Optimization of photonic crystal nanocavities based on deep learning
Optimization of photonic crystal nanocavities based on deep learning
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DOI：
10.1364/oe.26.032704
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发表时间：
2018-12-10
期刊：
影响因子：
3.8
通讯作者：
Noda, Susumu
中科院分区：
文献类型：
--
作者：
Asano, Takashi;Noda, Susumu
提出并论证了一种基于深度学习的二维光子晶体（2D-PC）纳米腔Q因子优化方法。我们准备了一个由1000个纳米腔组成的数据集，这些纳米腔是通过随机置换基底纳米腔中许多空气孔的位置而产生的，并使用第一原理方法计算它们的Q因子。我们训练了一个四层神经网络，包括一个卷积层，以识别空气孔的位移和Q因子之间的关系，使用准备好的数据集。训练后，神经网络能够从气孔位移估计Q因子，标准差误差为13%。关键是，训练好的神经网络可以使用反向传播非常快速地估计Q因子相对于空气孔位移的梯度。通过在类似于10（6）次迭代中优化50个孔的位置，利用高维参数空间中的梯度的非常快速的评估，成功地获得了具有1.58 x 10（9）的极高Q因子的纳米腔结构。得到的Q因子是一个以上的数量级高于基腔和两倍以上的最高Q因子的报道，迄今为止具有类似的模态体积的腔。这种方法可以优化2D-PC结构的参数空间的大小不可行的大以前的优化方法，完全基于直接计算。我们相信这种方法对于改善其他光学特性也是有用的。(C)2018年美国光学学会根据OSA开放获取出版协议的条款
An approach to optimizing the Q factors of two-dimensional photonic crystal (2D-PC) nanocavities based on deep learning is hereby proposed and demonstrated. We prepare a data set consisting of 1000 nanocavities generated by randomly displacing the positions of many air holes in a base nanocavity and calculate their Q factors using a first-principles method. We train a four-layer neural network including a convolutional layer to recognize the relationship between the air holes' displacements and the Q factors using the prepared data set. After the training, the neural network is able to estimate the Q factors from the air holes' displacements with an error of 13% in standard deviation. Crucially, the trained neural network can estimate the gradient of the Q factor with respect to the air holes' displacements very quickly using back-propagation. A nanocavity structure with an extremely high Q factor of 1.58 x 10(9) was successfully obtained by optimizing the positions of 50 holes over similar to 10(6) iterations, taking advantage of the very fast evaluation of the gradient in high-dimensional parameter spaces. The obtained Q factor is more than one order of magnitude higher than that of the base cavity and more than twice that of the highest Q factors reported so far for cavities with similar modal volumes. This approach can optimize 2D-PC structures over a parameter space of a size unfeasibly large for previous optimization methods that were based solely on direct calculations. We believe that this approach is also useful for improving other optical characteristics. (C) 2018 Optical Society of America under the terms of the OSA Open Access Publishing Agreement