-
Photonic crystals have garnered significant attention in the field of photonics due to their unique band structures, which enable the manipulation of light propagation through periodic dielectric arrangements. Accurate prediction of these band structures is crucial for the design and optimization of photonic devices. However, traditional numerical simulation methods, such as plane wave expansion and finite element methods, are often limited by high computational complexity and long processing times. In this study, we explore the application of the Vision Transformer (ViT) model to predict the band structures of photonic crystals efficiently and accurately. To further validate the superiority of the ViT model, we also conducted experiments using CNN and MLP models of the same scale for band structure prediction. We first generate a dataset of photonic band structures using traditional numerical simulations and then train the ViT model on this dataset. The ViT model demonstrates excellent learning capabilities, with the loss function value decreasing to as low as 4.42×10-6 during training. The test results show that the average mean squared error (MSE) of the ViT model predictions is 3.46×10-5, and the coefficient of determination (R2) reaches 0.9996, indicating high prediction accuracy and good generalization ability. In contrast, the CNN and MLP models, despite being trained on the same dataset and having the same computational resource allocation, exhibited higher MSE values and lower R2 scores. This highlights the superior performance of the ViT model in predicting the band structures of photonic crystals. Our study shows that the ViT model can effectively predict the band structures of photonic crystals, providing a new and efficient prediction tool for related research and applications. This work is expected to advance the development of photonic device design by offering a rapid and accurate alternative to traditional methods.
-
Keywords:
- photonic crystal /
- band structure /
- prediction /
- Vision Transformer
-
[1] Heaton J, Goodfellow I, Bengio Y, Courville A 2018 Genet. Program. Evolution Mach. 19305
[2] LeCun Y, Bengio Y, Hinton G 2015 Nature 521436
[3] Yablonovitch E 1995 Phys. Rev. Lett. 582059
[4] John S 1987 Phys. Rev. Lett. 582486
[5] Joannopoulos J D, Meade R D, Winn J N 2008 Photonic Crystals: Molding the Flow of Light (Princeton NJ: Princeton Univ. Press)
[6] Nyachionjeka K, Tarus H, Langat K 2020 Sci. Afr. 9 e00511
[7] Bogaerts W, Pérez D, Capmany J, Miller D A B, Poon J, Englund D, Morichetti F, Melloni A 2020 Nature 586207
[8] Fallahi V, Kordrostami Z, Hosseini M 2024 Sci. Rep. 142001
[9] Fu Y L, Hu X Y, Gong Q H 2013 Phys. Lett. A 377329
[10] Safinezhad A, Babaei Ghoushji H, Shiri M, Rezaei M H 2021 Opt. Quant. Electron. 53259
[11] Giden I H, Mahariq I 2024 Opt. Quant. Electron. 56170
[12] Tavares S C da C, Sousa F B de, Oliveira L A de, Sousa F M de, Miranda I R S, Costa M B C 2024 Opt. Quant. Electron. 56622
[13] Sathyadevaki R, Raja A S, Sundar D S 2017 Photon. Netw. Commun. 3377
[14] Bazian M 2021 Photon. Netw. Commun. 4157
[15] Liu Y, Zhao T, Ju W, Shi S 2017 J. Materiomics 3159
[16] Ma W, Liu Z, Kudyshev Z A, Boltasseva A, Cai W, Liu Y 2021 Nat. Photon. 1577
[17] Christensen T, Loh C, Picek S, Jakobović D, Jing L, Fisher S, Ceperic V, Joannopoulos J D, Soljačić M 2020 Nanophotonics 94183
[18] Ferreira A da S, Silveira G N M, Figueroa H E H 2018 SBFoton International Optics and Photonics Conference Campinas, Brazil October 08-10, 2019 p1
[19] He K, Zhang X, Ren S, Sun J 2016 IEEE Conference on Computer Vision and Pattern Recognition Las Vegas, NV, USA, June 27-30, 2016 p770
[20] Girshick R 2015 IEEE International Conference on Computer Vision Santiago, Chile, December 07-13, 2015 p1440
[21] Shen D, Wu G, Suk H 2017 Annu. Rev. Biomed. Eng. 19221
[22] Vinyals O, Toshev A, Bengio S, Erhan D 2017 IEEE Trans. Pattern Anal. Mach. Intell. 39652
[23] Dosovitskiy A, Beyer L, Kolesnikov A, Weissenborn D, Zhai X, Unterthiner T, Dehghani M, Minderer M, Heigold G, Gelly S, Uszkoreit J, Houlsby N 2020 arXiv:2010.11929[cs.CV]
[24] Lecun Y, Bottou L, Bengio Y, Haffner P 1998 Proc. IEEE 862278
[25] Li Y, Yin G, Yan G, Yao S 2025 Mech. Syst. Sig. Process. 224111975
[26] Michelucci U, Venturini F 2021 Mach. Learn. Knowl. Extr. 3357
Metrics
- Abstract views: 182
- PDF Downloads: 0
- Cited By: 0
下载:
