The shrinking of the size of the advanced technological nodes brings up new challenges to the semiconductor manufacturing community.The optical proximity correction(OPC)is invented to reduce the errors of the lithogra...The shrinking of the size of the advanced technological nodes brings up new challenges to the semiconductor manufacturing community.The optical proximity correction(OPC)is invented to reduce the errors of the lithographic process.The conventional OPC techniques rely on the empirical models and optimization methods of iterative type.Both the accuracy and computing speed of the existing OPC techniques need to be improved to fulfill the stringent requirement of the research and design for latest technological nodes.The emergence of machine learning technologies inspires novel OPC algorithms.More accurate forward simulation of the lithographic process and single turn optimization methods are enabled by the machine learning based OPC techniques.We discuss the latest progress made by the OPC community in the process simulation and optimization based on machine learning techniques.展开更多
Considering the efficiency and veracity of rules based optical proximity correction (OPC),the importance of rules in rules based OPC is pointed out.And how to select,to construct and to apply more concise and practi...Considering the efficiency and veracity of rules based optical proximity correction (OPC),the importance of rules in rules based OPC is pointed out.And how to select,to construct and to apply more concise and practical rules base is disscussed.Based on those ideas,four primary rules are suggested.Some data resulted in rules base are shown in table.The patterns on wafer are clearly improved by applying these rules to correct mask.OPCL,the automatic construction of the rules base is an important part of the whole rules based OPC system.展开更多
Optical proximity correction (OPC) is a key step in modern integrated circuit (IC) manufacturing.The quality of model-based OPC (MB-OPC) is directly determined by segment offsets after OPC processing.However,in conven...Optical proximity correction (OPC) is a key step in modern integrated circuit (IC) manufacturing.The quality of model-based OPC (MB-OPC) is directly determined by segment offsets after OPC processing.However,in conventional MB-OPC,the intensity of a control site is adjusted only by the movement of its corresponding segment;this scheme is no longer accurate enough as the lithography process advances.On the other hand,matrix MB-OPC is too time-consuming to become practical.In this paper,we propose a new sparse matrix MB-OPC algorithm with model-based mapping between segments and control sites.We put forward the concept of 'sensitive area'.When the Jacobian matrix used in the matrix MB-OPC is evaluated,only the elements that correspond to the segments in the sensitive area of every control site need to be calculated,while the others can be set to 0.The new algorithm can effectively improve the sparsity of the Jacobian matrix,and hence reduce the computations.Both theoretical analysis and experiments show that the sparse matrix MB-OPC with model-based mapping is more accurate than conventional MB-OPC,and much faster than matrix MB-OPC while maintaining high accuracy.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
Optical proximity correction (OPC) systems require an accurate and fast way to predict how patterns will be transferred to the wafer.Based on Gabor's 'reduction to principal waves',a partially coherent ima...Optical proximity correction (OPC) systems require an accurate and fast way to predict how patterns will be transferred to the wafer.Based on Gabor's 'reduction to principal waves',a partially coherent imaging system can be represented as a superposition of coherent imaging systems,so an accurate and fast sparse aerial image intensity calculation algorithm for lithography simulation is presented based on convolution kernels,which also include simulating the lateral diffusion and some mask processing effects via Gaussian filter.The simplicity of this model leads to substantial computational and analytical benefits.Efficiency of this method is also shown through simulation results.展开更多
We describe a post resolution-enhancement-technique verification method for use in manufacturing data flow. The goal of the method is to verify whether designs function as intended,or more precisely, whether the print...We describe a post resolution-enhancement-technique verification method for use in manufacturing data flow. The goal of the method is to verify whether designs function as intended,or more precisely, whether the printed images are consistent with the design intent. The process modeling is described for the model-based verifi cation method. The performance of the method is demonstrated by experiment.展开更多
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 continuous decrease in the critical dimensions of integrated circuits, mask optimization has becomethe main challenge in VLSI design. In recent years, thriving machine learning has been gradually introduced i...With the continuous decrease in the critical dimensions of integrated circuits, mask optimization has becomethe main challenge in VLSI design. In recent years, thriving machine learning has been gradually introduced in the field ofoptical proximity correction (OPC). Currently, advanced learning-based frameworks have been limited by low mask printability or large computational overhead. To address these limitations, this paper proposes a learning-based frameworknamed SegNet-OPC, which can generate optimized masks from the target layout at shorter training and turnaround timewith higher mask printability. The proposed framework consists of a backbone network and loss terms suitable for maskoptimization tasks, followed by a fine-tuning network. The framework yields remarkable improvements over conventionalmethods, delivering significantly faster turnaround time and superior mask printability and manufacturability. With just1.25 hours of training, the framework achieves comparable mask complexity while surpassing the state-of-the-art methods,achieving a minimum 3% enhancement in mask printability and an impressive 16.7% improvement in mask manufacturability.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
Inverse lithography technology(ILT),also known as pixel-based optical proximity correction(PB-OPC),has shown promising capability in pushing the current 193 nm lithography to its limit.By treating the mask optimizatio...Inverse lithography technology(ILT),also known as pixel-based optical proximity correction(PB-OPC),has shown promising capability in pushing the current 193 nm lithography to its limit.By treating the mask optimization process as an inverse problem in lithography,ILT provides a more complete exploration of the solution space and better pattern fidelity than the traditional edge-based OPC.However,the existing methods of ILT are extremely time-consuming due to the slow convergence of the optimization process.To address this issue,in this paper we propose a support vector machine(SVM)based layout retargeting method for ILT,which is designed to generate a good initial input mask for the optimization process and promote the convergence speed.Supervised by optimized masks of training layouts generated by conventional ILT,SVM models are learned and used to predict the initial pixel values in the‘undefined areas’of the new layout.By this process,an initial input mask close to the final optimized mask of the new layout is generated,which reduces iterations needed in the following optimization process.Manufacturability is another critical issue in ILT;however,the mask generated by our layout retargeting method is quite irregular due to the prediction inaccuracy of the SVM models.To compensate for this drawback,a spatial filter is employed to regularize the retargeted mask for complexity reduction.We implemented our layout retargeting method with a regularized level-set based ILT(LSB-ILT)algorithm under partially coherent illumination conditions.Experimental results show that with an initial input mask generated by our layout retargeting method,the number of iterations needed in the optimization process and runtime of the whole process in ILT are reduced by 70.8%and 69.0%,respectively.展开更多
Growing data volume of masks tremendously increases manufacture cost. The cost increase is partially due to the complicated optical proximity corrections applied on mask design. In this paper, a yield-aware dissec- ti...Growing data volume of masks tremendously increases manufacture cost. The cost increase is partially due to the complicated optical proximity corrections applied on mask design. In this paper, a yield-aware dissec- tion method is presented. Based on the recognition of yield related mask context, the dissection result provides sufficient degrees of freedom to keep fidelity on critical sites while still retaining the frugality of modified designs. Experiments show that the final mask volume using the new method is reduced to about 50% of the conventional method.展开更多
基金by National Science and Technology Major Project of China(2017ZX02315001-003,2017ZX02101004-003)National Natural Science Foundation of China(61874002,61804174),Beijing Natural Fund(4182021).
文摘The shrinking of the size of the advanced technological nodes brings up new challenges to the semiconductor manufacturing community.The optical proximity correction(OPC)is invented to reduce the errors of the lithographic process.The conventional OPC techniques rely on the empirical models and optimization methods of iterative type.Both the accuracy and computing speed of the existing OPC techniques need to be improved to fulfill the stringent requirement of the research and design for latest technological nodes.The emergence of machine learning technologies inspires novel OPC algorithms.More accurate forward simulation of the lithographic process and single turn optimization methods are enabled by the machine learning based OPC techniques.We discuss the latest progress made by the OPC community in the process simulation and optimization based on machine learning techniques.
文摘Considering the efficiency and veracity of rules based optical proximity correction (OPC),the importance of rules in rules based OPC is pointed out.And how to select,to construct and to apply more concise and practical rules base is disscussed.Based on those ideas,four primary rules are suggested.Some data resulted in rules base are shown in table.The patterns on wafer are clearly improved by applying these rules to correct mask.OPCL,the automatic construction of the rules base is an important part of the whole rules based OPC system.
文摘Optical proximity correction (OPC) is a key step in modern integrated circuit (IC) manufacturing.The quality of model-based OPC (MB-OPC) is directly determined by segment offsets after OPC processing.However,in conventional MB-OPC,the intensity of a control site is adjusted only by the movement of its corresponding segment;this scheme is no longer accurate enough as the lithography process advances.On the other hand,matrix MB-OPC is too time-consuming to become practical.In this paper,we propose a new sparse matrix MB-OPC algorithm with model-based mapping between segments and control sites.We put forward the concept of 'sensitive area'.When the Jacobian matrix used in the matrix MB-OPC is evaluated,only the elements that correspond to the segments in the sensitive area of every control site need to be calculated,while the others can be set to 0.The new algorithm can effectively improve the sparsity of the Jacobian matrix,and hence reduce the computations.Both theoretical analysis and experiments show that the sparse matrix MB-OPC with model-based mapping is more accurate than conventional MB-OPC,and much faster than matrix MB-OPC while maintaining high accuracy.
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.
文摘Optical proximity correction (OPC) systems require an accurate and fast way to predict how patterns will be transferred to the wafer.Based on Gabor's 'reduction to principal waves',a partially coherent imaging system can be represented as a superposition of coherent imaging systems,so an accurate and fast sparse aerial image intensity calculation algorithm for lithography simulation is presented based on convolution kernels,which also include simulating the lateral diffusion and some mask processing effects via Gaussian filter.The simplicity of this model leads to substantial computational and analytical benefits.Efficiency of this method is also shown through simulation results.
文摘We describe a post resolution-enhancement-technique verification method for use in manufacturing data flow. The goal of the method is to verify whether designs function as intended,or more precisely, whether the printed images are consistent with the design intent. The process modeling is described for the model-based verifi cation method. The performance of the method is demonstrated by experiment.
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supported by the Special Fund for Research on National Major Research Instruments of China under Grant No.62027815the National Natural Science Foundation of China under Grant Nos.61834006,62274052,and 61404001.
文摘With the continuous decrease in the critical dimensions of integrated circuits, mask optimization has becomethe main challenge in VLSI design. In recent years, thriving machine learning has been gradually introduced in the field ofoptical proximity correction (OPC). Currently, advanced learning-based frameworks have been limited by low mask printability or large computational overhead. To address these limitations, this paper proposes a learning-based frameworknamed SegNet-OPC, which can generate optimized masks from the target layout at shorter training and turnaround timewith higher mask printability. The proposed framework consists of a backbone network and loss terms suitable for maskoptimization tasks, followed by a fine-tuning network. The framework yields remarkable improvements over conventionalmethods, delivering significantly faster turnaround time and superior mask printability and manufacturability. With just1.25 hours of training, the framework achieves comparable mask complexity while surpassing the state-of-the-art methods,achieving a minimum 3% enhancement in mask printability and an impressive 16.7% improvement in mask manufacturability.
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.
文摘Inverse lithography technology(ILT),also known as pixel-based optical proximity correction(PB-OPC),has shown promising capability in pushing the current 193 nm lithography to its limit.By treating the mask optimization process as an inverse problem in lithography,ILT provides a more complete exploration of the solution space and better pattern fidelity than the traditional edge-based OPC.However,the existing methods of ILT are extremely time-consuming due to the slow convergence of the optimization process.To address this issue,in this paper we propose a support vector machine(SVM)based layout retargeting method for ILT,which is designed to generate a good initial input mask for the optimization process and promote the convergence speed.Supervised by optimized masks of training layouts generated by conventional ILT,SVM models are learned and used to predict the initial pixel values in the‘undefined areas’of the new layout.By this process,an initial input mask close to the final optimized mask of the new layout is generated,which reduces iterations needed in the following optimization process.Manufacturability is another critical issue in ILT;however,the mask generated by our layout retargeting method is quite irregular due to the prediction inaccuracy of the SVM models.To compensate for this drawback,a spatial filter is employed to regularize the retargeted mask for complexity reduction.We implemented our layout retargeting method with a regularized level-set based ILT(LSB-ILT)algorithm under partially coherent illumination conditions.Experimental results show that with an initial input mask generated by our layout retargeting method,the number of iterations needed in the optimization process and runtime of the whole process in ILT are reduced by 70.8%and 69.0%,respectively.
文摘Growing data volume of masks tremendously increases manufacture cost. The cost increase is partially due to the complicated optical proximity corrections applied on mask design. In this paper, a yield-aware dissec- tion method is presented. Based on the recognition of yield related mask context, the dissection result provides sufficient degrees of freedom to keep fidelity on critical sites while still retaining the frugality of modified designs. Experiments show that the final mask volume using the new method is reduced to about 50% of the conventional method.
