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Transforming Inverse Lithography: Overcoming Challenges with AI-Driven Innovations


  • Lukashchuk, A. et al. Photonic-electronic integrated circuit-based coherent LiDAR engine. Nat. Commun. 15, 3134 (2024).

    ADS 

    Google Scholar
     

  • Li, X. K. et al. High-efficiency reinforcement learning with hybrid architecture photonic integrated circuit. Nat. Commun. 15, 1044 (2024).

    ADS 

    Google Scholar
     

  • Wang, Y. B. et al. Photonic-circuit-integrated titanium: sapphire laser. Nat. Photonics 17, 338–345 (2023).

    ADS 

    Google Scholar
     

  • El Helou, C. et al. Mechanical integrated circuit materials. Nature 608, 699–703 (2022).

    ADS 

    Google Scholar
     

  • Yang, Y. X. et al. A non-printed integrated-circuit textile for wireless theranostics. Nat. Commun. 12, 4876 (2021).

    ADS 

    Google Scholar
     

  • Liu, Y. et al. A photonic integrated circuit-based erbium-doped amplifier. Science 376, 1309–1313 (2022).

    ADS 

    Google Scholar
     

  • Moore, G. E. Progress in digital integrated electronics. In Proc. 1975 IEEE International Electron Devices Meeting. 11–13 (IEEE, Washington, D.C., USA,1975).

  • Huang, L. Y. et al. Sub-wavelength patterned pulse laser lithography for efficient fabrication of large-area metasurfaces. Nat. Commun. 13, 5823 (2022).

    ADS 

    Google Scholar
     

  • Liu, T. Q. et al. Ultrahigh-printing-speed photoresists for additive manufacturing. Nat. Nanotechnol. 19, 51–57 (2024).

    ADS 

    Google Scholar
     

  • Tian, X. L. et al. Crosslinking-induced patterning of MOFs by direct photo-and electron-beam lithography. Nat. Commun. 15, 2920 (2024).

    ADS 

    Google Scholar
     

  • Qin, N. et al. 3D electron-beam writing at sub-15 nm resolution using spider silk as a resist. Nat. Commun. 12, 5133 (2021).

    ADS 
    MathSciNet 

    Google Scholar
     

  • Zhu, C. X. et al. Electron beam lithography on nonplanar and irregular surfaces. Microsyst. Nanoeng. 10, 52 (2024).

    ADS 

    Google Scholar
     

  • Liu, X. Q. et al. Biomimetic sapphire windows enabled by inside-out femtosecond laser deep-scribing. PhotoniX 3, 1 (2022).

  • Garcia, R., Knoll, A. W. & Riedo, E. Advanced scanning probe lithography. Nat. Nanotechnol. 9, 577–587 (2014).

    ADS 

    Google Scholar
     

  • Kim, G. et al. Binary-state scanning probe microscopy for parallel imaging. Nat. Commun. 13, 1438 (2022).

    ADS 

    Google Scholar
     

  • Kudryashov, S. I. et al. Direct femtosecond-laser writing of optical-range nanoscale metagratings/metacouplers on diamond surfaces. Appl. Phys. Lett. 115, 073102 (2019).

    ADS 

    Google Scholar
     

  • Hayasaki, Y. et al. Variable holographic femtosecond laser processing by use of a spatial light modulator. Appl. Phys. Lett. 87, 031101 (2005).

    ADS 

    Google Scholar
     

  • Somers, P. et al. Rapid, continuous projection multi-photon 3D printing enabled by spatiotemporal focusing of femtosecond pulses. Light Sci. Appl. 10, 199 (2021).

    ADS 

    Google Scholar
     

  • He, M. F. et al. Single-color peripheral photoinhibition lithography of nanophotonic structures. PhotoniX 3, 25 (2022).


    Google Scholar
     

  • Lee, Y. et al. Ultra-thin light-weight laser-induced-graphene (LIG) diffractive optics. Light Sci. Appl. 12, 146 (2023).

    ADS 

    Google Scholar
     

  • Balena, A. et al. Recent advances on high-speed and holographic two-photon direct laser writing. Adv. Funct. Mater. 33, 2211773 (2023).


    Google Scholar
     

  • Farsari, M. et al. Two-photon fabrication. Nat. Photonics 3, 450–452 (2009).

    ADS 

    Google Scholar
     

  • Saha, S. K. et al. Scalable submicrometer additive manufacturing. Science 366, 105–109 (2019).

    ADS 

    Google Scholar
     

  • Geng, Q. et al. Ultrafast multi-focus 3-D nano-fabrication based on two-photon polymerization. Nat. Commun. 10, 2179 (2019).

    ADS 

    Google Scholar
     

  • Ouyang, W. Q. et al. Ultrafast 3D nanofabrication via digital holography. Nat. Commun. 14, 1716 (2023).

    ADS 

    Google Scholar
     

  • Cao, C. et al. Ultra-high precision nano additive manufacturing of metal oxide semiconductors via multi-photon lithography. Nat. Commun. 15, 9216 (2024).


    Google Scholar
     

  • Guan, L. L. et al. Light and matter co-confined multi-photon lithography. Nat. Commun. 15, 2387 (2024).

    ADS 

    Google Scholar
     

  • Wang, L. et al. Plasmonic nano-printing: large-area nanoscale energy deposition for efficient surface texturing. Light Sci. Appl. 6, e17112 (2017).


    Google Scholar
     

  • Zou, T. T. et al. High-speed femtosecond laser plasmonic lithography and reduction of graphene oxide for anisotropic photoresponse. Light Sci. Appl. 9, 69 (2020).

    ADS 

    Google Scholar
     

  • Choi, H. et al. Realization of high aspect ratio metalenses by facile nanoimprint lithography using water-soluble stamps. PhotoniX 4, 18 (2023).


    Google Scholar
     

  • Kim, J. et al. One-step printable platform for high-efficiency metasurfaces down to the deep-ultraviolet region. Light Sci. Appl. 12, 68 (2023).

    ADS 

    Google Scholar
     

  • Zhang, B. et al. Ultrafast laser-induced self-organized nanostructuring in transparent dielectrics: fundamentals and applications. PhotoniX 4, 24 (2023).


    Google Scholar
     

  • Blaicher, M. et al. Hybrid multi-chip assembly of optical communication engines by in situ 3D nano-lithography. Light Sci. Appl. 9, 71 (2020).

    ADS 

    Google Scholar
     

  • Zhang, B. et al. Self-organized phase-transition lithography for all-inorganic photonic textures. Light Sci. Appl. 10, 93 (2021).

    ADS 

    Google Scholar
     

  • Li, Z. Q. et al. Realising high aspect ratio 10 nm feature size in laser materials processing in air at 800 nm wavelength in the far-field by creating a high purity longitudinal light field at focus. Light Sci. Appl. 11, 339 (2022).

    ADS 

    Google Scholar
     

  • Liu, Y. N. et al. Ultrafast laser one-step construction of 3D micro-/nanostructures achieving high-performance zinc metal anodes. PhotoniX 5, 6 (2024).


    Google Scholar
     

  • Li, J. Q. et al. Nanoscale multi-beam lithography of photonic crystals with ultrafast laser. Light Sci. Appl. 12, 164 (2023).

    ADS 

    Google Scholar
     

  • Gan, Z. F. et al. Spatial modulation of nanopattern dimensions by combining interference lithography and grayscale-patterned secondary exposure. Light Sci. Appl. 11, 89 (2022).

    ADS 

    Google Scholar
     

  • Wu, Q. Photolithography Process Near the Diffraction Limit (Tsinghua University Press, 2020).

  • Rayleigh, J. W. S. Investigations in optics, with special reference to the spectroscope. Lond. Edinb. Dublin Philos. Mag. J. Sci. 8, 261–274 (1879).


    Google Scholar
     

  • Levinson, H. J. Principles of Lithography. 2nd edn. (SPIE Press, 2005).

  • Markle, D. A. A new projection printer. Solid State Technol. 17, 50–53 (1974).


    Google Scholar
     

  • Wei, Y. Y. et al. Computational Lithography and Layout Optimization (Electronics Industry Press, 2021).

  • Liu, P. Mask synthesis using machine learning software and hardware platforms. Proc SPIE 11327, Optical Microlithography XXXIII (SPIE, San Jose, CA, USA, 2020).

  • Cecil, T. et al. Establishing fast, practical, full-chip ILT flows using machine learning. Proc SPIE 11327, Optical Microlithography XXXIII (SPIE, San Jose, CA, USA, 2020).

  • Shi, X. L. et al. Physics based feature vector design: a critical step towards machine learning based inverse lithography. Proc SPIE 11327, Optical Microlithography XXXIII (SPIE, San Jose, CA, USA, 2020).

  • Adam, K. et al. Using machine learning in the physical modeling of lithographic processes. Proc SPIE 10962, Design-Process-Technology Co-optimization for Manufacturability XIII (SPIE, San Jose, CA, USA, 2019).

  • Kim, Y. S. et al. OPC model accuracy study using high volume contour based gauges and deep learning on memory device. Proc SPIE 10959, Metrology, Inspection, and Process Control for Microlithography XXXIII (SPIE, San Jose, CA, USA, 2019).

  • Ma, X. et al. Fast lithography aerial image calculation method based on machine learning. Appl. Opt. 56, 6485–6495 (2017).

    ADS 

    Google Scholar
     

  • Lin, J. X. et al. Fast extreme ultraviolet lithography mask near-field calculation method based on machine learning. Appl. Opt. 59, 2829–2838 (2020).

    ADS 

    Google Scholar
     

  • Liu, P. et al. Fast 3D thick mask model for full-chip EUVL simulations. Proc SPIE 8679, Extreme Ultraviolet (EUV) Lithography IV (SPIE, San Jose, CA, USA, 2013).

  • Zhang, H. et al. 3D rigorous simulation of defective masks used for EUV lithography via machine learning-based calibration. Acta Opt. Sin. 38, 1222002 (2018).


    Google Scholar
     

  • Tanabe, H., Sato, S. & Takahashi, A. Fast 3D lithography simulation by convolutional neural network. Proc. SPIE 11518, Design-Process-Technology Co-optimization XV (SPIE, 2021).

  • Lin, J. X. et al. Fast mask near-field calculation using fully convolution network. Proc. 2020 International Workshop on Advanced Patterning Solutions (IWAPS) 1–4 (IEEE, Chengdu, China, 2020).

  • Kareem, P. & Shin, Y. Synthesis of lithography test patterns using machine learning model. IEEE Trans. Semiconduct. Manuf. 34, 49–57 (2021).


    Google Scholar
     

  • Ye, W. et al. LithoGAN: end-to-end lithography modeling with generative adversarial networks. Proc. 2019 56th ACM/IEEE Design Automation Conference 1–6 (IEEE, Las Vegas, NV, USA, 2019).

  • Ye, W. et al. TEMPO: fast mask topography effect modeling with deep learning. Proc. 2020 International Symposium on Physical Design 127–134 (ACM, Taipei, China, 2020).

  • Lan, S. et al. Deep learning assisted fast mask optimization. Proc. SPIE 10587, Optical Microlithography XXXI. (SPIE, San Jose, CA, USA, 2018).

  • Lin, J. X. et al. Learning-based compressive sensing method for EUV lithographic source optimization. Opt. Express 27, 22563–22581 (2019).

    ADS 

    Google Scholar
     

  • Lin, J. X. et al. Fast aerial image model for EUV lithography using the adjoint fully convolutional network. Opt. Express 30, 11944–11958 (2022).

    ADS 

    Google Scholar
     

  • Li, Z. Q. et al. High-precision lithography thick-mask model based on a decomposition machine learning method. Opt. Express 30, 17680–17697 (2022).

    ADS 

    Google Scholar
     

  • Li, Z. Q. et al. Decomposition-learning-based thick-mask model for partially coherent lithography system. Opt. Express 31, 20321–20337 (2023).

    ADS 

    Google Scholar
     

  • Li, Z. Q. et al. Fast diffraction model of an EUV mask based on asymmetric patch data fitting. Appl. Opt. 62, 6561–6570 (2023).

    ADS 

    Google Scholar
     

  • Li, Z. Q. et al. Fast source mask co-optimization method for high-NA EUV lithography. Opto-Electron. Adv. 7, 230235 (2024).


    Google Scholar
     

  • Xia, J. H. et al. Modeling of silicon carbide ECR etching by feed-forward neural network and its physical interpretations. IEEE Trans. Plasma Sci. 38, 1091–1096 (2010).

    ADS 

    Google Scholar
     

  • Kim, B. & Lee, B. T. Prediction of silicon oxynitride plasma etching using a generalized regression neural network. J. Appl. Phys. 98, 034912 (2005).

    ADS 

    Google Scholar
     

  • Shiraishi, M. et al. Flare modeling and calculation on EUV optics. Proc. SPIE 7636, Extreme Ultraviolet (EUV) Lithography IV (pp. 763629, SPIE, San Jose, CA, USA, 2010).

  • Shim, S., Choi, S. & Shin, Y. Machine learning (ML)-based lithography optimizations. Proc. 2016 IEEE Asia Pacific Conference on Circuits and Systems (APCCAS) 530–533 (IEEE, Jeju, Korea (South), 2016).

  • Shim, S. & Shin, Y. Etch proximity correction through machine-learning-driven etch bias model. Proc. SPIE 9782, Advanced Etch Technology for Nanopatterning V (SPIE, San Jose, CA, USA, 2016).

  • Chen, R. et al. ETCH model based on machine learning. Proc. 2020 China Semiconductor Technology International Conference (CSTIC) 1–4 (IEEE, Shanghai, China, 2020).

  • Pan, Y. H. et al. Efficient informatics-based source and mask optimization for optical lithography. Appl. Opt. 60, 8307–8315 (2021).

    ADS 

    Google Scholar
     

  • Ma, X. et al. Fast pixel-based optical proximity correction based on nonparametric kernel regression. J. Micro/Nanolithogr. MEMS MOEMS 13, 043007 (2014).

    ADS 

    Google Scholar
     

  • Luo, K. S. et al. SVM based layout retargeting for fast and regularized inverse lithography. J. Zhejiang Univ. Sci. C. 15, 390–400 (2014).


    Google Scholar
     

  • Luo, R. Optical proximity correction using a multilayer perceptron neural network. J. Opt. 15, 075708 (2013).

    ADS 

    Google Scholar
     

  • Yang, H. Y. et al. Imbalance aware lithography hotspot detection: a deep learning approach. J. Micro/Nanolithogr. MEMS MOEMS 16, 033504 (2017).

    ADS 

    Google Scholar
     

  • Yang, H. Y. et al. GAN-OPC: mask optimization with lithography-guided generative adversarial nets. Proc. 55th Annual Design Automation Conference 131 (IEEE, San Francisco, CA, USA, 2018).

  • Sim, W. et al. Automatic correction of lithography hotspots with a deep generative model. SPIE Adv. Lithogr. 10961, 1096105 (2019).


    Google Scholar
     

  • Zhu, J. Y. et al. Unpaired image-to-image translation using cycle-consistent adversarial networks. Proc. 2017 IEEE International Conference on Computer Vision 2242–2251 (IEEE, Venice, Italy, 2017).

  • Zhang, Y. J. & Ye, W. J. Deep learning–based inverse method for layout design. Struct. Multidiscip. Optim. 60, 527–536 (2019).

    MathSciNet 

    Google Scholar
     

  • Zhang, S. G. et al. Fast optical proximity correction based on graph convolution network. Proc. SPIE 11613, Optical Microlithography XXXIV (SPIE, 2021).

  • Ma, X. et al. Model-driven convolution neural network for inverse lithography. Opt. Express 26, 32565–32584 (2018).

    ADS 

    Google Scholar
     

  • Ma, X. & Arce, G. R. Generalized inverse lithography methods for phase-shifting mask design. Opt. Express 15, 15066–15079 (2007).

    ADS 

    Google Scholar
     

  • Zheng, X. Q. et al. Model-informed deep learning for computational lithography with partially coherent illumination. Opt. Express 28, 39475–39491 (2020).

    ADS 

    Google Scholar
     

  • Ma, X., Zheng, X. Q. & Arce, G. R. Fast inverse lithography based on dual-channel model-driven deep learning. Opt. Express 28, 20404–20421 (2020).

    ADS 

    Google Scholar
     

  • Wei, Y. Y. Advanced Lithography Theory and Application of VLSI (Science Press, 2016).

  • Schellenberg, F. M. A history of resolution enhancement technology. Opt. Rev. 12, 83–89 (2005).


    Google Scholar
     

  • Shibuya, M. Resolution enhancement techniques for optical lithography and optical imaging theory. Opt. Rev. 4, 151–160 (1997).


    Google Scholar
     

  • Shi, W. J. et al. Computational lithography technology under chip manufacture context. Laser Optoelectron. Prog. 59, 0922001 (2022).


    Google Scholar
     

  • Ma, X. & Arce, G. R. Computational Lithography. (John Wiley & Sons, Hoboken, 2011).


    Google Scholar
     

  • Shi, R. et al. The selection and creation of the rules in rules-based optical proximity correction. Proc. ASICON 2001 4th International Conference on ASIC Proceedings 50–53 (IEEE, Shanghai, China, 2001).

  • Cobb, N. B., Zakhor, A. & Miloslavsky, E. A. Mathematical and CAD framework for proximity correction. Proc. SPIE 2726, Optical Microlithography IX 208–222 (SPIE, Santa Clara, CA, USA, 1996).

  • Liu, A. C. & Lin, B. J. A study of projected optical images for typical IC mask patterns illuminated by partially coherent light. IEEE Trans. Electron Devices 30, 1251–1263 (1983).

    ADS 

    Google Scholar
     

  • Yong, L., Zakhor, A. & Zuniga, M. A. Computer-aided phase shift mask design with reduced complexity. IEEE Trans. Semicond. Manuf. 9, 170–181 (1996).


    Google Scholar
     

  • Lucas, K. D. et al. Model-based OPC for first-generation 193-nm lithography. Proc. SPIE 4346, Optical Microlithography XIV 119–130 (SPIE, Santa Clara, CA, USA, 2001).

  • Hsu, S. et al. An innovative source-mask co-optimization (SMO) method for extending low k1 imaging. Proc. SPIE 7140, Lithography Asia 2008 220–229 (SPIE, Taipei, China, 2008).

  • Tolani, V. et al. Source-mask co-optimization (SMO) using level set methods. Proc. SPIE 7488, Photomask Technology 2009 (SPIE, Monterey, CA, USA, 2009).

  • Chiu, M. C. et al. Challenges of 29nm half-pitch NAND Flash STI patterning with 193nm dry lithography and self-aligned double patterning. Proc. SPIE 7140, Lithography Asia 2008 (SPIE, Taipei, China, 2008).

  • Tsai, M. C. et al. Full-chip source and mask optimization. Proc. SPIE 7973, Optical Microlithography XXIV (SPIE, San Jose, CA, USA, 2011).

  • Saleh, B. E. A. & Sayegh, S. I. Reduction of errors of microphotographic reproductions by optimal corrections of original masks. Opt. Eng. 20, 205781 (1981).


    Google Scholar
     

  • Pang, L. Y. Inverse lithography technology: 30 years from concept to practical, full-chip reality. J. Micro/Nanopattern. Mater. Metrol. 20, 030901 (2021).

    ADS 

    Google Scholar
     

  • Pang, L. et al. TrueMask ILT MWCO: full-chip curvilinear ILT in a day and full mask multi-beam and VSB writing in 12 h for 193i. Proc. SPIE 11327, Optical Microlithography XXXIII (SPIE, San Jose, CA, USA).

  • Nashold, K. M. & Saleh, B. E. A. Image construction through diffraction-limited high-contrast imaging systems: an iterative approach. J. Opt. Soc. Am. A 2, 635–643 (1985).

    ADS 

    Google Scholar
     

  • Liu, Y. & Zakho, A. Binary and phase-shifting image design for optical lithography. Proc. SPIE 1463, Optical/Laser Microlithography IV (pp. 382–399. SPIE, San Jose, CA, USA, 1991).


    Google Scholar
     

  • Rosenbluth, A. et al. Optimum mask and source patterns to print a given shape. J. Micro/Nanolithogr. MEMS MOEMS 1, 13–30 (2002).

    ADS 

    Google Scholar
     

  • Wang, Y. T. et al. Automated design of halftoned double-exposure phase-shifting masks. Proc. SPIE 2440, Optical/Laser Microlithography VIII 290–301 (SPIE, Santa Clara, CA, USA, 1995).

  • Jang, S. H. et al. Manufacturability evaluation of model-based OPC masks. Proc. SPIE 4889, 22nd Annual BACUS Symposium on Photomask Technology 520–529 (SPIE, Monterey, CA, USA, 2002).

  • Fuhner, T. & Erdmann, A. Improved mask and source representations for automatic optimization of lithographic process conditions using a genetic algorithm. Proc. SPIE 5754, Optical Microlithography XVIII 415–426 (SPIE, San Jose, CA, USA, 2005).

  • Lin, B. J. Immersion lithography and its impact on semiconductor manufacturing. Proc. SPIE 5377, Optical Microlithography XVII 46–67 (SPIE, Santa Clara, CA, USA, 2004).

  • Osher, S. & Sethian, J. A. Fronts propagating with curvature-dependent speed: algorithms based on Hamilton-Jacobi formulations. J. Comput. Phys. 79, 12–49 (1988).

    ADS 
    MathSciNet 

    Google Scholar
     

  • Abrams, D. & Pang, L. Y. Fast inverse lithography technology. Proc. SPIE 6154, Optical Microlithography XIX (SPIE, San Jose, CA, USA, 2006).

  • Hung, C. Y. et al. Pushing the lithography limit: applying inverse lithography technology (ILT) at the 65nm generation. Proc. SPIE 6154, Optical Microlithography XIX (SPIE, San Jose, CA, USA, 2006).

  • Lin, B. et al. Inverse lithography technology at chip scale. Proc. SPIE 6154, Optical Microlithography XIX (pp. 615414. SPIE, San Jose, CA, USA, 2006).


    Google Scholar
     

  • Granik, Y. Fast pixel-based mask optimization for inverse lithography. J. Micro/Nanolithogr., MEMS, MOEMS 5, 043002 (2006).

    ADS 

    Google Scholar
     

  • Borodovsky, Y. et al. Pixelated phase mask as novel lithography RET. Proceedings of SPIE 6924, Optical Microlithography XXI (SPIE, San Jose, CA, USA, 2008).

  • Singh, V. et al. Making a trillion pixels dance. Proceedings of SPIE 6924, Optical Microlithography XXI (SPIE, San Jose, CA, USA, 2008).

  • Torunoglu, I. et al. A GPU-based full-chip inverse lithography solution for random patterns. Proceedings of SPIE 7641, Design for Manufacturability through Design-Process Integration IV (SPIE, San Jose, CA, USA, 2010).

  • Ma, X. & Arce, G. R. Generalized inverse lithography methods for phase-shifting mask design. Proceedings of SPIE 6520, SPIE Advanced Lithography (SPIE, San Jose, CA, USA, 2007).

  • Ma, X. & Arce, G. Binary mask optimization for inverse lithography with partially coherent illumination. J. Opt. Soc. Am. A 25, 2960–2970 (2008).

    ADS 

    Google Scholar
     

  • Maa, X. & Arce, G. PSM design for inverse lithography with partially coherent illumination. Opt. Express 16, 20126–20141 (2008).

    ADS 

    Google Scholar
     

  • Zhang, J. Y. et al. A highly efficient optimization algorithm for pixel manipulation in inverse lithography technique. Proceedings of 2008 IEEE/ACM International Conference on Computer-Aided Design 480–487 (SPIE, San Jose, CA, 2008).

  • Shen, S. H., Yu, P. & Pan, D. Z. Enhanced DCT2-based inverse mask synthesis with initial SRAF insertion. Proceedings of SPIE 7122, Photomask Technology 2008 (SPIE, Monterey, CA, USA, 2008).

  • Yang, Y. W., Shi, Z. & Shen, S. H. Seamless-merging-oriented parallel inverse lithography technology. J. Semiconduct. 30, 106002 (2009).

    ADS 

    Google Scholar
     

  • Lam, E. Y. & Wong, A. K. K. Computation lithography: virtual reality and virtual virtuality. Opt. Express 17, 12259–12268 (2009).

    ADS 

    Google Scholar
     

  • Jia, N. N. & Lam, E. Y. Machine learning for inverse lithography: using stochastic gradient descent for robust photomask synthesis. J. Opt. 12, 045601 (2010).

    ADS 

    Google Scholar
     

  • Lv, W., Xia, Q. & Liu, S. Y. Mask-filtering-based inverse lithography. J. Micro/Nanolithogr. MEMS MOEMS 12, 043003 (2013).

    ADS 

    Google Scholar
     

  • Lv, W. et al. Level-set-based inverse lithography for mask synthesis using the conjugate gradient and an optimal time step. J. Vac. Sci. Technol. B 31, 041605 (2013).


    Google Scholar
     

  • Lv, W. et al. Cascadic multigrid algorithm for robust inverse mask synthesis in optical lithography. J. Micro/Nanolithogr. MEMS MOEMS 13, 023003 (2014).

    ADS 

    Google Scholar
     

  • Shen, Y. et al. Level-set-based inverse lithography for photomask synthesis. Opt. Express 17, 23690–23701 (2009).

    ADS 

    Google Scholar
     

  • Shen, Y. J., Wong, N. & Lam, E. Y. Robust level-set-based inverse lithography. Opt. Express 19, 5511–5521 (2011).

    ADS 

    Google Scholar
     

  • Shen, Y. J. Level-set based mask synthesis with a vector imaging model. Opt. Express 25, 21775–21785 (2017).

    ADS 

    Google Scholar
     

  • Shen, Y. J., Peng, F. & Zhang, Z. R. Efficient optical proximity correction based on semi-implicit additive operator splitting. Opt. Express 27, 1520–1528 (2019).

    ADS 

    Google Scholar
     

  • Shen, Y. J., Zhou, Y. Z. & Zhang, Z. R. Fast implicit active contour model for inverse lithography. Opt. Express 29, 10036–10047 (2021).

    ADS 

    Google Scholar
     

  • Yu, D., Liu, Y. & Hawkinson, C. The application of a new stochastic search algorithm “Adam” in inverse lithography technology (ILT) in critical recording head fabrication process. Proc. SPIE 11613, Optical Microlithography XXXIV (SPIE, 2021).

  • Zheng, X. Q. et al. Study of inverse lithography approaches based on deep learning. J. Microelectron. Manuf. 3, 20030301 (2020).


    Google Scholar
     

  • Wang, S. B. et al. Machine learning assisted SRAF placement for full chip. Proc. SPIE 10451, Photomask Technology 2017 (SPIE, Monterey, CA, USA, 2017).

  • Abbe, E. Beiträge zur Theorie des Mikroskops und der mikroskopischen Wahrnehmung. Arch. f.ür. mikroskopische Anat. 9, 413–468 (1873).


    Google Scholar
     

  • Cecil, T. et al. Advances in inverse lithography. ACS Photonics 10, 910–918 (2023).


    Google Scholar
     

  • Shao, Y. et al. Wavelength-multiplexed multi-mode EUV reflection ptychography based on automatic differentiation. Light Sci. Appl. 13, 196 (2024).


    Google Scholar
     

  • Hinton, G. E. & Salakhutdinov, R. R. Reducing the dimensionality of data with neural networks. Science 313, 504–507 (2006).

    ADS 
    MathSciNet 

    Google Scholar
     

  • Goodfellow, I., Bengio, Y. & Courville, A. Deep Learning (Cambridge, MIT Press, 2016).

  • Lin, Y. B. et al. Data efficient lithography modeling with residual neural networks and transfer learning. Proc. 2018 International Symposium on Physical Design 82–89 (ACM, Monterey CA, USA, 2018).

  • Watanabe, Y. et al. Accurate lithography simulation model based on convolutional neural networks. Proceedings of SPIE 10454, Photomask Japan 2017: XXIV Symposium on Photomask and Next-Generation Lithography Mask Technology (SPIE, Yokohama, Japan, 2017).

  • Hooker, K. et al. Using machine learning etch models in OPC and ILT correction. Proc. SPIE 11614, Design-Process-Technology Co-Optimization XV (SPIE, 2021).

  • Matsunawa, T., Yu, B. & Pan, D. Z. Optical proximity correction with hierarchical bayes model. Proceedings of SPIE 9426, Optical Microlithography XXVIII (SPIE, San Jose, CA, USA, 2015).

  • Choi, S., Shim, S. & Shin, Y. Machine learning (ML)-guided OPC using basis functions of polar Fourier transform. Proc. SPIE 9780, Optical Microlithography XXIX (pp. 97800H. SPIE, San Jose, CA, USA, 2016).


    Google Scholar
     

  • Yang, H. Y. et al. Layout hotspot detection with feature tensor generation and deep biased learning. Proc. 54th Annual Design Automation Conference 2017 62 (IEEE, Austin, TX, USA, 2017).

  • Zhang, H., Yu, B. & Young, E. F. Y. Enabling online learning in lithography hotspot detection with information-theoretic feature optimization. Proc. 2016 IEEE/ACM International Conference on Computer-Aided Design (ICCAD). 1–8 (IEEE, Austin, TX, USA, 2016).

  • Matsunawa, T. et al. A new lithography hotspot detection framework based on AdaBoost classifier and simplified feature extraction. Proc. SPIE 9427, Design-Process-Technology Co-optimization for Manufacturability IX (SPIE, San Jose, CA, USA, 2015).

  • Xu, X. Q. et al. A machine learning based framework for sub-resolution assist feature generation. Proceedings of 2016 on International Symposium on Physical Design 161–168 (ACM, Santa Rosa, CA, USA, 2016).

  • Wang, S. B. et al. Efficient full-chip SRAF placement using machine learning for best accuracy and improved consistency. Proc. SPIE 10587, Optical Microlithography XXXI (SPIE, San Jose, CA, USA, 2018).

  • Ma, X. et al. Pixelated source and mask optimization for immersion lithography. J. Opt. Soc. Am. A 30, 112–123 (2013).

    ADS 

    Google Scholar
     

  • Ma, X. et al. A fast and manufacture-friendly optical proximity correction based on machine learning. Microelectron. Eng. 168, 15–26 (2017).


    Google Scholar
     

  • LeCun, Y., Bengio, Y. & Hinton, G. Deep learning. Nature 521, 436–444 (2015).

    ADS 

    Google Scholar
     

  • Yasuda, J. et al. Recent progress and future of electron multi-beam mask writer. Jpn. J. Appl. Phys. 62, SG0803 (2023).


    Google Scholar
     

  • Chandramouli, M. et al. Development and deployment of advanced multi-beam mask writer. Proc. SPIE 11324, Novel Patterning Technologies for Semiconductors, MEMS/NEMS and MOEMS 2020 (SPIE, San Jose, CA, USA, 2020).

  • Kim, B. G. et al. Trade-off between inverse lithography mask complexity and lithographic performance. Proc. SPIE 7379, Photomask and Next-Generation Lithography Mask Technology XVI (SPIE, Yokohama, Japan, 2009).

  • Pearman, R. et al. How curvilinear mask patterning will enhance the EUV process window: a study using rigorous wafer+mask dual simulation. Proc. SPIE 11178, Photomask Japan 2019: XXVI Symposium on Photomask and Next-Generation Lithography Mask Technology (SPIE, Yokohama, Japan, 2019).

  • Xiao, G. M. et al. E-beam writing time improvement for inverse lithography technology mask for full-chip. Proc. SPIE 7748, Photomask and Next-Generation Lithography Mask Technology XVII (SPIE, Yokohama, Japan, 2010).

  • Xiao, G. M. et al. Affordable and process window increasing full chip ILT masks. Proc. SPIE 7823, Photomask Technology 2010 (SPIE, Monterey, CA, USA, 2010).

  • Pang, L. et al. Enabling faster VSB writing of 193i curvilinear ILT masks that improve wafer process windows for advanced memory applications. Proc. SPIE 11518, Photomask Technology 2020 (SPIE, 2020).

  • Spence, C. et al. Manufacturing challenges for curvilinear masks. Proc. SPIE 10451, Photomask Technology (SPIE, Monterey, CA, USA, 2017).



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