Tackling Photonic Inverse Design with Machine Learning.
Tackling Photonic Inverse Design with Machine Learning.
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DOI：
10.1002/advs.202002923
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发表时间：
2021-03
期刊：
影响因子：
--
通讯作者：
Cai W
中科院分区：
文献类型：
--
作者：
Liu Z;Zhu D;Raju L;Cai W
机器学习作为一种基于复杂数据自动预测和决策的算法研究，已经成为人工智能研究中最有效的工具之一。近年来，科学界逐渐将数据驱动的方法与研究相结合，在揭示潜在机制、预测基本属性和发现非常规现象方面取得了巨大进展。它正在成为量子物理、有机化学和医学成像等领域不可或缺的工具。最近，机器学习已被用于光子学和光学的研究中，作为解决逆设计问题的替代方法。本文总结了近年来基于机器学习的光子设计策略的快速发展。特别是，深度学习方法，机器学习算法的一个子集，处理棘手的高自由度结构设计的重点。利用工程光子结构对光进行非常规控制是现代光学的一个核心主题。近年来，在光子材料和器件的设计中探索人工智能的趋势越来越明显。本文总结了基于机器学习的光子设计策略的快速发展，特别强调了解决高自由度结构的深度学习方法。
Machine learning, as a study of algorithms that automate prediction and decision‐making based on complex data, has become one of the most effective tools in the study of artificial intelligence. In recent years, scientific communities have been gradually merging data‐driven approaches with research, enabling dramatic progress in revealing underlying mechanisms, predicting essential properties, and discovering unconventional phenomena. It is becoming an indispensable tool in the fields of, for instance, quantum physics, organic chemistry, and medical imaging. Very recently, machine learning has been adopted in the research of photonics and optics as an alternative approach to address the inverse design problem. In this report, the fast advances of machine‐learning‐enabled photonic design strategies in the past few years are summarized. In particular, deep learning methods, a subset of machine learning algorithms, dealing with intractable high degrees‐of‐freedom structure design are focused upon. Employing engineered photonic structures for unconventional control of light represents a central theme in modern optics. The last few years have witnessed a growing trend of exploring artificial intelligence for the design of photonic materials and devices. Herein, the fast advances of machine‐learning‐enabled photonic design strategies are summarized, with particular emphasis on deep learning methods that tackle high degrees‐of‐freedom structures.
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