High Speed Simulation and Freeform Optimization of Nanophotonic Devices with Physics-Augmented Deep Learning
High Speed Simulation and Freeform Optimization of Nanophotonic Devices with Physics-Augmented Deep Learning
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DOI：
10.1021/acsphotonics.2c00876
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发表时间：
2022-08
期刊：
影响因子：
7
通讯作者：
Ming-Keh Chen;Robert Lupoiu;Chenkai Mao;Der-Han Huang;Jiaqi Jiang;P. Lalanne;Jonathan A. Fan
中科院分区：
文献类型：
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作者：
Ming-Keh Chen;Robert Lupoiu;Chenkai Mao;Der-Han Huang;Jiaqi Jiang;P. Lalanne;Jonathan A. Fan
我们介绍了Wavey-Net，这是一种数据和物理混合增强的卷积神经网络，可以超高速和高精度地预测整个类型的介电纳米光子结构的电磁场分布。这种精度是通过训练神经网络只学习系统的磁场近场分布并以两种方式使用麦克斯韦方程的离散形式来实现的：从磁场计算电场和作为损耗函数中的物理约束。结果表明，Wavey-Net能够准确地预测周期性、高介电对比度纳米结构阵列中的近场，并可以与基于梯度的算法相结合，以数量级的速度显著加速衍射光子器件的局部和全局自由形式优化。我们预计，物理增强的深度神经网络将改变纳米光子学模拟和设计的实践。
: We introduce WaveY-Net, a hybrid data-and physics-augmented convolutional neural network that can predict electromagnetic field distributions with ultrafast speeds and high accuracy for entire classes of dielectric nanophotonic structures. This accuracy is achieved by training the neural network to learn only the magnetic near-field distributions of a system and to use a discrete formalism of Maxwell’s equations in two ways: to calculate electric fields from the magnetic fields and as physical constraints in the loss function. We show that WaveY-Net can accurately predict the near-fields in periodic, high dielectric contrast nanostructure arrays, and that it can combine with gradient-based algorithms to dramatically accelerate the local and global freeform optimization of diffractive photonic devices by orders of magnitude faster speeds. We anticipate that physics-augmented deep neural networks will transform the practice of nanophotonics simulation and design.