Inverse design of anisotropic and multi-resonant absorbers based on black phosphorus via residual neural network
Inverse design of anisotropic and multi-resonant absorbers based on black phosphorus via residual neural network
复制标题
DOI：
10.1088/2040-8986/ac5f8f
复制
发表时间：
2022
影响因子：
2.1
通讯作者：
Yijun Cai;Kehao Feng;Yuanguo Zhou;Yingshi Chen;Chengying Chen;R. Abdi-Ghaleh;Jinfeng Zhu
中科院分区：
文献类型：
--
作者：
Yijun Cai;Kehao Feng;Yuanguo Zhou;Yingshi Chen;Chengying Chen;R. Abdi-Ghaleh;Jinfeng Zhu
黑磷（BP）作为一种新型二维材料，因其优异的性能而受到广泛关注。 BP的各向异性使得其物理性质在不同方向上变化很大，增加了BP超材料设计的复杂性。提出一种基于改进的自适应批归一化算法的残差神经网络，实现基于BP的多层薄膜结构的逆向设计，并采用特征矩阵法获得完美的光吸收样本。神经网络模型对于单共振和多共振吸波结构的预测精度均超过95%。该方法具有收敛速度快、预测精度高等优点，达到了基于BP超材料结构的设计目标。
Black phosphorus (BP), a new type of two-dimensional material, has attracted extensive attention because of its excellent properties. The anisotropy of BP makes its physical properties vary greatly in different directions, which increases the complexity of the design of BP metamaterials. We present a residual neural network on the basis of the improved adaptive batch normalization algorithm to achieve the inverse design of a multilayer thin film structure based on BP, and we adopt the characteristic matrix method to obtain perfect optical absorption samples. The prediction accuracy of the neural network model is more than 95% for absorbing structures with both single and multiple resonances. This method has the advantages of a fast rate of convergence and high precision of prediction and achieves the design target on the basis of the structure of a BP metamaterial.