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图神经网络增强光刻布局分解

研究人员开发了一种用于定向自组装与多重图形光刻(DSA-MP)相结合的布局分解新方法。这种新方法利用图神经网络来指导局部优化,解决了现有方法在解决方案质量、运行时间和DSA模板的关键边到边间距约束方面存在的局限性。所提出的技术旨在提高可制造性,并在各种规模和设计参数下,与当前最先进的方法相比,展示了更优的解决方案质量和更快的收敛速度。 AI

影响 这项研究通过改进先进光刻技术的布局分解,有望带来更高效、更具成本效益的半导体制造工艺。

排序理由 该集群包含一篇详细介绍半导体制造技术问题的研究论文。[lever_c_demoted from research: ic=1 ai=0.7]

在 arXiv cs.AI 阅读 →

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图神经网络增强光刻布局分解

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该集群包含一篇详细介绍半导体制造技术问题的研究论文。[lever_c_demoted from research: ic=1 ai=0.7]
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报道来源 [1]

  1. arXiv cs.AI TIER_1 English(EN) · Yu Feng, Liyi Zhou, Haoyu Wang, Yiming Xu, Hao Geng, Shaozhi Deng, Ningsheng Xu, Shisheng Xiong ·

    GNN-Guided Local Refinement for Efficient Layout Decomposition with DSA-MP

    arXiv:2610.22214v1 Announce Type: new Abstract: Multiple patterning lithography (MPL) has been widely used to improve lithography resolution in advanced nodes, but it faces cost limits as feature sizes shrink. Directed self-assembly (DSA) offers a cost-effective complement, exten…