Hybrid inverse design of photonic structures by combining optimization methods with neural networks
Hybrid inverse design of photonic structures by combining optimization methods with neural networks
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DOI：
10.1016/j.photonics.2022.101073
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发表时间：
2022-10-02
影响因子：
2.7
通讯作者：
Liu, Yongmin
中科院分区：
文献类型：
--
作者：
Deng, Lin;Xu, Yihao;Liu, Yongmin
在过去的几十年里，经典的优化方法，包括基于梯度的拓扑优化和进化算法，已被广泛用于各种光子结构和器件的逆向设计，而最近的神经网络已成为一个强大的工具，用于相同的目的。虽然这些方法与传统的数值模拟方法相比都显示了一定的优越性，但它们都有各自的局限性。为了充分发挥智能光学设计的潜力，研究人员提出将优化方法与神经网络相结合，使它们能够协调工作，以进一步提高更复杂设计任务的效率，精度和能力。在这篇简短的评论中，我们将重点介绍混合模型的一些代表性例子，以展示它们的工作原理和独特的特性。
Over the past decades, classical optimization methods, including gradient-based topology optimization and the evolutionary algorithm, have been widely employed for the inverse design of various photonic structures and devices, while very recently neural networks have emerged as one powerful tool for the same purpose. Although these techniques have demonstrated their superiority to some extent compared to the conventional numerical simulations, each of them still has its own imitations. To fully exploit the potential of intelligent optical design, researchers have proposed to integrate optimization methods with neural networks, so that they can work coordinately to further boost the efficiency, accuracy and capability for more complicated design tasks. In this mini-review, we will highlight some representative examples of the hybrid models to show their working principles and unique proprieties.