Machine learning-assisted global optimization of photonic devices
Machine learning-assisted global optimization of photonic devices
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DOI：
10.1515/nanoph-2020-0376
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发表时间：
2021-01-01
期刊：
影响因子：
7.5
通讯作者：
Boltasseva, Alexandra
中科院分区：
文献类型：
--
作者：
Kudyshev, Zhaxylyk A.;Kildishev, Alexander, V;Boltasseva, Alexandra
在过去的十年里，人工设计的光学材料和纳米结构薄膜通过使用超材料和超表面的新概念，使光子学领域发生了革命性的变化，在这些超材料和超表面中，空间变化的结构产生了可根据设计定制的有效电磁特性。目前最先进的设计和优化这类结构的方法在很大程度上依赖于其单位细胞或异原子的简单、直观的形状。这种方法不能为复杂的优化问题提供全局解决方案，在这个问题中，必须适当地选择元原子形状、平面内几何形状、平面外结构和组成材料才能产生最大性能。在这项工作中，我们提出了一种新的机器学习辅助的光子元器件设计的全局优化框架。我们证明，使用对抗性自动编码器(AAE)和元启发式优化框架相结合，可以显著提高具有复杂拓扑的元设备配置的优化搜索效率。我们展示了物理驱动的压缩设计空间工程的概念，它基于器件的光学响应将先进的正则化引入到AAE的压缩空间中。除了全局优化方案的显著进步外，我们的方法还可以通过揭示具有复杂拓扑和材料组成的元设备的光学性能的潜在物理原理，帮助获得全面的设计“直觉”。
Over the past decade, artificially engineered optical materials and nanostructured thin films have revolutionized the area of photonics by employing novel concepts of metamaterials and metasurfaces where spatially varying structures yield tailorable "by design" effective electromagnetic properties. The current state-of-the-art approach to designing and optimizing such structures relies heavily on simplistic, intuitive shapes for their unit cells or metaatoms. Such an approach cannot provide the global solution to a complex optimization problem where metaatom shape, inplane geometry, out-of-plane architecture, and constituent materials have to be properly chosen to yield the maximum performance. In this work, we present a novel machine learning-assisted global optimization framework for photonic metadevice design. We demonstrate that using an adversarial autoencoder (AAE) coupled with a metaheuristic optimization framework significantly enhances the optimization search efficiency of the metadevice configurations with complex topologies. We showcase the concept of physics-driven compressed design space engineering that introduces advanced regularization into the compressed space of an AAE based on the optical responses of the devices. Beyond the significant advancement of the global optimization schemes, our approach can assist in gaining comprehensive design "intuition" by revealing the underlying physics of the optical performance of metadevices with complex topologies and material compositions.