Global Source Optimisation Based on Adaptive Nonlinear Particle Swarm Optimisation Algorithm for Inverse Lithography

Sun, Haifeng; Du, Jing; Jin, Chuan; Feng, J.; Wang, Jian; Hu, Song; Liu, Junbo

doi:10.1109/jphot.2021.3102229

Cited by 5 publications

(2 citation statements)

References 27 publications

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“…Zhang Zhinan [19] et al increased the optimization rate of EUV SMO using SL-PSO (social learning PSO), in which the particles are updated based on historical information. Sun Haifeng [20] et al combined PSO with the adaptive nonlinear control strategy (ANCS) to break away from the local optimum.…”

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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“…With this method, the target pattern, mask pattern, and wafer pattern are represented by level set functions, then the inverse lithography problem is regarded as a shape and topology optimization problem [32,33]. There are many other miscellaneous methods in inverse lithography, such as the genetic algorithm [11], the route of particle swarm optimizer combined with the adaptive nonlinear control strategy [35], deep convolution neural network methods [40,41]. A fuller review of inverse lithography techniques could be found in [25].…”

Section: Introductionmentioning

confidence: 99%

An Alternating Direction Method of Multipliers for Inverse Lithography Problem

Liu¹

2023

NMTMA

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We propose an alternating direction method of multipliers (ADMM) to solve an optimization problem stemming from inverse lithography. The objective functional of the optimization problem includes three terms: the misfit between the imaging on wafer and the target pattern, the penalty term which ensures the mask is binary and the total variation regularization term. By variable splitting, we introduce an augmented Lagrangian for the original objective functional. In the framework of ADMM method, the optimization problem is divided into several subproblems. Each of the subproblems can be solved efficiently. We give the convergence analysis of the proposed method. Specially, instead of solving the subproblem concerning sigmoid, we solve directly the threshold truncation imaging function which can be solved analytically. We also provide many numerical examples to illustrate the effectiveness of the method.

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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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Global Source Optimisation Based on Adaptive Nonlinear Particle Swarm Optimisation Algorithm for Inverse Lithography

Cited by 5 publications

References 27 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

An Alternating Direction Method of Multipliers for Inverse Lithography Problem

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

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