“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

doi:10.7498/aps.72.20230208

Abstract

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.

Keywords:

Authors and contacts

1.
School of Physical Science and Engineering, Beijing Jiaotong University, Beijing 100044, China
2.
Jeme Tienyow Honors College, Beijing Jiaotong University, Beijing 100044, China
3.
School of Electronic Information Engineering, Beijing Jiaotong University, Beijing 100044, China

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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References

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Cited By

图 1 基于AI的微纳光学设计年出版文献统计

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

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图 2 AI微纳光学设计概述

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

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图 3 数据集构建过程

Figure 3. Dataset construction process.

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图 4 基于结构向量的空芯反谐振光纤样本^[48]

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

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图 5 波长解复用器　(a) 结构图; (b) 逆向设计过程

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

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图 6 集成硅基光学功率分配器结构

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

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图 7 用于FT光纤设计的ANN模型

Figure 7. ANN model for FT fiber design.

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图 8 桥式光纤结构

Figure 8. Bridge fiber structure.

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图 9 将纳米结构进行编码

Figure 9. Encoding the nanostructures.

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图 10 PCF结构

Figure 10. PCF structure.

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图 11 用于OAM光纤设计的AI模型

Figure 11. AI model for OAM fiber design.

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

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

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图 13 AI微纳光学设计发展趋势

Figure 13. AI micro/nano optical design trends.

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图 14 “最近与均值向量距离”度量方法示意图

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

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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	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, 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	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	基于石墨烯的多光谱电光表面	材料

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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.	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	D_core	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

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表 3 混淆矩阵

Table 3. Confusion matrix.

混淆矩阵		真实值
混淆矩阵		Positive	Negative
预测值	Positive	True positive (TP)	False positive (FP)
预测值	Negative	False negative (FN)	True negative (TN)

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