Fast source independent estimation of lithographic difficulty supporting large scale source optimization

DeMaris, David; Gabrani, Maria; Sarkar, Sujoy; Casati, Nathalie; Luijten, Ronald P.; Lai, Kafai; Tian, Kehan

doi:10.1117/12.916433

Cited by 3 publications

(3 citation statements)

References 6 publications

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“…As a significant ILT approach, pixelated source optimization (SO) has been proven to be necessary for improving the imaging performance of lithography in advanced technology nodes [8], [9]. Furthermore, it has been successfully applied by several institutions, such as ASML [10]- [12] and IBM [13], [14], to modulate the intensity distribution and incident angles of lithography-illumination sources in industrial applications [15], [16]. However, the highly complex representation of the source impacts the performance of the pixelated SO method.…”

Section: Introductionmentioning

confidence: 99%

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%

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

et al. 2023

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“…The pixeled SO method, as an effective resolution enhancement technique, has been confirmed to improve the lithographic imaging performance at advanced nodes. And it has been successfully utilized in practical applications, such as ASML [2][3][4] , IBM 5,6 , and other institutions. Revising and optimizing the intensity of unit pixel in the pixeled source can be accomplished using several iterative algorithms, including gradient-based approaches and heuristic algorithms [7][8][9] .…”

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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“…Objectives include diffraction order-based diversity ( [7], [8]), lithographic difficulty estimation (LDE) coverage, image parameter distributions and pattern orientation/dimension ratios. This ensures that the resulting automatic sample plan is at least of equivalent quality to the manual sample plan, but generated in a shorter time due to the automated nature of the method.…”

Section: Introductionmentioning

confidence: 99%

Automated sample plan selection for OPC modeling

et al. 2014

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It is desired to reduce the time required to produce metrology data for calibration of Optical Proximity Correction (OPC) models and also maintain or improve the quality of the data collected with regard to how well that data represents the types of patterns that occur in real circuit designs. Previous work based on clustering in geometry and/or image parameter space has shown some benefit over strictly manual or intuitive selection, but leads to arbitrary pattern exclusion or selection which may not be the best representation of the product. Forming the pattern selection as an optimization problem, which co-optimizes a number of objective functions reflecting modelers' insight and expertise, has shown to produce models with equivalent quality to the traditional plan of record (POR) set but in a less time.

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Fast source independent estimation of lithographic difficulty supporting large scale source optimization

Cited by 3 publications

References 6 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

Source optimization using hybrid genetic algorithm in lithography

Automated sample plan selection for OPC modeling

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