欧亚联网-ILT:一个轻量级和代的面具优化解决方案与SRAF约束方案
Micromachines
|October 29, 2025
概括
本研究介绍了反向光刻技术 (ILT) 的代深度学习框架,加速计算并提高半导体制造的面具质量. 新方法改善了面罩优化,减少了复杂性,解决了先进芯片生产中的关键挑战.
科学领域:
- 半导体制造业 半导体制造业
- 计算式 lithography 的使用方法.
- 深度学习应用程序
背景情况:
- 集成电路的特征尺寸继续缩小,使得像反向光刻技术 (ILT) 这样的高级分辨率增强技术 (RET) 变得至关重要.
- 目前的ILT方法,包括机器学习和梯度下降,努力平衡面罩优化质量与计算时间.
- 在ILT中,成像真实性和可制造性之间存在一个常见的权衡,其中优先考虑真实性会增加面具的复杂性,而可制造性约束会降低真实性.
研究的目的:
- 开发一个高效和有效的ILT框架,克服现有方法的局限性.
- 为了加快运行时间并减少与面具优化相关的计算开销.
- 改进面具质量指标,减少制造复杂性,以实现先进的半导体制造.
主要方法:
- 一个基于深度学习的代ILT框架,使用轻量级模型,幽灵和适应性注意力U-net (EAAUnet).
- 通过使用预先训练的网络模型进行多次代来逐步改善面具质量.
- 实施一个面具约束方案来管理复杂的分分辨率辅助功能 (SRAF) 模式.
主要成果:
- 拟议的EAAUnet框架显著加快运行时间,减少计算开销.
- 与最先进的ILT解决方案相比,面具质量指标提高了39%.
- 面具约束方案有效地降低了SRAF模式的复杂性,提高了可制造性.
结论:
- 与EAAUnet一起的代深度学习框架为ILT提供了卓越的解决方案,平衡优化质量,计算效率和可制造性.
- 这种方法解决了先进的半导体制造中的关键挑战,通过提高面具图案的保真度和降低复杂度.
- 开发的方法代表了ILT在下一代集成电路制造方面的重大进步.
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