聊天到芯片:基于大型语言模型设计的任意形状的超表面
Huanshu Zhang1, Lei Kang1, Sawyer D Campbell1
1Department of Electrical Engineering, The Pennsylvania State University, University Park, PA, 16802, USA.
Nanophotonics (Berlin, Germany)
|November 10, 2025
概括
大型语言模型 (LLM) 通过从几何描述中学习物理关系,使复杂的元表面的快速设计成为可能. 这种"聊天到芯片"的方法简化了纳米光子设备的开发.
科学领域:
- 纳米光子学 纳米光子学
- 计算电磁学 计算机电磁学
- 人工智能的人工智能
背景情况:
- 超表面设计传统上依赖于计算密集的全波模拟,限制了复杂几何体的探索.
- 数据驱动的方法,如神经网络,加速元原子设计,但需要特定任务的培训和架构搜索.
- 现有的方法需要大量的新光学功能的努力,缺乏用户友好的界面.
研究的目的:
- 调查预先训练的大型语言模型 (LLM) 设计任意形状的超表面的有效性.
- 为了建立一个
- 聊天到芯片的聊天
- 工作流程将自然语言与纳米光子设备设计的电磁建模联系起来.
- 为了对各种LLM进行基准测试,用于光谱预测和反向设计任务.
主要方法:
- 使用1D代币智能的LLM,训练用地表几何形状的描述性输入.
- 用几何描述来学习用于光谱预测和反向设计的物理关系的LLM.
- 基准公开权重LLM,分析模型大小和预测准确性之间的相关性.
主要成果:
- 证明了LLM可以准确预测超表面光谱,并执行反向设计任务.
- 在10亿参数尺度上确定了LLM的性能-准确性关系.
- 展示了1D代币智能LLM的实用性,用于设计2D任意形状的元表面.
结论:
- 在超表面设计中,LLM为传统的基于模拟的方法提供了可行的,高效的替代方案.
- 拟议的 拟议的 拟议的
- 聊天到芯片的聊天
- 在数据驱动的纳米光子学中,工作流显著提高了用户友好性.
- 这种方法代表了向可访问和直观的纳米光子设备工程迈出的重要一步.
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