Machine learning has been widely adopted in various disciplines as they offer low-computational cost solutions to complex problems. Recently, deep learning-enabled methods for metasurface design have received increasing attention in the field of subwavelength electromagnetics. However, the previous metasurface design methods based on deep learning usually use huge datasets or complex networks to make deep neural networks achieve high prediction accuracy which results in more time for dataset establishment and network training. Here, we propose an expeditious and accurate scheme for designing phase-modulating dielectric metasurface through employing the transfer learning technology and genetic algorithm. The performance of the neural network is improved distinctly by migrating knowledge between real part and imaginary part spectrum-prediction tasks. Furthermore, the target meta-atoms can be optimized readily without increasing a large dataset through transfer learning. Finally, we design two deflectors and two metalenses as a proof-of-concept demonstration to validate the ability of our proposed approach. The scheme provides an efficient and promising design method for phase-modulating metasurface.

中文翻译：

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具有迁移学习和遗传算法的介电超表面的有效设计

机器学习已被各个学科广泛采用，因为它们为复杂问题提供了低计算成本的解决方案。最近，用于超表面设计的深度学习方法在亚波长电磁学领域受到越来越多的关注。然而，以往基于深度学习的超表面设计方法通常使用庞大的数据集或复杂的网络来使深度神经网络达到较高的预测精度，从而导致有更多的时间用于数据集建立和网络训练。在这里，我们提出了一种通过采用转移学习技术和遗传算法来设计相位调制介电超表面的快速而准确的方案。通过在实部和虚部频谱预测任务之间迁移知识，神经网络的性能得到显着提高。此外，目标元原子可以很容易地优化，而无需通过迁移学习增加大型数据集。最后，我们设计了两个偏转器和两个超透镜作为概念验证演示，以验证我们提出的方法的能力。该方案为相位调制超表面提供了一种有效且有前景的设计方法。