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Micro/nano optical materials and devices are the key to many optical fields such as optical communication, optical sensing, biophotonics, laser, and quantum optics, etc. At present, the design of micro/nano optics mainly relies on the numerical methods such as Finite-difference time-domain (FDTD), Finite element method (FEM) and Finite difference method (FDM). These methods bottleneck the current micro/nano optical design because of their dependence on computational resources, low innovation efficiency, and difficulties in obtaining global optimal design. Artificial intelligence (AI) has brought a new paradigm of scientific research: AI for Science, which has been successfully applied to chemistry, materials science, quantum mechanics, and particle physics. In the area of micro/nano design AI has been applied to the design research of chiral materials, power dividers, microstructured optical fibers, photonic crystal fibers, chalcogenide solar cells, plasma waveguides, etc. According to the characteristics of the micro/nano optical design objects, the datasets can be constructed in the form of parameter vectors for complex micro/nano optical designs such as hollow core anti-resonant fibers with multi-layer nested tubes, and in the form of images for simple micro/nano optical designs such as 3dB couplers. The constructed datasets are trained with artificial neural network, deep neural network and convolutional neural net algorithms to fulfill the regression or classification tasks for performance prediction or inverse design of micro/nano optics. The constructed AI models are optimized by adjusting the performance evaluation metrics such as mean square error, mean absolute error, and binary cross entropy. In this paper, the application of AI in micro/nano optics design is reviewed, the application methods of AI in micro/nano optics are summarized, and the difficulties and future development trends of AI in micro/nano optics research are analyzed and prospected.
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文献 年度 设计对象 应用领域 样本定义 标记定义 数据集样本数/来源 AI实现功能 学习任务 算法 性能度量指标 [15] 2020 等离子体超材料 材料 结构向量 圆二色性 28106/模拟 逆向设计 回归 DNN MSE [19] 2021 偏振分束器 通信 结构图像 透射率 —/实验 性能预测 回归 CNN MSE [46] 2019 手性纳米结构 通信 结构图像 圆二色性 10000/实验 性能优化 回归 BoNet MSE [47] 2020 3 dB功率分配器、
解模复用器通信 像素图像 透射率 —/实验 逆向设计 回归 digitized adjoint method MSE [48] 2021 空芯反谐振光纤 通信 结构向量 限制损耗 323000/模拟 性能预测 分类 KNN, decision tree Accuracy [49] 2021 光子晶体光纤 通信 结构向量 限制损耗 1000/模拟 性能预测 回归 GAN, ANN MSE [50] 2019 等离子体波导 通信 结构向量 透射率 20000/模拟 性能预测 回归 ANN, GA Accuracy [51] 2021 光栅耦合器 通信 结构向量 耦合效率 —/模拟 性能预测 回归 DNN MSE [52] 2022 模式耦合器 通信 结构向量 有效折射率 —/模拟 性能预测 回归 DNN, GA MSE [53] 2021 多端口多模波导 通信 像素图像 透射率 2500/模拟 性能预测 回归 ANN MSE [55] 2022 超表面 电磁波 结构图像 反射率、Ex、Ey相位 1000/实验 逆向设计 回归 CNN MSE [58] 2022 钙钛矿太阳能电池 光伏 结构向量 PCE能量转换效率 —/实验 性能预测 回归 LR, SVR, KNR,
RFR, GBR, NNRMSE, MAE [59] 2022 平顶光束激光器 激光器 结构向量 折射率 —/模拟 逆向设计 回归 ANN MSE [60] 2022 光纤 通信 结构向量 色散, 折射率差 1368/模拟 性能预测/
逆向设计回归/分类 PSO、MOPSO MSE [61] 2021 超表面 通信 结构图像 散射体的辐射模式 98000/模拟 性能预测 回归 DNN L2 loss [62] 2022 微纳硅基器件 加工 结构图像 distance 50680/实验 性能预测 回归 CNN BCE [63] 2022 光纤传输模型 通信 结构向量 透射率 —/模拟 性能预测 回归 PINN — [64] 2021 硅基光学器件 通信 结构向量 透射率 1000/模拟 性能预测 回归 DNN, GA RMSE [65] 2022 光学纳米结构 通信 结构向量 Latent Dimension 8000/模拟 逆向设计 回归 DNN MSE [66] 2021 光栅轮廓重建 通信 结构向量 反射率 —/实验 逆向设计 回归 DNN — [67] 2018 等离子体纳米结构 通信 结构向量 透射率 1500/模拟 逆向设计 回归 DNN MSE [68] 2022 光子晶体光纤 通信 结构向量 (PCF)各项参数 2515/模拟 性能预测 回归 ANN MSE [69] 2022 光子晶体 通信 结构图像 Q参数, 纳米结构V 12750/实验 性能预测 回归 CNN MSE [70] 2020 光子晶体 通信 结构向量 频率 —/实验 性能优化 回归 DNN — [54] 2020 相位噪声滤波器 通信 [56] 2020 等离子体-声子耦合器 传感 [57] 2021 X射线瞬态光栅 传感 [71] 2021 光隔离器 通信 [72] 2021 基于石墨烯的
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No. Symbols Range Description 1 s1 1, 2, 3 Structural style of the first tubes 2 s2 0, 1, 2, 3 Structural style of the second tubes 3 N 5—10 Number of first/second tubes 4 Dcore 30 µm Core diameter 5 Ma_1c 20—40 µm Major axis of the first cladding tube 6 Mi_1c 20—40 µm Minor axis of the first cladding tube 7 Ma_2c 10—20 µm Major axis of the second cladding tube 8 Mi_2c 10—20 µm Minor axis of the second cladding tube 9 Ma_2n 0.3—0.8·Ma_2c Major axis of the second nested tube 10 Mi_2n 0.3—0.8·Ma_2n Minor axis of the second nested tube 11 Ma_1n 0.3—0.8·Ma_1c Major axis of the first nested tube 12 Mi_1n 0.3—0.8·Ma_1n Minor axis of the first nested tube 13 t_1c 0.3—0.7 µm Thickness of the first cladding tube 14 t_1n 0.3—0.7 µm Thickness of the first nested tube 15 t_2c 0.3—0.7 µm Thickness of the second cladding tube 16 t_2n 0.3—0.7 µm Thickness of the second nested tube DownLoad:
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混淆矩阵 真实值 Positive Negative 预
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值Positive True positive (TP) False positive (FP) Negative False negative (FN) True negative (TN) DownLoad:
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