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相关概念视频

Non-equilibrium in the Cell01:16

Non-equilibrium in the Cell

An important concept in studying metabolism and energy is that of chemical equilibrium. Most chemical reactions are reversible. They can proceed in both directions, releasing energy into their environment in one direction, and absorbing it from the environment in the other direction. The same is true for the chemical reactions involved in cell metabolism, such as the breaking down and building up of proteins into and from individual amino acids, respectively. Reactants within a closed system...

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适应性增强学习用于光刻优化:用于下一代半导体制造的可扩展AI驱动的解决方案.

Umar Rashid1,2, Fahad Shafique1, Hamza Atif1

  • 1Department of Electrical Engineering, University of Engineering and Technology, New Campus, Lahore, Pakistan.

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概括

适应性增强学习以优化石版 (ARLO) 通过减少图案错误来增强半导体石版. 这种人工智能方法优化了用于亚纳米集成电路制造的光罩,提高了精度和效率.

关键词:
计算式 lithography 的使用方法.深度神经网络是一种深度神经网络.反向光刻技术的反向光刻技术.面具优化优化 面具优化强化学习是一种强化学习.半导体制造业 半导体制造业

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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.
  • 在工程领域的人工智能.

背景情况:

  • 半导体光刻对集成电路 (IC) 制造至关重要,占成本的30%左右.
  • 将特征尺寸缩小到亚纳米尺度,带来了光学衍射和扭曲的挑战.
  • 传统的光学近距离校正 (OPC) 是不够的;反光刻法技术 (ILT) 是计算密集的.

研究的目的:

  • 引入适应性增强学习以优化石版画 (ARLO) 进行先进的光罩优化.
  • 解决现有的ILT方法的可扩展性和计算复杂性的局限性.
  • 为了提高图案保真度,减少半导体光刻的工艺变化.

主要方法:

  • 开发了一个基于U-Net的框架,整合了自我注意力和强化学习 (RL).
  • 采用了使用实时光刻模拟的代光罩优化.
  • 根据LithoBench基准指标对最先进的方法进行评估.

主要成果:

  • ARLO实现了37.8%的减少[公式:见文本]损失和74.0%的减少过程变化带 (PVB) 与GAN-OPC相比.
  • 在损失和PVB方面,与Deep LithoNet (DLN) 和RL-ILT相比显著改善.
  • 保持了0.035秒的竞争性运行时间,尽管增加了射击数量.

结论:

  • 阿洛为下一代半导体制造提供了可扩展和高效的解决方案.
  • 拟议的框架有效地优化了光罩,提高了图案保真度,减少了工艺变化.
  • ARLO提供了一个有前途的AI驱动的方法来克服亚纳米光刻的挑战.