Inverse design of directional hybrid nanoantennas using neural networks

doi:10.1088/1674-1056/adeb5c

Abstract Controlling the directionality of quantum emitter (QE) radiation is crucial for advancing nanophotonic devices, yet designing compact, single nanoantennas for this purpose remains challenging with traditional methods due to computational demands and optimization complexity. This paper introduces a neural-network-based inverse design approach to efficiently optimize single core-shell nanosphere antennas and their derivatives — notched core-shell structures and dimers — for highly directional QE emission. We systematically compare the radial basis function (RBF), support vector regression (SVR), and backpropagation neural network (BPNN) algorithms. Our results demonstrate BPNN's superior performance in accurately mapping nanoantennas' geometric and material parameters to their far-field radiation characteristics. Subsequent BPNN-driven optimization confirms that all three investigated structures (single core-shell, notched core-shell, and dimer) can achieve robust directional emission. This is accomplished by precisely exciting higher-order electric and magnetic multipoles engineered to possess equal amplitudes and opposite phases, thereby facilitating directional radiation from QEs and enabling more efficient nanophotonic device design.

Keywords: artificial neural network Kerker condition nanosphere nanoantenna high-order plasmon modes

Received: 03 April 2025
Revised: 01 July 2025
Accepted manuscript online: 03 July 2025

PACS:	07.05.Mh	(Neural networks, fuzzy logic, artificial intelligence)
	42.25.-p	(Wave optics)
	73.20.Mf	(Collective excitations (including excitons, polarons, plasmons and other charge-density excitations))
	73.20.Mf	(Collective excitations (including excitons, polarons, plasmons and other charge-density excitations))

Fund: Project supported by the Key Research Projects of Colleges and Universities in Anhui Province (Grant No. KJ2021A0492), the Pre-research Project of the National Natural Science Foundation, AnHui Polytechnic University (Grant Nos. Xjky2022042 and Xjky2022048), the Testing Technology and Energy Saving Device Anhui Provincial Laboratory Open Fund (Grant No. JCKJ2022A09), the Joint Opening Project of Anhui Engineering Research Center of Vehicle Display Integrated Systems and Joint Discipline Key Laboratory of Touch Display Materials and Devices in Anhui Province (Grant No. VDIS2023B02), and the Science and Technology Project of Wuhu (Grant No. 2023jc05).

Corresponding Authors: Deng-Chao Huang
E-mail: huangdengchao@ahpu.edu.cn

Cite this article:

Ru-Lin Guan(管如林), Deng-Chao Huang(黄登朝), Ya-Qiong Li(李雅琼), Chen Wang(王晨), and Bin-Zi Xu(徐彬梓) Inverse design of directional hybrid nanoantennas using neural networks 2025 Chin. Phys. B 34 090702

[1] Zeng W, Jin Y, Zhou R, Li Y and Chen H 2024 Chem. Eng. J. 482 148994
[2] López-Fernández I, Valli D and Wang C Y 2024 Adv. Funct. Mater. 34 2307896
[3] Chen Y, Wang S and Zhang F 2023 Nat. Rev. Bioeng. 1 60
[4] Sun Y, Wu J and Li Y 2023 IEEE J. Sel. Top. Quantum Electron. 29 1
[5] Fang S and Hu Y H 2022 Chem. Soc. Rev. 51 3609
[6] Guidry M A, Lukin D M and Yang K Y 2022 Nat. Photonics 16 52
[7] Savelev R S, Sergaeva O N and Baranov D G 2017 Phys. Rev. B 95 235409
[8] Kar N, McCoy M and Wolfe J 2024 Nat. Synth. 3 175
[9] Zhou Y, Lu Y and Liu Y 2023 Biosens. Bioelectron. 228 115231
[10] Ammari H, Li B and Zou J 2023 Trans. Am. Math. Soc. 376 39
[11] Krasnok A E, Simovski C R and Belov P A 2014 Nanoscale 6 7354
[12] Jiang L, Fang B and Yan Z 2020 Microw. Opt. Technol. Lett. 62 2405
[13] Limonov M F, Rybin M V and Poddubny A N 2017 Nat. Photonics 11 543
[14] Hu D C, Wang J and Dong Q X 2023 ACS Appl. Nano Mater. 6 16856
[15] Wekalao J, Elsayed H A and El-Sherbeeny A M 2025 Plasmonics 1
[16] Wu Q, Li X and Jiang L 2021 Opt. Mater. Express 11 1907
[17] He J, He C, Zheng C and Wang Q 2019 Nanoscale 11 17444
[18] Wiecha P R and Muskens O L 2019 Nano Lett. 20 329
[19] Jiang Q, Zhu L, Shu C and Sekar V 2022 Neural Comput. Appl. 1
[20] Shi L, Zhang Q and Zhang S 2021 IEEE J. Multiscale Multiphys. Comput. Tech. 6 50
[21] Wang Z, Qin J and Hu Z 2022 Appl. Sci. 12 12543
[22] Yarotsky D 2017 Neural Netw. 94 103
[23] Vinatoru M, Mason T J and Calinescu I 2017 TrAC Trends Anal. Chem. 97 159
[24] Dubrovskii V G 2022 Nanomaterials 12 253
[25] Hodson T O 2022 Geosci. model Dev. Discuss. 1
[26] Namasudra S, Dhamodharavadhani S and Rathipriya R 2023 Neural Process. Lett. 1

[1]	Neural network analysis for prediction of heat transfer of aqueous hybrid nanofluid flow in a variable porous space with varying film thickness over a stretched surface Abeer S Alnahdi and Taza Gul. Chin. Phys. B, 2025, 34(2): 024701.
[2]	One memristor-one electrolyte-gated transistor-based high energy-efficient dropout neuronal units Yalin Li(李亚霖), Kailu Shi(时凯璐), Yixin Zhu(朱一新), Xiao Fang(方晓), Hangyuan Cui(崔航源), Qing Wan(万青), and Changjin Wan(万昌锦). Chin. Phys. B, 2024, 33(6): 068401.
[3]	Pulse coding off-chip learning algorithm for memristive artificial neural network Ming-Jian Guo(郭明健), Shu-Kai Duan(段书凯), and Li-Dan Wang(王丽丹). Chin. Phys. B, 2022, 31(7): 078702.
[4]	Parameter estimation of continuous variable quantum key distribution system via artificial neural networks Hao Luo(罗浩), Yi-Jun Wang(王一军), Wei Ye(叶炜), Hai Zhong(钟海), Yi-Yu Mao(毛宜钰), and Ying Guo(郭迎). Chin. Phys. B, 2022, 31(2): 020306.
[5]	Artificial neural network potential for gold clusters Ling-Zhi Cao(曹凌志), Peng-Ju Wang(王鹏举), Lin-Wei Sai(赛琳伟), Jie Fu(付洁), and Xiang-Mei Duan(段香梅). Chin. Phys. B, 2020, 29(11): 117304.
[6]	Pattern recognition and data mining software based on artificial neural networks applied to proton transfer in aqueous environments Amani Tahat, Jordi Marti, Ali Khwaldeh, Kaher Tahat. Chin. Phys. B, 2014, 23(4): 046101.
[7]	Unsupervised neural networks for solving Troesch’s problem Muhammad Asif Zahoor Raja. Chin. Phys. B, 2014, 23(1): 018903.
[8]	Novel model of a AlGaN/GaN high electron mobility transistor based on an artificial neural network Cheng Zhi-Qun(程知群), Hu Sha(胡莎), Liu Jun(刘军), and Zhang Qi-Jun . Chin. Phys. B, 2011, 20(3): 036106.
[9]	Prediction of the plasma distribution using an artificial neural network Li Wei(李炜), Chen Jun-Fang(陈俊芳), and Wang Teng(王腾). Chin. Phys. B, 2009, 18(6): 2441-2444.
[10]	HL-2A tokamak disruption forecasting based on an artificial neural network Wang Hao(王灏), Wang Ai-Ke(王爱科), Yang Qing-Wei(杨青巍), Ding Xuan-Tong(丁玄同), Dong Jia-Qi(董家齐), Sanuki H, and Itoh K. Chin. Phys. B, 2007, 16(12): 3738-3741.

No Suggested Reading articles found!

Viewed

Full text

Abstract

Cited

Metrics
Related Articles

Inverse design of directional hybrid nanoantennas using neural networks

Cite this article:

Online attention