Yao Kan, Unni Rohit, Zheng Yuebing
Department of Mechanical Engineering, The University of Texas at Austin, Austin, TX 78712, USA.
Texas Materials Institute, The University of Texas at Austin, Austin, TX 78712, USA.
Nanophotonics. 2019 Mar;8(3):339-366. doi: 10.1515/nanoph-2018-0183. Epub 2019 Jan 25.
Nanophotonics has been an active research field over the past two decades, triggered by the rising interests in exploring new physics and technologies with light at the nanoscale. As the demands of performance and integration level keep increasing, the design and optimization of nanophotonic devices become computationally expensive and time-inefficient. Advanced computational methods and artificial intelligence, especially its subfield of machine learning, have led to revolutionary development in many applications, such as web searches, computer vision, and speech/image recognition. The complex models and algorithms help to exploit the enormous parameter space in a highly efficient way. In this review, we summarize the recent advances on the emerging field where nanophotonics and machine learning blend. We provide an overview of different computational methods, with the focus on deep learning, for the nanophotonic inverse design. The implementation of deep neural networks with photonic platforms is also discussed. This review aims at sketching an illustration of the nanophotonic design with machine learning and giving a perspective on the future tasks.
在过去二十年中，纳米光子学一直是一个活跃的研究领域，这是由探索纳米尺度光的新物理和技术的兴趣不断上升所引发的。随着对性能和集成水平的要求不断提高，纳米光子器件的设计和优化在计算上变得昂贵且效率低下。先进的计算方法和人工智能，尤其是机器学习这一子领域，在许多应用中带来了革命性的发展，如网络搜索、计算机视觉以及语音/图像识别。复杂的模型和算法有助于高效地探索巨大的参数空间。在本综述中，我们总结了纳米光子学与机器学习融合这一新兴领域的最新进展。我们概述了不同的计算方法，重点是用于纳米光子逆向设计的深度学习。还讨论了光子平台上深度神经网络的实现。本综述旨在勾勒出利用机器学习进行纳米光子设计的示意图，并展望未来的任务。
