 10.1364/OE.26.033732
[54] Bogaerts, W. et al. Programmable photonic circuits. Nature 586, 207-216 (2020). doi: 10.1038/s41586-020-2764-0
[55] Harris, N. C. et al. Large-scale quantum photonic circuits in silicon. Nanophotonics 5, 456-468 (2016).
[56] Su, Y. K. et al. Scalability of large-scale photonic integrated circuits. ACS Photonics 10, 2020-2030 (2023). doi: 10.1021/acsphotonics.2c01529
[57] Liu, P. et al. Fast and accurate 3D mask model for full-chip OPC and verification. Proceedings of SPIE 6520, Optical Microlithography XX. San Jose: SPIE, 2007.
[58] Bao, Y. J. et al. Efficient gradient-based metasurface optimization toward the limits of wavelength-polarization multiplexing. Nano Letters 25, 6340-6347 (2025). doi: 10.1021/acs.nanolett.5c01292
[59] Xu, Y. et al. Gradient-descent optimization of metasurfaces based on one deep-enhanced rsenet. Chinese Optics Letters 23, 083601 (2025). doi: 10.3788/COL202523.083601
[60] Peng, D. P. et al. Toward a consistent and accurate approach to modeling projection optics. Proceedings of SPIE 7640, Optical Microlithography XXⅢ. San Jose: SPIE, 2010.
[61] Liu, J. M. et al. Quasi-visualizable detection of deep sub-wavelength defects in patterned wafers by breaking the optical form birefringence. International Journal of Extreme Manufacturing 7, 015601 (2025). doi: 10.1088/2631-7990/ad870e
[62] Mu, C. et al. Efficient resist modeling and calibration using a Wiener–Padé formulation and convex optimizations. Optics & Laser Technology 189, 113022 (2025). doi: 10.1016/j.optlastec.2025.113022
[63] Mu, C. X. et al. Efficient nonlinear resist modeling by combining and cascading quadratic Wiener systems. Optics & Laser Technology 183, 112315 (2025). doi: 10.1016/j.optlastec.2024.112315
[64] Brent, R. P. Algorithms for minimization without derivatives. (Mineola: Dover Publications, 2013).
[65] Mitchell, I. M. The flexible, extensible and efficient toolbox of level set methods. Journal of Scientific Computing 35, 300-329 (2008). doi: 10.1007/s10915-007-9174-4
[66] Yu, Z. Y. et al. A GPU-enabled level-set method for mask optimization. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems 42, 594-605 (2023). doi: 10.1109/TCAD.2022.3175939
[67] Recla, M. & Schmitt, M. Improving deep learning-based height estimation from single SAR images by injecting sensor parameters. Proceedings of IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium, Pasadena: IEEE, 2023, 1806-1809.