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

Sun, Haifeng; Zhang, Qingyan; Jin, Chuan; Li, Yanli; Tang, Yan; Wang, Jian; Hu, Song; Liu, Junbo

doi:10.1109/jphot.2022.3226266

Cited by 3 publications

(1 citation statement)

References 49 publications

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“…Therefore, to improve the optimization efficiency and obtain the global optimal solution, the algorithm of hybrid PSO and GA is proposed 27,28 . This hybrid algorithm has been applied to digital signal processing, determine pyrolysis kinetics of biomass, and inverse lithography technologies, but there is little research related to the design of flat‐top beam antennas 29–31 …”

Section: Introductionmentioning

confidence: 99%

An E‐band 3D‐printed dielectric lens antenna with flat‐top beam designed by hybrid optimization algorithm

Zheng,

Peng,

Zhu

et al. 2024

Micro & Optical Tech Letters

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An all‐dielectric lens antenna (LA), with flat‐top radiation patterns in both E‐ and H‐planes, is proposed. The antenna covers the 64–70 GHz band. The dielectric LA consists of a source horn (LB‐12‐15‐A with WR12) and a dielectric lens. The lens is composed of multiple layers of dielectric rings with different heights. The structural parameters of the dielectric lens are optimized by a hybrid optimization algorithm (particle swarm optimization [PSO]‐genetic algorithm [GA]) that involves genetic operations of GA and PSO. The hybrid algorithm is beneficial to enhance the optimization efficiency, and an objective function is designed to optimize the beam width, the S11, and the sidelobe level (SLL). Both the simulated and measured results show that the dielectric LA provides good flat‐top radiation patterns within the desired band. Moreover, the dielectric lens is manufactured by three‐dimensional‐printed technology, which allows for low cost, easy manufacturing, and lightweight.

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Section: Introductionmentioning

confidence: 99%

An E‐band 3D‐printed dielectric lens antenna with flat‐top beam designed by hybrid optimization algorithm

Zheng,

Peng,

Zhu

et al. 2024

Micro & Optical Tech Letters

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Efficient mask optimization for enhanced digital maskless lithography quality by improved particle swarm optimization algorithm

Huang,

Wu,

Tang

et al. 2024

Journal of Vacuum Science &Amp; Technology B

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In this paper, an efficient mask optimization method for enhanced digital micromirror device lithography quality based on improved particle swarm optimization (PSO) is proposed, which greatly improves the quality of lithography. First, the traditional PSO algorithm is improved by introducing adaptive parameter adjustment to enhance its search ability in complex problems. In addition, in order to avoid premature convergence of the algorithm, a simulated annealing operation is introduced to make it accept the different solution with a certain probability and jump out of the local optimal better. The numerical simulation experiment results showed that the pattern errors between the print image and target pattern were reduced by 93.5%, 95.8%, and 95.6%, respectively. Compared with traditional optimization methods, the proposed algorithm significantly improves the image quality, especially in the aspects of edge contour and pattern fidelity.

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RETRACTED: MSFGQ: Design of an efficient Multiparametric model for improving sub-field scheduling performance via novel GA & Q-Learning optimizations

Keswani,

Peshwe

2024

IFS

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This paper presents the design of a novel multiparametric model aimed at improving sub-field scheduling performance for lithographic processes. The proposed model incorporates various parameters such as sub-field locations, conflict analysis, critical dimensions, delay, current, voltage, dose, and depth of current for optimization of scheduling operations. To achieve this, we have utilized both Genetic Algorithm (GA) and Q-learning algorithms to optimize the scheduling performance in real-time lithographic processes. The need for this work stems from the increasing demand for high precision lithographic processes, which require efficient scheduling operations to achieve optimal results. The proposed model has been tested on real-time lithographic processes, and the results have been evaluated in terms of critical dimensions, scheduling performance, and scheduling efficiency. The results show that the proposed model has reduced critical dimensions by 8.5%, improved scheduling performance by 10.5%, and increased scheduling efficiency by 8.3% . These results demonstrate the efficacy of the proposed model in improving sub-field scheduling performance in lithographic processes. Based on the results it can be observed that this work presents a novel multiparametric model that utilizes GA and Q-learning algorithms to improve sub-field scheduling performance in lithographic processes.

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

Cited by 3 publications

References 49 publications

An E‐band 3D‐printed dielectric lens antenna with flat‐top beam designed by hybrid optimization algorithm

An E‐band 3D‐printed dielectric lens antenna with flat‐top beam designed by hybrid optimization algorithm

Efficient mask optimization for enhanced digital maskless lithography quality by improved particle swarm optimization algorithm

RETRACTED: MSFGQ: Design of an efficient Multiparametric model for improving sub-field scheduling performance via novel GA & Q-Learning optimizations

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