深度卷积混合密度网络用于层级光子结构的反向设计
Rohit Unni1, Kan Yao1, Yuebing Zheng1
1Walker Department of Mechanical Engineering and Texas Materials Institute, The University of Texas at Austin, Austin, Texas 78712, United States.
ACS photonics
|November 30, 2023
概括
本研究引入了一个混合密度网络 (MDN),以解决纳米光子反向设计的机器学习中的非唯一性问题. MDN方法有效地处理复杂的光谱,并确定光子结构的多个有效解决方案.
科学领域:
- 纳米光子学 纳米光子学
- 机器学习 机器学习
- 材料科学 材料科学 材料科学
背景情况:
- 机器学习 (ML) 对纳米光子反向设计至关重要.
- 光学光谱的非唯一性阻碍了传统的ML算法融合.
- 截然不同的设计可以产生相同的光谱特性,这是一项挑战.
研究的目的:
- 为了解决ML驱动的纳米光子反向设计中的非唯一性问题.
- 开发一种能够处理设计参数多式模式概率分布的ML方法.
- 为了实现非独特解决方案的融合,而不会失去退化的可能性.
主要方法:
- 引入了一种混合密度网络 (MDN) 方法.
- 模拟设计参数作为多式模式概率分布,而不是离散值.
- 应用MDN技术反向设计多层氧化物薄膜光子结构 (10层和4层的情况下).
主要成果:
- 对于一个10层结构,MDN成功地处理了复杂的传输光谱和不同的照明条件.
- 四层案例显示了更强的多式联运特征,揭示了替代的光谱解决方案.
- MDN的概率输出提供了关于预测不确定性的有价值的后处理信息.
结论:
- MDN方法为光子结构的反向设计提供了有效的解决方案,特别是那些具有高度退化和光谱复杂性的结构.
- 这种方法通过适应非独特的解决方案,增强了对最佳纳米光子设计的搜索.
- 该技术提供了对设计不确定性的洞察,提高了ML驱动反向设计的可靠性.
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