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光子晶体因其独特的能带结构在光子学领域具有重要应用前景，而准确预测其能带结构对于光子器件的设计与优化也至关重要。鉴于此，本研究应用Vision Transformer（ViT）模型，探索高效、准确的光子晶体能带结构预测方法。首先，通过传统数值仿真方法得到光子晶体的能带结构数据，构建了训练和测试数据集；其次，利用数据集对ViT模型进行训练，训练过程中模型展现出良好的学习能力，损失函数值持续下降最低可至4.42×10-6；最终，测试结果表明，ViT模型预测平均均方误差（MSE）低至3.46×10-5，决定系数（R2）达到0.9996，表明ViT模型具有极高的预测精度和良好的泛化能力。研究表明，ViT模型能够有效预测光子晶体的能带结构，为光子晶体相关研究和应用提供了一种新的高效预测工具，有望推动光子器件设计的进一步发展。
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关键词:
- 光子晶体 /
- 能带结构 /
- 预测 /
- Vision Transformer
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
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[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
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