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“机器微纳光学科学家”: 人工智能在微纳光学设计的应用与发展

侯晨阳 孟凡超 赵一鸣 丁进敏 赵小艇 刘鸿维 王鑫 娄淑琴 盛新志 梁生

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“机器微纳光学科学家”: 人工智能在微纳光学设计的应用与发展

侯晨阳, 孟凡超, 赵一鸣, 丁进敏, 赵小艇, 刘鸿维, 王鑫, 娄淑琴, 盛新志, 梁生

“Machine micro/nano optics scientist”: Application and development of artificial intelligence in micro/nano optical design

Hou Chen-Yang, Meng Fan-Chao, Zhao Yi-Ming, Ding Jin-Min, Zhao Xiao-Ting, Liu Hong-Wei, Wang Xin, Lou Shu-Qin, Sheng Xin-Zhi, Liang Sheng
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  • 微纳光学材料与器件是光通信、光传感、生物光子学、激光、量子光学等诸多光学领域的关键. 目前微纳光学设计主要依赖传统数值方法, 存在依赖计算资源、创新效率低、得到全局最优设计困难的难题, 是当前微纳光学设计的瓶颈. 人工智能(artificial intelligence, AI)目前已经在多个学科开展应用, 带来了科学研究的新范式. 本文从微纳光学设计对象、数据集构建、学习任务与算法以及性能度量四个方面对AI在微纳光学设计领域的应用进行综述. 对AI在微纳光学研究中的难点及未来的发展趋势进行了分析与展望.
    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.
      通信作者: 梁生, shliang@bjtu.edu.cn
    • 基金项目: 国家自然科学基金(批准号: 12174022, 62005020, 62101027)资助的课题.
      Corresponding author: Liang Sheng, shliang@bjtu.edu.cn
    • Funds: Project supported by the National Natural Science Foundation of China (Grant Nos. 12174022, 62005020, 62101027).
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  • 图 1  基于AI的微纳光学设计年出版文献统计

    Fig. 1.  Statistics on the number of published papers on AI based micro/nano optical design.

    图 2  AI微纳光学设计概述

    Fig. 2.  Overview of AI based micro/nano optical design.

    图 3  数据集构建过程

    Fig. 3.  Dataset construction process.

    图 4  基于结构向量的空芯反谐振光纤样本[48]

    Fig. 4.  Structures of the hollow-core anti-resonant fiber[48].

    图 5  波长解复用器 (a) 结构图; (b) 逆向设计过程

    Fig. 5.  Wavelength demultiplexer: (a) Structure diagram; (b) reverse design process.

    图 6  集成硅基光学功率分配器结构

    Fig. 6.  Integrated silicon-based optical power divider structure.

    图 7  用于FT光纤设计的ANN模型

    Fig. 7.  ANN model for FT fiber design.

    图 8  桥式光纤结构

    Fig. 8.  Bridge fiber structure.

    图 9  将纳米结构进行编码

    Fig. 9.  Encoding the nanostructures.

    图 10  PCF结构

    Fig. 10.  PCF structure.

    图 11  用于OAM光纤设计的AI模型

    Fig. 11.  AI model for OAM fiber design.

    图 12  基于KNN、决策树算法的 (a) ROC图和(b)预测精度值[48]

    Fig. 12.  (a) ROC plot and (b) prediction accuracy values based on KNN, decision tree algorithm[48].

    图 13  AI微纳光学设计发展趋势

    Fig. 13.  AI micro/nano optical design trends.

    图 14  “最近与均值向量距离”度量方法示意图

    Fig. 14.  Schematic diagram of the “nearest-to-mean vector distance” metric.

    表 1  现有微纳光学设计研究工作

    Table 1.  Current research on micro/nano optical design.

    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DNNMSE
    [19]2021偏振分束器通信结构图像透射率—/实验性能预测回归CNNMSE
    [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BoNetMSE
    [47]20203 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 methodMSE
    [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 treeAccuracy
    [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, ANNMSE
    [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, GAAccuracy
    [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DNNMSE
    [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, GAMSE
    [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ANNMSE
    [55]2022超表面电磁波结构图像反射率、ExEy相位1000/实验逆向设计回归CNNMSE
    [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, NN
    RMSE, 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ANNMSE
    [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、MOPSOMSE
    [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DNNL2 loss
    [62]2022微纳硅基器件加工结构图像distance50680/实验性能预测回归CNNBCE
    [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, GARMSE
    [65]2022光学纳米结构通信结构向量Latent Dimension8000/模拟逆向设计回归DNNMSE
    [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DNNMSE
    [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ANNMSE
    [69]2022光子晶体通信结构图像Q参数, 纳米结构V12750/实验性能预测回归CNNMSE
    [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]2021X射线瞬态光栅传感
    [71]2021光隔离器通信
    [72]2021基于石墨烯的
    多光谱电光表面
    材料
    下载: 导出CSV

    表 2  定义HC-ARF样本的结构参数向量

    Table 2.  Structure-parameter vector for defining HC-ARF samples.

    No.SymbolsRangeDescription
    1s11, 2, 3Structural style of the first tubes
    2s20, 1, 2, 3Structural style of the second tubes
    3N5—10Number of first/second tubes
    4Dcore30 µmCore diameter
    5Ma_1c20—40 µmMajor axis of the first cladding tube
    6Mi_1c20—40 µmMinor axis of the first cladding tube
    7Ma_2c10—20 µmMajor axis of the second cladding tube
    8Mi_2c10—20 µmMinor axis of the second cladding tube
    9Ma_2n0.3—0.8·Ma_2cMajor axis of the second nested tube
    10Mi_2n0.3—0.8·Ma_2nMinor axis of the second nested tube
    11Ma_1n0.3—0.8·Ma_1cMajor axis of the first nested tube
    12Mi_1n0.3—0.8·Ma_1nMinor axis of the first nested tube
    13t_1c0.3—0.7 µmThickness of the first cladding tube
    14t_1n0.3—0.7 µmThickness of the first nested tube
    15t_2c0.3—0.7 µmThickness of the second cladding tube
    16t_2n0.3—0.7 µmThickness of the second nested tube
    下载: 导出CSV

    表 3  混淆矩阵

    Table 3.  Confusion matrix.

    混淆矩阵真实值
    PositiveNegative


    PositiveTrue positive (TP)False positive (FP)
    NegativeFalse negative (FN)True negative (TN)
    下载: 导出CSV
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  • 收稿日期:  2023-02-15
  • 修回日期:  2023-04-03
  • 上网日期:  2023-04-12
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