Opt Express. 2021 Aug 30;29(18):28406-28415. doi: 10.1364/OE.435427.
Deep neural networks (DNNs) have been used as a new method for nanophotonic inverse design. However, DNNs need a huge dataset to train if we need to select materials from the material library for the inverse design. This puts the DNN method into a dilemma of poor performance with a small training dataset or loss of the advantage of short design time, for collecting a large amount of data is time consuming. In this work, we propose a multi-scenario training method for the DNN model using imbalanced datasets. The imbalanced datasets used by our method is nearly four times smaller compared with other training methods. We believe that as the material library increases, the advantages of the imbalanced datasets will become more obvious. Using the high-precision predictive DNN model obtained by this new method, different multilayer nanoparticles and multilayer nanofilms have been designed with a hybrid optimization algorithm combining genetic algorithm and gradient descent optimization algorithm. The advantage of our method is that it can freely select discrete materials from the material library and simultaneously find the inverse design of discrete material type and continuous structural parameters of the nanophotonic devices.
深度神经网络 (DNN) 已被用作纳米光子学反向设计的新方法。然而，如果我们需要从材料库中选择材料进行反向设计，那么 DNN 需要一个庞大的数据集进行训练。这使得 DNN 方法陷入了困境，即数据集小则性能差，数据集大则设计时间短的优势丧失，因为收集大量数据非常耗时。在这项工作中，我们提出了一种使用不平衡数据集的 DNN 模型的多场景训练方法。与其他训练方法相比，我们方法中使用的不平衡数据集要小近四倍。我们相信，随着材料库的增加，不平衡数据集的优势将变得更加明显。使用这种新方法获得的高精度预测 DNN 模型，我们结合遗传算法和梯度下降优化算法的混合优化算法，设计了不同的多层纳米粒子和多层纳米薄膜。我们方法的优点是可以从材料库中自由选择离散材料，并同时找到离散材料类型和纳米光子器件连续结构参数的反向设计。
