Demonstration of ACO-Based Freeform Source for ArF Laser Immersion Lithography System

Lie, Frederick; Huang, Chengcheng; Wu, Chunsheng; Chen, Kao-Tun; Kuo, Hung-Fei

doi:10.1109/access.2017.2694854

Cited by 2 publications

(1 citation statement)

References 39 publications

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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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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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Sampling-based imaging model for fast source and mask optimization in immersion lithography

Sun¹,

Li²,

Liao³

et al. 2022

Appl. Opt.

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Current source and mask optimization (SMO) research tends to focus on advanced inverse optimization algorithms to accelerate SMO procedures. However, innovations of forward imaging models currently attract little attention, which impacts computational efficiency more significantly. A sampling-based imaging model is established with the innovation of an inverse point spread function to reduce computational dimensions, which can provide an advanced framework for fast inverse lithography. Simulations show that the proposed SMO method with the help of the proposed model can further speed up the algorithm-accelerated SMO procedure by a factor of 3.

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Demonstration of ACO-Based Freeform Source for ArF Laser Immersion Lithography System

Cited by 2 publications

References 39 publications

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

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

Sampling-based imaging model for fast source and mask optimization in immersion lithography

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