Intelligent nanophotonics: merging photonics and artificial intelligence at the nanoscale.
Intelligent nanophotonics: merging photonics and artificial intelligence at the nanoscale.
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DOI：
10.1515/nanoph-2018-0183
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发表时间：
2019-03
期刊：
影响因子：
7.5
通讯作者：
Zheng Y
中科院分区：
文献类型：
--
作者：
Yao K;Unni R;Zheng Y
在过去的二十年里，纳米光子学一直是一个活跃的研究领域，这是由于人们对探索纳米级光的新物理和技术的兴趣日益浓厚。随着对性能和集成度要求的不断提高，纳米光子器件的设计和优化变得计算量大且时间效率低。先进的计算方法和人工智能，特别是其机器学习子领域，已经在许多应用中取得了革命性的发展，例如网络搜索，计算机视觉和语音/图像识别。复杂的模型和算法有助于以高效的方式利用巨大的参数空间。在这篇综述中，我们总结了纳米光子学和机器学习融合的新兴领域的最新进展。我们提供了不同的计算方法的概述，重点是深度学习，用于纳米光子逆设计。还讨论了使用光子平台实现深度神经网络。这篇评论旨在勾勒出一个例子的纳米光子设计与机器学习，并给出了未来的任务的观点。
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.
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影响因子：
8.6
作者：
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