1. Neviere, M. and E. Popov, Light Propagation in Periodic Media: Differential Theory and Design, CRC Press, 2002.

2. Mu, X., S. Wu, L. Cheng, and H. Fu, "Edge couplers in silicon photonic integrated circuits: A review," Applied Sciences, Vol. 10, No. 4, 1538, 2020.
doi:10.3390/app10041538

3. Marchetti, R., C. Lacava, A. Khokhar, X. Chen, I. Cristiani, D. J. Richardson, G. T. Reed, P. Petropoulos, and P. Minzioni, "High-efficiency grating-couplers: Demonstration of a new design strategy," Scientific Reports, Vol. 7, 16670, Nov. 2017.
doi:

4. Chrostowski, L. and M. Hochberg, Silicon Photonics Design: From Devices to Systems, Cambridge University Press, 2015.
doi:10.1017/CBO9781316084168

5. Cheng, L., S. Mao, Z. Li, Y. Han, and H. Y. Fu, "Grating couplers on silicon photonics: Design principles, emerging trends and practical issues," Micromachines, Vol. 11, No. 7, 666, 2020.
doi:10.3390/mi11070666

6. Djavid, M., S. A. Mirtaheri, and M. S. Abrishamian, "Photonic crystal notch-filter design using particle swarm optimization theory and finite-difference time-domain analysis," J. Opt. Soc. Am. B, Vol. 26, 849-853, Apr. 2009.
doi:10.1364/JOSAB.26.000849

7. Shen, L., Z. Ye, and S. He, "Design of two-dimensional photonic crystals with large absolute band gaps using a genetic algorithm," Phys. Rev. B, Vol. 68, 035109, Jul. 2003.

8. Assefa, S., F. Xia, and Y. A. Vlasov, "Reinventing germanium avalanche photodetector for nanophotonic on-chip optical interconnects," Nature, Vol. 464, 80-84, Mar. 2010.
doi:10.1038/nature08813

9. Peurifoy, J., Y. Shen, L. Jing, Y. Yang, F. Cano-Renteria, B. G. DeLacy, J. D. Joannopoulos, M. Tegmark, and M. Soljacic, "Nanophotonic particle simulation and inverse design using artificial neural networks," Science Advances, Vol. 4, No. 6, eaar4206, 2018.
doi:10.1126/sciadv.aar4206

10. Wu, Y.-D., "High efficiency multi-functional all-optical logic gates based on MIM plasmonic waveguide structure with the Kerr-type nonlinear nano-ring resonators," Progress In Electromagnetics Research, Vol. 170, 79-95, 2021.
doi:10.2528/PIER20082001

11. LeCun, Y., Y. Bengio, and G. Hinton, "Deep learning," Nature, Vol. 521, 436-444, May 2015.
doi:10.1038/nature14539

12. Zhang, Z., J. Geiger, J. Pohjalainen, A. E.-D. Mousa, W. Jin, and B. Schuller, "Deep learning for environmentally robust speech recognition: An overview of recent developments," ACM Trans. Intell. Syst. Technol., Vol. 9, Apr. 2018.

13. Pak, M. and S. Kim, "A review of deep learning in image recognition," 2017 4th International Conference on Computer Applications and Information Processing Technology (CAIPT), 1-3, 2017.

14. Otter, D. W., J. R. Medina, and J. K. Kalita, "A survey of the usages of deep learning for natural language processing," IEEE Transactions on Neural Networks and Learning Systems, Vol. 32, No. 2, 604-624, 2021.
doi:10.1109/TNNLS.2020.2979670

15. Ho-Phuoc, T., "CIFAR10 to compare visual recognition performance between deep neural networks and humans,", CoRR, arXiv:1811.07270, 2018.

16. Shan, T., M. Li, S. Xu, and F. Yang, "Phase synthesis of beam-scanning reflectarray antenna based on deep learning technique," Progress In Electromagnetics Research, Vol. 172, 41-49, 2021.
doi:10.2528/PIER21091307

17. An, S., B. Zheng, M. Y. Shalaginov, H. Tang, H. Li, L. Zhou, J. Ding, A. M. Agarwal, C. Rivero- Baleine, M. Kang, K. A. Richards, T. Gu, J. Hu, C. Fowler, and H. Zhang, "Deep learning modeling approach for metasurfaces with high degrees of freedom," Opt. Express, Vol. 28, 31932-31942, Oct. 2020.

18. So, S., J. Mun, and J. Rho, "Simultaneous inverse design of materials and structures via deep learning: Demonstration of dipole resonance engineering using core-shell nanoparticles," ACS Applied Materials & Interfaces, Vol. 11, 24264-24268, Jul. 2019.

19. Jiang, J. and J. A. Fan, "Multiobjective and categorical global optimization of photonic structures based on resnet generative neural networks," Nanophotonics, Vol. 10, No. 1, 361-369, 2021.
doi:10.1515/nanoph-2020-0407

20. Jiang, H., J. Chen, K. Tang, H. Yan, and R. Hao, "Exploring high-performance slow light grating waveguides by means of deep learning," IEEE Photonics Technology Letters, Vol. 34, No. 20, 1112-1115, 2022.
doi:10.1109/LPT.2022.3203475

21. Miyatake, Y., N. Sekine, K. Toprasertpong, S. Takagi, and M. Takenaka, "Computational design of efficient grating couplers using artificial intelligence," Japanese Journal of Applied Physics, Vol. 59, No. SG, SGGE09.1-SGGE09.8, 2019.

22. Tu, X., W. Xie, Z. Chen, M.-F. Ge, T. Huang, C. Song, and H. Y. Fu, "Analysis of deep neural network models for inverse design of silicon photonic grating coupler," Journal of Lightwave Technology, Vol. 39, No. 9, 2790-2799, 2021.
doi:10.1109/JLT.2021.3057473

23. Tahersima, M. H., K. Kojima, T. Koike-Akino, D. Jha, B. Wang, C. Lin, and K. Parsons, "Deep neural network inverse design of integrated photonic power splitters," Scientific Reports, Vol. 9, 1368, Feb. 2019.
doi:10.1038/s41598-018-37952-2

24. Taillaert, D., P. Bienstman, and R. Baets, "Compact efficient broadband grating coupler for silicon on-insulator waveguides," Opt. Lett., Vol. 29, 2749-2751, Dec. 2004.
doi:10.1364/OL.29.002749

25. Na, N., H. Frish, I.-W. Hsieh, O. Harel, R. George, A. Barkai, and H. Rong, "Efficient broadband silicon-on-insulator grating coupler with low backreflection," Opt. Lett., Vol. 36, 2101-2103, Jun. 2011.
doi:10.1364/OL.36.002101

26. Orobtchouk, R., A. Layadi, H. Gualous, D. Pascal, A. Koster, and S. Laval, "High-efficiency light coupling in a submicrometric silicon-on-insulator waveguide," Appl. Opt., Vol. 39, 5773-5777, Nov. 2000.

27. Michaels, A. and E. Yablonovitch, "Inverse design of near unity efficiency perfectly vertical grating couplers," Opt. Express, Vol. 26, 4766-4779, Feb. 2018.
doi:10.1364/OE.26.004766

28. Chen, X., D. J. Thomson, L. Crudginton, A. Z. Khokhar, and G. T. Reed, "Dual-etch apodised grating couplers for efficient fibre-chip coupling near 1310 nm wavelength," Opt. Express, Vol. 25, 17864-17871, Jul. 2017.

29. Sanchez-Postigo, A., J. G. Wanguemert-Perez, J. M. Luque-Gonzalez, I. Molina-Fernandez, P. Cheben, C. A. Alonso-Ramos, R. Halir, J. H. Schmid, and A. Ortega-Monux, "Broadband fiber-chip zero-order surface grating coupler with 0.4 dB efficiency," Opt. Lett., Vol. 41, 3013-3016, Jul. 2016.

30. Su, L., R. Trivedi, N. V. Sapra, A. Y. Piggott, D. Vercruysse, and J. Vuckovie, "Fully-automated optimization of grating couplers," Opt. Express, Vol. 26, 4023-4034, Feb. 2018.
doi:10.1364/OE.26.004023

31. Rahim, A., T. Spuesens, R. Baets, and W. Bogaerts, "Open-access silicon photonics: Current status and emerging initiatives," Proceedings of the IEEE, Vol. 106, No. 12, 2313-2330, 2018.
doi:10.1109/JPROC.2018.2878686

32. Roelkens, G., D. V. Thourhout, and R. Baets, "High efficiency silicon-on-insulator grating coupler based on a poly-silicon overlay," Opt. Express, Vol. 14, 11622-11630, Nov. 2006.

33. Taillaert, D., W. Bogaerts, P. Bienstman, T. Krauss, P. Van Daele, I. Moerman, S. Verstuyft, K. De Mesel, and R. Baets, "An out-of-plane grating coupler for efficient butt-coupling between compact planar waveguides and single-mode fibers," IEEE Journal of Quantum Electronics, Vol. 38, No. 7, 949-955, 2002.
doi:10.1109/JQE.2002.1017613

34. Taflove, A., S. C. Hagness, and M. Piket-May, "Computational electromagnetics: The finite difference time-domain method," The Electrical Engineering Handbook, Vol. 3, No. 15, 629-670, 2005.
doi:10.1016/B978-012170960-0/50046-3

35. Berenger, J.-P., Perfectly Matched Layer (PML) for Computational Electromagnetics, Springer Nature, 2022.

36. Gedney, S. D. and B. Zhao, "An auxiliary differential equation formulation for the complex frequency shifted PML," IEEE Transactions on Antennas and Propagation, Vol. 58, No. 3, 838-847, 2010.
doi:10.1109/TAP.2009.2037765

37. He, K., X. Zhang, S. Ren, and J. Sun, "Deep residual learning for image recognition," Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Jun. 2016.

38. He, K., X. Zhang, S. Ren, and J. Sun, "Identity mappings in deep residual networks," Computer Vision --- ECCV 2016, 630-645, B. Leibe, J. Matas, N. Sebe, and M. Welling, eds., Springer International Publishing, Cham, 2016.

39. Glorot, X. and Y. Bengio, "Understanding the difficulty of training deep feedforward neural networks," Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, 249-256, Y. W. Teh and M. Titterington, eds., Vol. 9 of Proceedings of Machine Learning Research, PMLR, Chia Laguna Resort, Sardinia, Italy, May 13-15, 2010.

40. Clevert, D.-A., T. Unterthiner, and S. Hochreiter, "Fast and accurate deep network learning by exponential linear units (ELUS)," arXiv:1511.07289, 2015.

41. Klambauer, G., T. Unterthiner, A. Mayr, and S. Hochreiter, "Self-normalizing neural networks," Advances in Neural Information Processing Systems, Vol. 30, I. Guyon, U. V. Luxburg, S. Bengio, H. Wallach, R. Fergus, S. Vishwanathan, and R. Garnett, eds., Curran Associates, Inc., 2017.

42. Elfwing, S., E. Uchibe, and K. Doya, "Sigmoid-weighted linear units for neural network function approximation in reinforcement learning," Neural Networks, Vol. 107, 3-11, Special issue on deep reinforcement learning, 2018.

43. Dozat, T., "Incorporating nesterov momentum into adam," ICLR 2016, 2016.

44. Chicco, D., M. J. Warrens, and G. Jurman, "The coefficient of determination R-squared is more informative than SMAPE, MAE, MAPE, MSE and RMSE in regression analysis evaluation," PeerJ Computer Science, Vol. 7, e623, 2021.
doi:10.7717/peerj-cs.623

45. Smith, L. N., "A disciplined approach to neural network hyper-parameters: Part 1 --- Learning rate, batch size, momentum, and weight decay,", CoRR, abs/1803.09820, 2018.

46. Ioffe, S. and C. Szegedy, "Batch normalization: Accelerating deep network training by reducing internal covariate shift," Proceedings of the 32nd International Conference on Machine Learning, 448-456, F. Bach and D. Blei, eds., Vol. 37 of Proceedings of Machine Learning Research, PMLR, Lille, France, Jul. 7-9, 2015.

47. Goyal, P., P. Dollar, R. B. Girshick, P. Noordhuis, L. Wesolowski, A. Kyrola, A. Tulloch, Y. Jia, and K. He, "Accurate, large minibatch SGD: Training imagenet in 1 hour,", CoRR, abs/1706.02677, 2017.

48. Masters, D. and C. Luschi, "Revisiting small batch training for deep neural networks,", CoRR, abs/1804.07612, 2018.

49. Smith, S. L., P. Kindermans, and Q. V. Le, "Don't decay the learning rate, increase the batch size,", CoRR, abs/1711.00489, 2017.

50. Zhang, Z., Y. Jiang, S. Zhang, S. Geng, H. Wang, and G. Sang, "An adaptive particle swarm optimization algorithm for reservoir operation optimization," Applied Soft Computing, Vol. 18, 167-177, 2014.
doi:10.1016/j.asoc.2014.01.034

51. Wang, J., C. Gui, C. Li, Q. Yang, D. Gao, J. Sun, and X. Zhang, "On-chip demultiplexing of polarization and wavelength multiplexed of DM/OQAM 64/128-QAM signals using silicon 2D grating coupler and microring resonators," Optical Fiber Communication Conference, Th2A.48, Optica Publishing Group, 2014.

52. Li, H.-Y., W.-C. Hsu, K.-C. Liu, Y.-L. Chen, L.-K. Chau, S. Hsieh, and W.-H. Hsieh, "A low cost, label-free biosensor based on a novel double-sided grating waveguide coupler with sub-surface cavities," Sensors and Actuators B: Chemical, Vol. 206, 371-380, 2015.
doi:10.1016/j.snb.2014.09.065

53. Xu, L., X. Chen, C. Li, and H. K. Tsang, "Bi-wavelength two dimensional chirped grating couplers for low cost WDM pon transceivers," Optics Communications, Vol. 284, No. 8, 2242-2244, 2011.
doi:10.1016/j.optcom.2010.12.072

54. Zhou, X. and H. K. Tsang, "Photolithography fabricated sub-decibel high-efficiency silicon waveguide grating coupler," IEEE Photonics Technology Letters, Vol. 35, No. 1, 43-46, 2023.
doi:10.1109/LPT.2022.3221527

55. Duan, L., C. Xu, S. Zhong, H. Zhou, and J. Duan, "Optical power auto-alignment method with eugenics sorting for enhancing the alignment speed and robustness of fiber-grating couplers," Opt. Express, Vol. 30, 39544-39560, Oct. 2022.