Global Optimization of Dielectric Metasurfaces Using a Physics-Driven Neural Network
Global Optimization of Dielectric Metasurfaces Using a Physics-Driven Neural Network
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DOI：
10.1021/acs.nanolett.9b01857
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发表时间：
2019-08-01
期刊：
影响因子：
10.8
通讯作者：
Fan, Jonathan A.
中科院分区：
文献类型：
--
作者：
Jiang, Jiaqi;Fan, Jonathan A.
我们提出了一个全局优化器，基于条件生成神经网络，它可以输出集合的高效拓扑优化的元表面操作范围内的参数。该网络的一个关键特征是，它最初生成对设计空间进行广泛采样的器件分布，然后在优化过程中将该分布向有利的设计空间区域移动和细化。通过计算输出设备的前向和伴随电磁模拟并使用随后的效率梯度进行反向传播来执行训练。随着metagratings在一系列波长和角度作为一个模型系统，我们表明，从训练的生成网络产生的设备具有效率可比或优于基于伴随的拓扑优化产生的最佳设备，同时需要更少的计算成本。我们对生成神经网络训练的基于伴随的优化的重构通常适用于可以利用梯度来提高性能的物理系统。
We present a global optimizer, based on a conditional generative neural network, which can output ensembles of highly efficient topology-optimized metasurfaces operating across a range of parameters. A key feature of the network is that it initially generates a distribution of devices that broadly samples the design space and then shifts and refines this distribution toward favorable design space regions over the course of optimization. Training is performed by calculating the forward and adjoint electromagnetic simulations of outputted devices and using the subsequent efficiency gradients for backpropagation. With metagratings operating across a range of wavelengths and angles as a model system, we show that devices produced from the trained generative network have efficiencies comparable to or better than the best devices produced by adjoint-based topology optimization, while requiring less computational cost. Our reframing of adjoint-based optimization to the training of a generative neural network applies generally to physical systems that can utilize gradients to improve performance.