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加快聚合物自我一致场模拟和用深度神经网络进行反向DSA光刻.

Haolan Wang1,2, Sikun Li1,2, Jiale Zeng1

  • 1Department of Advanced Optical and Microelectronic Equipment, Shanghai Institute of Optics and Fine Mechanics, Chinese Academy of Sciences, Shanghai 201800, China.

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概括

我们开发了一种深度学习方法,以加速自相一致的场理论 (SCFT) 模拟用于块共聚合物 (BCP) 自组装. 这种方法通过使用深度神经网络 (DNN) 来从早期模拟数据中预测平衡结构,显著减少了计算时间.

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科学领域:

  • 聚合物科学 聚合物科学
  • 计算化学的计算化学
  • 材料科学 材料科学 材料科学

背景情况:

  • 自相一致的场理论 (SCFT) 对于研究块共聚合物 (BCP) 自组装至关重要.
  • 在计算上,SCFT模拟是昂贵的,这阻碍了需要广泛前向模拟的应用程序.

研究的目的:

  • 通过基于深度学习的方法加速SCFT模拟.
  • 为了降低BCP自组装研究的计算成本.
  • 为了提高BCP自组装中反向设计问题的效率.

主要方法:

  • 一个深度神经网络 (DNN) 被训练来从早期的SCFT代输出中预测平衡聚合物结构.
  • 在反向设计方法中,DNN模型取代了计算密集型前向模拟.
  • 该方法在2D和3D散装系统上得到了验证,并应用于反向定向自组装光刻法.

主要成果:

  • DNN从有限的SCFT代中准确预测平衡状态,显著减少模拟时间.
  • 初始SCFT代的数量可以优化以实现速度准确性权衡.
  • 训练集大小影响了DNN的性能,为成本效益高的数据集生成提供了指导.
  • 通过DNN加速反向设计方法提高了100倍的效率,消除了SCFT模拟.

结论:

  • 深度学习提供了一种强大的策略,可以加速SCFT模拟用于BCP自组装.
  • 这种方法大大减少了对前向和反向设计问题的计算需求.
  • 这种方法有助于更有效地探索复杂的聚合物自组装现象和材料设计.