Robust source and mask optimization compensating for mask topography effects in computational lithography

Li, Jia; Lam, Edmund Y.

doi:10.1364/oe.22.009471

Cited by 35 publications

(7 citation statements)

References 33 publications

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“…Traditional gradient-based methods of solving optimization problems, such as conjugate gradient (CG) [5,6], steepest descent (SD) [7], and gradient descent (GD) [8][9][10], have been used to solve the SMO problem. These methods are relatively efficient.…”

Section: Introductionmentioning

confidence: 99%

The Inverse Optimization of Lithographic Source and Mask via GA-APSO Hybrid Algorithm

et al. 2023

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Source mask optimization (SMO) is an effective method for improving the image quality of high-node lithography. Reasonable algorithm optimization is the critical issue in SMO. A GA-APSO hybrid algorithm, combining genetic algorithm (GA) and adaptive particle swarm optimization (APSO), was proposed to inversely obtain the global optimal distribution of the pixelated source and mask in the lithographic imaging process. The computational efficiency was improved by combining the GA and PSO algorithms. Additionally, the global search and local search were balanced through adaptive strategies, leading to a closer result to the global optimal solution. To verify the performance of GA-APSO, simple symmetric patterns and complex patterns were optimized and compared with GA and APSO, respectively. The results show that the pattern errors (PEs) of the resist image optimized by GA-APSO were reduced by 40.13–52.94% and 10.28–33.31% compared to GA and APSO, respectively. The time cost of GA-APSO was reduced by 75.91–87.00% and 48.43–58.66% compared to GA and APSO, respectively. Moreover, repeated calculation showed that the GA-APSO results were relatively stable. The results demonstrate the superior performance of GA-APSO in efficiency, accuracy, and repeatability for source and mask optimization.

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Section: Introductionmentioning

confidence: 99%

The Inverse Optimization of Lithographic Source and Mask via GA-APSO Hybrid Algorithm

et al. 2023

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“…Gradient-based methods, as local search algorithms, have been generally applied in lithographic SO, such as gradient descent (GD) 10,11 , steepest descent (SD) 7 , conjugate gradient (CG) 12,13 . However, due to the complex gradient function for the resist model at advanced nodes, the optimization performance of gradient-based pixeled SO method is evidently limited.…”

Section: Introductionmentioning

confidence: 99%

Source optimization using hybrid genetic algorithm in lithography

Sun¹,

Zhang²,

Jin³

et al. 2023

Ninth Symposium on Novel Photoelectronic Detection Technology and Applications

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“…The development of these algorithms can serve as guidelines for the investigation of 3D OPC. Examples of such algorithms are the conjugate gradient methods [32,33], the augmented Lagrangian methods [34], the compressive sensing methods [35,36], the semi-implicit methods [37,38], and the model-driven neural network methods [39,40]. Inspired by these algorithms, Peng et al simplified the derivatives as a matrix form and proposed the 3D OPC method based on 3D lithography [41].…”

Section: Introductionmentioning

confidence: 99%

An Improved 3D OPC Method for the Fabrication of High-Fidelity Micro Fresnel Lenses

Peng,

Sun,

Wan

et al. 2023

Micromachines

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Based on three-dimensional optical proximity correction (3D OPC), recent advancements in 3D lithography have enabled the high-fidelity customization of 3D micro-optical elements. However, the micron-to-millimeter-scale structures represented by the Fresnel lens design bring more stringent requirements for 3D OPC, which poses significant challenges to the accuracy of models and the efficiency of algorithms. Thus, a lithographic model based on optical imaging and photochemical reaction curves is developed in this paper, and a subdomain division method with a statistics principle is proposed to improve the efficiency and accuracy of 3D OPC. Both the simulation and the experimental results show the superiority of the proposed 3D OPC method in the fabrication of Fresnel lenses. The computation memory requirements of the 3D OPC are reduced to below 1%, and the profile error of the fabricated Fresnel lens is reduced 79.98%. Applying the Fresnel lenses to an imaging system, the average peak signal to noise ratio (PSNR) of the image is increased by 18.92%, and the average contrast of the image is enhanced by 36%. We believe that the proposed 3D OPC method can be extended to the fabrication of vision-correcting ophthalmological lenses.

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Robust source and mask optimization compensating for mask topography effects in computational lithography

Cited by 35 publications

References 33 publications

The Inverse Optimization of Lithographic Source and Mask via GA-APSO Hybrid Algorithm

The Inverse Optimization of Lithographic Source and Mask via GA-APSO Hybrid Algorithm

Source optimization using hybrid genetic algorithm in lithography

An Improved 3D OPC Method for the Fabrication of High-Fidelity Micro Fresnel Lenses

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