Iterative optimization of photonic crystal nanocavity designs by using deep neural networks
Iterative optimization of photonic crystal nanocavity designs by using deep neural networks
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DOI：
10.1515/nanoph-2019-0308
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发表时间：
2019-12-01
期刊：
影响因子：
7.5
通讯作者：
Noda, Susumu
中科院分区：
文献类型：
--
作者：
Asano, Takashi;Noda, Susumu
基于二维光子晶体纳米腔（由其气孔图案定义）的器件通常需要高质量（Q）因子才能实现高性能。我们证明，通过迭代过程可以有效地找到具有非常高 Q 因子的孔图案，该迭代过程包括孔图案与相应 Q 因子之间关系的机器学习以及基于机器学习获得的回归函数生成新数据集。首先，准备一个包含随机生成的空腔结构及其第一原理 Q 因子的数据集。然后使用初始数据集训练深度神经网络，以获得从结构参数近似预测 Q 因子的回归函数。通过使用回归函数搜索参数空间来选择几个较高 Q 因子的候选者。将这些新结构及其第一原理 Q 因子添加到训练数据集中后，重复上述过程。例如，标准硅基 L3 腔通过此方法进行了优化。在 101 个迭代步骤和总共 8070 个腔体结构中找到了 Q 值超过 1100 万的腔体设计。该理论 Q 因子是之前报道的通过进化算法和泄漏模式可视化方法检测到的腔体设计记录值的两倍多。研究发现，通过探索当前最高 Q 结构附近的参数空间以及距当前数据集较远的参数空间，可以在较少的迭代步骤内检测到具有较高 Q 因子的结构。
Devices based on two-dimensional photonic-crystal nanocavities, which are defined by their air hole patterns, usually require a high quality (Q) factor to achieve high performance. We demonstrate that hole patterns with very high Q factors can be efficiently found by the iteration procedure consisting of machine learning of the relation between the hole pattern and the corresponding Q factor and new dataset generation based on the regression function obtained by machine learning. First, a dataset comprising randomly generated cavity structures and their first principles Q factors is prepared. Then a deep neural network is trained using the initial dataset to obtain a regression function that approximately predicts the Q factors from the structural parameters. Several candidates for higher Q factors are chosen by searching the parameter space using the regression function. After adding these new structures and their first principles Q factors to the training dataset, the above process is repeated. As an example, a standard silicon-based L3 cavity is optimized by this method. A cavity design with a high Q factor exceeding 11 million is found within 101 iteration steps and a total of 8070 cavity structures. This theoretical Q factor is more than twice the previously reported record values of the cavity designs detected by the evolutionary algorithm and the leaky mode visualization method. It is found that structures with higher Q factors can be detected within less iteration steps by exploring not only the parameter space near the present highest-Q structure but also that distant from the present dataset.