Gradient-Based Source Mask Optimization for Extreme Ultraviolet Lithography

Ma, Xu; Wang, Zhiqiang; Chen, Xuanbo; Li, Yanqiu; Arce, Gonzalo R.

doi:10.1109/tci.2018.2880342

Cited by 19 publications

(2 citation statements)

References 37 publications

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“…Kawashima et al and Hakkoet alused the two-dimensional transmission cross coefficient (2D-TCC) technique as the basis for an algorithm [17], [18] and discovered that the simplified optical model could quickly determine potential SRAF sites and apply these in OPC. However, the technique was unable to further enhance the quality of aerial images, and the gradient descent algorithm used further complicated the computation by requiring the calculation of the gradient between an optimized target pattern and the corrected photomask [19], [20]. Moreover, the optimized target pattern in this design was primarily measured by the main focus and main exposure dose, meaning that it could not guarantee an increase in the process window.…”

Section: Introductionmentioning

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: Introductionmentioning

confidence: 99%

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

Kan

Huang

et al. 2019

IEEE Access

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“…进一步地, 在先进节点的光刻工艺中, 设计者、掩模版制造商和制造厂三者之间的交互, 每个环节都会影响光刻工艺性能乃至最终芯片良率. 北京理工大学 [23] [24] , 并利用可布线性调整局部设计方案 [25] , 解决跨阶段优化的相关性不足问题. 但现有工作只是初步的局部性尝试, 没有提出涉及架构层次的可实现的全局优化目标.…”

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The shift-left design paradigm of EDA: progress and challenges

LIANG,

ZHUO,

2024

Sci. Sin.-Inf.

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Shift-left and fusion is one of the important innovative design paradigms in the field of electronic design automation (EDA). The traditional EDA flow can be divided into multiple serial design stages, resulting in a long design cycle. Different from the traditional waterfall design paradigm used in EDA design flow, it proposes the use of fusion and parallelization in different stages. More clearly, through the integration and parallelization of adjacent design stages, the design cycle can be optimized effectively, and efficiency can be improved. Shift-left and fusion usually involve sharing data models between different design stages and adopting a new abstraction that can be applied to various stages of EDA flow, including high-level synthesis, logic synthesis, placement and routing, verification, process optimization, etc. In addition, with the help of early data analysis, possible problems in the late stage can be identified and addressed early, thereby reducing development and verification costs. In summary, left shift and fusion are of great importance for improving the efficiency and quality of the EDA flow. This paper will introduce and discuss the progress and challenges of the left-shift and fusion paradigm and look forward to the potential frontier research directions recommended to the National Natural Science Foundation of China.

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