Gradient-based source mask and polarization optimization with the hybrid Hopkins–Abbe model

Ding, Ming; Niu, Zhiyuan; Zhang, Fang; Zhu, Linglin; Shi, Wei; Zeng, Aijun; Huang, Huijie

doi:10.1117/1.jmm.19.3.033201

Cited by 3 publications

(2 citation statements)

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“…Thus, the threshold and the sigmoid functions can be employed to approximately represent the layout pattern after the resist effect is exerted on the wafer surface [23], [24]. Regarding the application of the gradient-based SO method, many studies have proven that the imaging performance of lithography can be improved by optimizing the source's intensity distribution [18], [25]. Peng et al employed a graylevel pixel to represent the lithographic source and utilized the gradient-based SO method to improve the image fidelity and depth of focus [18].…”

Section: Introductionmentioning

confidence: 99%

“…Peng et al employed a graylevel pixel to represent the lithographic source and utilized the gradient-based SO method to improve the image fidelity and depth of focus [18]. Ding et al employed the gradientdescent method to optimize the lithographic source and mask in the hybrid Hopkins-Abbe imaging model to enhance the optimization performance [25]. However, for a lithographic imaging process using a complicated resist model, it is not recommended to calculate the gradients of a highly complex merit function in the gradient-based SO model.…”

Section: Introductionmentioning

confidence: 99%

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Inverse Lithography Source Optimization via Particle Swarm Optimization and Genetic Combined Algorithm

et al. 2023

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Inverse lithography technologies (ILTs) are critical for improving the imaging performance of lithography in advanced technology nodes. Pixel-based source optimization (SO), as an efficient part of ILTs, can be implemented via heuristic approaches to achieve high-performance lithographic imaging. In this paper, a SO approach based on a combination of the particle-swarm optimization and genetic algorithms (PSO-GA) is proposed to determine the optimal intensity distribution of the source via iterations. The pixelated source can be decoded into the optimized variables of the merit functions in the SO model. The proposed PSO-GA algorithm, as a highefficiency hybrid algorithm, can transform the discrete SO problem into the optimal search solution for the merit function, thereby inversely enhancing the lithographic-imaging performance. In the forward-imaging model in the lithography, the extraction of the mask's effective diffraction spectrum is implemented to calculate the layout of resist patterns. The simulation results highlight the superior performance of the proposed approach in achieving pixelated SO over the traditional GA and PSO algorithm in terms of convergence capacity.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Inverse Lithography Source Optimization via Particle Swarm Optimization and Genetic Combined Algorithm

et al. 2023

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Global optimisation of source and mask in inverse lithography via tabu search combined with genetic algorithm

Sun

chuan

et al. 2022

Opt. Express

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Source mask optimisation (SMO) is a resolution enhancement technology that is utilised in the advanced process node of optical lithography to achieve acceptable imaging quality and fidelity. It is crucial in enhancing the convergence performance and optimisation capability of pixel-based SMO. In this study, an SMO approach that employs a genetic algorithm (GA), combined with the tabu search method (TS), is proposed. GA-TS, a hybrid-type global optimisation algorithm, has an outperforming capacity to avoid local optima owing to the excellent local searching function of TS. Furthermore, an edge-optimisation strategy was implemented to optimise the mask for a low-complexity mask layout. The simulation results confirm that the proposed approach exhibits exceptional optimisation capability and convergence performance.

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The Inverse Optimization of an Optical Lithographic Source with a Hybrid Genetic Algorithm

Liu

Zhou

et al. 2023

Applied Sciences

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As an effective resolution enhancement technology, source optimization (SO) is considered key for significantly improving the image quality of optical lithography at advanced nodes. To solve the problem of unsatisfactory SO performance, it is necessary to combine it with optimization algorithms. In this study, an SO method based on a hybrid genetic algorithm is proposed to achieve an acceptable source shape in the imaging process for optical lithography. To overcome the problems of local optima and the small search scope, an update strategy that uses particle swarm optimization and the tabu list method from the tabu search algorithm are utilized to enhance the optimization performance. Meanwhile, different feature patterns were employed as the input of the optimization model. These simulation results show that the proposed SO method exhibits dominant optimization performance for SO in optical lithography.

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Gradient-based source mask and polarization optimization with the hybrid Hopkins–Abbe model

Cited by 3 publications

References 0 publications

Inverse Lithography Source Optimization via Particle Swarm Optimization and Genetic Combined Algorithm

Inverse Lithography Source Optimization via Particle Swarm Optimization and Genetic Combined Algorithm

Global optimisation of source and mask in inverse lithography via tabu search combined with genetic algorithm

The Inverse Optimization of an Optical Lithographic Source with a Hybrid Genetic Algorithm

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