Ant Colony Optimization-Based Freeform Sources for Enhancing Nanolithographic Imaging Performance

Kuo, Hung-Fei

doi:10.1109/tnano.2016.2560221

Cited by 15 publications

(9 citation statements)

References 37 publications

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“…[57]- [61] (explained in Section B-A1); 3) Binary Bat algorithm (BBA) [30], [39], [62]- [65] (explained in Section B-A2); 4) Binary Cat Swarm Optimization (BCSO) [28], [66], [67], [67], [68], [68], [69] (explained in Section B-A3); 5) Binary Gravitational search algorithm (BGSA) [37], [51], [70]- [73] (explained in Section B-A4); 6) Grey Wolf Optimizer (GWO) [7], [74]- [79] (explained in Section B-A5); 7) Binary Particle Swarm Optimization (BPSO) [31], [38], [40], [74], [80]- [96] (explained in Section A-D). As the key idea in swarm intelligence is to update a new position of an agent considering a previous position, a collective position or a neighbour position, the algorithms provide adaptations for the original equations to incorporate binary solutions.…”

Section: B Binary-continuous Algorithmsmentioning

confidence: 99%

“…The pheromone evaporates over time, and its concentration decreases with the path length. The ants are more likely to follow the trails that present the highest pheromone concentrations [59], [61].…”

Section: ) Ant-inspiredmentioning

confidence: 99%

“…However, they disregard these parameters in their applications, as in [60] and [57]. Other works, like [61] and [58], define this probability without mentioning the visibility. In both cases, (21) can be rewritten as (23) [61]:…”

Section: ) Ant-inspiredmentioning

confidence: 99%

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Overview on Binary Optimization Using Swarm-Inspired Algorithms

et al. 2021

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Swarm Intelligence is applied to optimisation problems due to its robustness, scalability, generality, and flexibility. Based on simple rules, simple reactive agents -swarm (e.g. fish, bird, and ant)directly or indirectly exchange information to find an optimal solution. Among multiple nature inspirations and versions, the dilemma of choosing proper swarm-based algorithms for each type of problem prevents their recurrent application. This scenario gets even more challenging when considering binary optimisation because of the absence of overview papers that assembles the trends, benefits and limitations of swarmbased techniques. Based on 403 scientific papers, we describe the basis of the leading binary swarm-based algorithms presenting their rationales, equations, pseudocodes, and descriptions of their applications to tackle this research gap. We also define a new classification based on the mechanism to update the solutions and the displacements, indicating that the Binary-Binary approach -binary decision variables and binary search space -is more efficient for binary optimisation in accuracy and computational cost.

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Section: B Binary-continuous Algorithmsmentioning

confidence: 99%

Section: ) Ant-inspiredmentioning

confidence: 99%

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Overview on Binary Optimization Using Swarm-Inspired Algorithms

et al. 2021

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“…This study employed edge placement error (EPE) as the fitness function for GA optimization [46], [47], defining an arbitrary critical shape (CS) point on the target pattern as the displacement error from the aerial image contour (Fig. 8).…”

Section: Ga-based Sraf Designmentioning

confidence: 99%

Placement of Sub-Resolution Assist Features Based on a Genetic Algorithm

Kan

Huang

et al. 2019

IEEE Access

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Resolution enhancement techniques compatible with an ArF (193 nm) immersion optical lithography system may constitute an effective means of minimizing the size of technology nodes of the dynamic random access memory. This paper investigated one such technique, namely mask optimization (MO), and applied sub-resolution assist features (SRAFs) in the MO to improve the aerial image quality of a target pattern that had undergone optical proximity correction (OPC). This paper first developed an optical model based on the Hopkins model to create an interference map, which was then used to create a cutlevel map. The cut-level map was instrumental in predicting potential SRAF sites and randomly generating SRAFs that would serve as the initial population for a genetic algorithm (GA). Chromosomes were defined as a section map and encoded genes were used to define SRAF and target pattern. Using a GA to identify SRAF geometric measurements and placement was revealed to increase the process window and improve the image performance of the target pattern. This paper used 1D and 2D line/space (L/S) images as the baseline to test the convergence of the proposed method. 2D images were also used to test improvements in aerial image performance. The results indicated that the 1D L/S pattern converged at the 100th iteration. Furthermore, the depths of the focus of the 2D L/S array and 2D contact hole patterns were successfully increased by 113 and 21 nm, respectively. The proposed SRAF method, which integrated the GA and interference map, was able to ensure the diversity of potential SRAF solutions. Moreover, it was able to restrict the SRAF solutions to rectangular structures through the application of mask rules, thereby reducing the cost and improving the feasibility of photomasks. INDEX TERMS Sub-resolution assist feature (SRAF), interference map, genetic algorithm (GA), mask optimization, depth of focus (DOF).

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“…In recent years, ACO algorithm has been successfully applied to solve real optimization problems. ese problems include visual tracking, the traveling salesman problem (TSP) [4][5][6], vehicle routing [7], clustering [8], and image processing [9,10].…”

Section: Introductionmentioning

confidence: 99%

A Novel Gaussian Ant Colony Algorithm for Clustering Cell Tracking

Jin

et al. 2021

Discrete Dynamics in Nature and Society

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Cell behavior analysis is a fundamental process in cell biology to obtain the correlation between many diseases and abnormal cell behavior. Moreover, accurate number estimation plays an important role for the construction of cell lineage trees. In this paper, a novel Gaussian ant colony algorithm, for clustering or spatial overlap cell state and number estimator, simultaneously, is proposed. We have introduced a novel definition of the Gaussian ant system borrowed from the concept of the multi-Bernoulli random finite set (RFS) in the way that it encourages ants searching for cell regions effectively. The existence probability of ant colonies is considered for the number and state estimation of cells. Through experiments on two real cell sequences, it is confirmed that our proposed algorithm could automatically track clustering cells in various scenarios and has enabled superior performance compared with other state-of-the-art approaches.

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Ant Colony Optimization-Based Freeform Sources for Enhancing Nanolithographic Imaging Performance

Cited by 15 publications

References 37 publications

Overview on Binary Optimization Using Swarm-Inspired Algorithms

Overview on Binary Optimization Using Swarm-Inspired Algorithms

Placement of Sub-Resolution Assist Features Based on a Genetic Algorithm

A Novel Gaussian Ant Colony Algorithm for Clustering Cell Tracking

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