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相关实验视频

Updated: Jan 10, 2026

Objectification of Tongue Diagnosis in Traditional Medicine, Data Analysis, and Study Application
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一个双阶段的Unet框架,用于分分辨率辅助功能预测.

Mu Lin1, Le Ma2,3, Lisong Dong2,3

  • 1Key Laboratory of Photoelectronic Imaging Technology and System of Ministry of Education of China, School of Optics and Photonics, Beijing Institute of Technology, Beijing 100081, China.

Micromachines
|November 27, 2025
PubMed
概括
此摘要是机器生成的。

这项研究引入了一种新的两阶段Unet框架,用于预测分辨率辅助特征 (SRAF) 参数,提高光刻精度. 该方法通过显著减少图案和边缘放置错误来提高图像保真度.

关键词:
适应性的混合注意力机制.小分辨率辅助功能提供了帮助.两个阶段的Unet网络.升温的共弦炼算法

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相关实验视频

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科学领域:

  • 半导体制造业 半导体制造业
  • 摄影石刻法 (photolithography) 是一种摄影方式.
  • 计算机成像成像技术

背景情况:

  • 亚分辨率辅助功能 (SRAFs) 对于在先进的光刻中增强对比度和过程窗口至关重要.
  • 现有的SRAF方法 (基于模型,基于规则和端到端的学习) 在适应性,计算成本或精确的几何参数提取方面存在局限性.

研究的目的:

  • 开发一种有效的基于学习的方法,用于精确的SRAF参数预测,特别是曼哈顿SRAFs.
  • 为了提高SRAF图案生成在石版画的准确性和效率.

主要方法:

  • 提出了一个两阶段的UNET框架,用于预测SRAF多边形的中心坐标和维度.
  • 集成了自适应式混合注意力机制,以增强功能集成和预测准确性.
  • 为了稳定和更快的培训,采用了热身等号化学习率策略.

主要成果:

  • 拟议的方法准确且快速估计SRAF参数.
  • 实现了从25776.44降至15203.33的平均模式误差 (PE) 和从5.8367降至3.5283的边缘位置误差 (EPE) 的显著降低.
  • 与传统的神经网络相比,该方法在预测SRAF模式方面表现优异.

结论:

  • 具有自适应混合注意力机制的两阶段Unet框架为SRAF参数预测提供了有效的解决方案.
  • 这种方法显著提高了石版系统的图像保真度.
  • 该方法克服了以前的SRAF技术的局限性,提供了更高的准确性和效率.