Source Optimization Using Particle Swarm Optimization Algorithm in Optical Lithography

Lei, 王磊 Wang; Sikun, 李思坤 Li; Xiangzhao, 王向朝 Wang; Guanyong, 闫观勇 Yan; Chaoxing, 杨朝兴 Yang

doi:10.3788/aos201535.0422002

Cited by 8 publications

(1 citation statement)

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“…At present, the parameter identification methods of the Bouc–Wen model include the particle swarm optimization (PSO) algorithm and genetic algorithm (GA), et al [ 25 , 26 , 27 , 28 ]. These algorithms have their defects, such as the PSO algorithm’s propensity to easily enter the local optimum and the GA’s poor capacity for searching for the local optimal solution [ 29 ].…”

Section: Introductionmentioning

confidence: 99%

Parameter Identification of Model for Piezoelectric Actuators

Dong

Guo

et al. 2023

Micromachines

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Piezoelectric actuators are widely used in high-precision positioning systems. The nonlinear characteristics of piezoelectric actuators, such as multi-valued mapping and frequency-dependent hysteresis, severely limit the advancement of the positioning system’s accuracy. Therefore, a particle swarm genetic hybrid parameter identification method is proposed by combining the directivity of the particle swarm optimization algorithm and the genetic random characteristics of the genetic algorithm. Thus, the global search and optimization abilities of the parameter identification approach are improved, and the problems, including the genetic algorithm’s poor local search capability and the particle swarm optimization algorithm’s ease of falling into local optimal solutions, are resolved. The nonlinear hysteretic model of piezoelectric actuators is established based on the hybrid parameter identification algorithm proposed in this paper. The output of the model of the piezoelectric actuator is in accordance with the real output obtained from the experiments, and the root mean square error is only 0.029423 μm. The experimental and simulation results show that the model of piezoelectric actuators established by the proposed identification method can describe the multi-valued mapping and frequency-dependent nonlinear hysteresis characteristics of piezoelectric actuators.

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Section: Introductionmentioning

confidence: 99%

Parameter Identification of Model for Piezoelectric Actuators

Dong

Guo

et al. 2023

Micromachines

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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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Multi-objective adaptive source optimization for full chip

et al. 2021

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Source optimization (SO) is an extensively used resolution enhancement technique in optical lithography. To improve computational efficiency, compressive sensing (CS) theory was applied to SO for clip-level applications in previous works. We propose, for the first time to our knowledge, a multi-objective adaptive SO (adaptive-MOSO) with CS for full chip. The fast optimization of a pixel illumination source pattern is achieved, and the imaging fidelity of each clip is guaranteed simultaneously at full chip. Fast CS with contour sampling is applied to accelerate the SO procedure by sampling all layout patterns. Novel cost function with adaptive weight distribution for every single clip is established to guarantee the lithography imaging fidelity for full chip. The simulation results prove that the adaptive-MOSO method improves the efficiency of SO and the lithography performance for large-scale chips.

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Source Optimization Using Particle Swarm Optimization Algorithm in Optical Lithography

Cited by 8 publications

References 0 publications

Parameter Identification of Model for Piezoelectric Actuators

Parameter Identification of Model for Piezoelectric Actuators

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

Multi-objective adaptive source optimization for full chip

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