Computational lithography(CL)has become an indispensable technology to improve imaging resolution and fidelity of deep sub-wavelength lithography.The state-of-the-art CL approaches are capable of optimizing pixel-base...Computational lithography(CL)has become an indispensable technology to improve imaging resolution and fidelity of deep sub-wavelength lithography.The state-of-the-art CL approaches are capable of optimizing pixel-based mask patterns to effectively improve the degrees of optimization freedom.However,as the growth of data volume of photomask layouts,computational complexity has become a challenging problem that prohibits the applications of advanced CL algorithms.In the past,a number of innovative methods have been developed to improve the computational efficiency of CL algorithms,such as machine learning and deep learning methods.Based on the brief introduction of optical lithography,this paper reviews some recent advances of fast CL approaches based on deep learning.At the end,this paper briefly discusses some potential developments in future work.展开更多
Inverse lithography technology(ILT)is a promising approach in computational lithography to address the challenges posed by shrinking semiconductor device dimensions.The ILT leverages optimization algorithms to generat...Inverse lithography technology(ILT)is a promising approach in computational lithography to address the challenges posed by shrinking semiconductor device dimensions.The ILT leverages optimization algorithms to generate mask patterns,outperforming traditional optical proximity correction methods.This review provides an overview of ILT's principles,evolution,and applications,with an emphasis on integration with artificial intelligence(AI)techniques.The review tracks recent advancements of ILT in model improvement and algorithmic efficiency.Challenges such as extended computational runtimes and mask-writing complexities are summarized,with potential solutions discussed.Despite these challenges,AI-driven methods,such as convolutional neural networks,deep neural networks,generative adversarial networks,and model-driven deep learning methods,are transforming ILT.AI-based approaches offer promising pathways to overcome existing limitations and support the adoption in high-volume manufacturing.Future research directions are explored to exploit ILT's potential and drive progress in the semiconductor industry.展开更多
With the continued shrinking of the critical dimensions(CDs)of wafer patterning,the requirements for modeling precision in optical proximity correction(OPC)increase accordingly.This requirement extends beyond CD contr...With the continued shrinking of the critical dimensions(CDs)of wafer patterning,the requirements for modeling precision in optical proximity correction(OPC)increase accordingly.This requirement extends beyond CD controlling accuracy to include pattern alignment accuracy because misalignment can lead to considerable overlay and metal-via coverage issues at advanced nodes,affecting process window and yield.This paper proposes an efficient OPC modeling approach that prioritizes pattern-shift-related elements to tackle the issue accurately.Our method integrates careful measurement selection,the implementation of pattern-shift-aware structures in design,and the manipulation of the cost function during model tuning to establish a robust model.Confirmatory experiments are performed on a via layer fabricated using a negative tone development.Results demonstrate that pattern shifts can be constrained within a range of+1 nm,remarkably better than the original range of±3 nm.Furthermore,simulations reveal notable differences between post OPC and original masks when considering pattern shifts at locations sensitive to this phenomenon.Experimental validation confirms the accuracy of the proposed modeling approach,and a firm consistency is observed between the simulation results and experimental data obtained from actual design structures.展开更多
Extreme ultraviolet(EUV)lithography with high numerical aperture(NA)is a future technology to manufacture the integrated circuit in sub-nanometer dimension.Meanwhile,source mask co-optimization(SMO)is an extensively u...Extreme ultraviolet(EUV)lithography with high numerical aperture(NA)is a future technology to manufacture the integrated circuit in sub-nanometer dimension.Meanwhile,source mask co-optimization(SMO)is an extensively used approach for advanced lithography process beyond 28 nm technology node.This work proposes a novel SMO method to improve the image fidelity of high-NA EUV lithography system.A fast high-NA EUV lithography imaging model is established first,which includes the effects of mask three-dimensional structure and anamorphic magnification.Then,this paper develops an efficient SMO method that combines the gradient-based mask optimization algorithm and the compressivesensing-based source optimization algorithm.A mask rule check(MRC)process is further proposed to simplify the optimized mask pattern.Results illustrate that the proposed SMO method can significantly reduce the lithography patterning error,and maintain high computational efficiency.展开更多
As semiconductor manufacturing moves toward fine feature sizes,precise and efficient resist model calibration has become crucial for optical proximity correction to ensure pattern fidelity.However,traditional calibrat...As semiconductor manufacturing moves toward fine feature sizes,precise and efficient resist model calibration has become crucial for optical proximity correction to ensure pattern fidelity.However,traditional calibration methods struggle with efficiency and scalability and are prone to becoming trapped in local optima.Herein,we propose a surrogate-assisted genetic algorithm(SAGA)that integrates Kriging interpolation-based surrogate models and dynamic adaptive mechanisms to optimize resist model coefficients,convolution kernel parameters,and aerial image settings jointly.By leveraging surrogate models to predict high-performance solutions and adaptively adjusting crossover/mutation rates,SAGA balances global exploration and local exploitation,achieving rapid convergence and superior model accuracy compared with other algorithms.Experimental validation across three resist cases demonstrates that SAGA outperforms conventional genetic algorithms and grid search.Compared with other algorithms,SAGA not only achieves higher accuracy but also converges faster,with its optimization trajectories stabilizing earlier in the iterative process.These results highlight SAGA’s potential for efficient and high-precision resist calibration in computational lithography.展开更多
基金the financial support by the National Natural Science Foundation of China(NSFC)(61675021)the Fundamental Research Funds for the Central Universities(2020CX02002,2018CX01025)。
文摘Computational lithography(CL)has become an indispensable technology to improve imaging resolution and fidelity of deep sub-wavelength lithography.The state-of-the-art CL approaches are capable of optimizing pixel-based mask patterns to effectively improve the degrees of optimization freedom.However,as the growth of data volume of photomask layouts,computational complexity has become a challenging problem that prohibits the applications of advanced CL algorithms.In the past,a number of innovative methods have been developed to improve the computational efficiency of CL algorithms,such as machine learning and deep learning methods.Based on the brief introduction of optical lithography,this paper reviews some recent advances of fast CL approaches based on deep learning.At the end,this paper briefly discusses some potential developments in future work.
基金support by the National Natural Science Foundation of China(62235009).
文摘Inverse lithography technology(ILT)is a promising approach in computational lithography to address the challenges posed by shrinking semiconductor device dimensions.The ILT leverages optimization algorithms to generate mask patterns,outperforming traditional optical proximity correction methods.This review provides an overview of ILT's principles,evolution,and applications,with an emphasis on integration with artificial intelligence(AI)techniques.The review tracks recent advancements of ILT in model improvement and algorithmic efficiency.Challenges such as extended computational runtimes and mask-writing complexities are summarized,with potential solutions discussed.Despite these challenges,AI-driven methods,such as convolutional neural networks,deep neural networks,generative adversarial networks,and model-driven deep learning methods,are transforming ILT.AI-based approaches offer promising pathways to overcome existing limitations and support the adoption in high-volume manufacturing.Future research directions are explored to exploit ILT's potential and drive progress in the semiconductor industry.
基金funded by the National Natural Science Foundation of China(Grant Nos.52130504,52305577,and 52205592)the Key Research and Development Plan of Hubei Province,China(Grant No.2022BAA013)+2 种基金the Major Program(JD)of Hubei Province,China(Grant No.2023BAA008-2)the Innovation Projection of Optics Valley Laboratory,China(Grant No.OVL2023PY003)the Postdoctoral Fellowship Program(Grade B)of the China Postdoctoral Science Foundation(Grant No.GZB20230244).
文摘With the continued shrinking of the critical dimensions(CDs)of wafer patterning,the requirements for modeling precision in optical proximity correction(OPC)increase accordingly.This requirement extends beyond CD controlling accuracy to include pattern alignment accuracy because misalignment can lead to considerable overlay and metal-via coverage issues at advanced nodes,affecting process window and yield.This paper proposes an efficient OPC modeling approach that prioritizes pattern-shift-related elements to tackle the issue accurately.Our method integrates careful measurement selection,the implementation of pattern-shift-aware structures in design,and the manipulation of the cost function during model tuning to establish a robust model.Confirmatory experiments are performed on a via layer fabricated using a negative tone development.Results demonstrate that pattern shifts can be constrained within a range of+1 nm,remarkably better than the original range of±3 nm.Furthermore,simulations reveal notable differences between post OPC and original masks when considering pattern shifts at locations sensitive to this phenomenon.Experimental validation confirms the accuracy of the proposed modeling approach,and a firm consistency is observed between the simulation results and experimental data obtained from actual design structures.
基金financially supported by National Natural Science Foundation of China (No. 62274181,62204257 and 62374016)Chinese Ministry of Science and Technology (No. 2019YFB2205005)+4 种基金Guangdong Province Research and Development Program in Key Fields (No. 2021B0101280002)the support from Youth Innovation Promotion Association Chinese Academy of Sciences (No. 2021115)Beijing Institute of ElectronicsBeijing Association for Science and Technology as well,the support from University of Chinese Academy of Sciences (No. 118900M032)China Fundamental Research Funds for the Central Universities (No. E2ET3801)
文摘Extreme ultraviolet(EUV)lithography with high numerical aperture(NA)is a future technology to manufacture the integrated circuit in sub-nanometer dimension.Meanwhile,source mask co-optimization(SMO)is an extensively used approach for advanced lithography process beyond 28 nm technology node.This work proposes a novel SMO method to improve the image fidelity of high-NA EUV lithography system.A fast high-NA EUV lithography imaging model is established first,which includes the effects of mask three-dimensional structure and anamorphic magnification.Then,this paper develops an efficient SMO method that combines the gradient-based mask optimization algorithm and the compressivesensing-based source optimization algorithm.A mask rule check(MRC)process is further proposed to simplify the optimized mask pattern.Results illustrate that the proposed SMO method can significantly reduce the lithography patterning error,and maintain high computational efficiency.
基金funded by the National Natural Science Foundation of China(Grant Nos.52205592 and 52130504)Key Research and Development Plan of Hubei Province,China(Grant No.2022BAA013)+1 种基金Major Program(JD)of Hubei Province,China(Grant No.2023BAA008-2)Innovation Project of Optics Valley Laboratory,China(Grant No.OVL2023PY003)。
文摘As semiconductor manufacturing moves toward fine feature sizes,precise and efficient resist model calibration has become crucial for optical proximity correction to ensure pattern fidelity.However,traditional calibration methods struggle with efficiency and scalability and are prone to becoming trapped in local optima.Herein,we propose a surrogate-assisted genetic algorithm(SAGA)that integrates Kriging interpolation-based surrogate models and dynamic adaptive mechanisms to optimize resist model coefficients,convolution kernel parameters,and aerial image settings jointly.By leveraging surrogate models to predict high-performance solutions and adaptively adjusting crossover/mutation rates,SAGA balances global exploration and local exploitation,achieving rapid convergence and superior model accuracy compared with other algorithms.Experimental validation across three resist cases demonstrates that SAGA outperforms conventional genetic algorithms and grid search.Compared with other algorithms,SAGA not only achieves higher accuracy but also converges faster,with its optimization trajectories stabilizing earlier in the iterative process.These results highlight SAGA’s potential for efficient and high-precision resist calibration in computational lithography.
