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Optimization of an H0 photonic crystal nanocavity using machine learning

Abe Ryotaro Takeda Taichi Shiratori Ryo Shirakawa Shinichi 90633272 Saito Shota Baba Toshihiko 50202271 横浜国立大学

2020.01.15

概要

Using machine learning, we optimized an ultrasmall photonic crystal nanocavity to attain a high Q. Training data were collected via finite-difference time-domain simulation for models with randomly shifted holes, and a fully connected neural network (NN) was trained, resulting in a coefficient of determination between predicted and calculated values of 0.977. By repeating NN training and optimization of the Q value on the trained NN, the Q was roughly improved by a factor of 10–20 for various situations. Assuming a 180-nm-thick semiconductor slab at a wavelength approximately 1550 nm, we obtained Q = 1,011,400 in air; 283,200 in a solution, which was suitable for biosensing; and 44,600 with a nanoslot for high sensitivity. Important hole positions were also identified using the linear Lasso regression algorithm.

参考文献

1. Z. Zhang, and M. Qiu, “Small-volume waveguide-section high Q

microcavities in 2D photonic crystal slabs”, Opt. Express, vol. 12, no. 17,

pp. 3988-3995, 2004.

2. K. Nozaki, S. Kita, and T. Baba, “Room temperature continuous wave

operation and controlled spontaneous emission in ultrasmall photonic

crystal nanolaser”, Opt. Express, vol. 15, no. 12, pp. 7506-7514, 2007.

3. S. Kita, K. Nozaki, S. Hachuda, H. Watanabe, Y. Saito, S. Otsuka, T. Nakada,

Y. Arita, and T. Baba, “Photonic crystal point-shift nanolaser with and

without nanoslots --- design, fabrication, lasing and sensing

characteristics”, IEEE J. Sel. Top. Quantum Electron., vol. 17, no. 6, pp.

1632-1647, 2011.

4. T. Baba, “Photonic and iontronic sensing in GaInAsP semiconductor

photonic crystal nanolasers”, Photonics, vol. 6, no. 65, pp. 1-17, 2019.

5. K. Nozaki, T. Tanabe, A. Shinya, S. Matsuo, T. Sato, H. Taniyama, and M.

Notomi, “Sub-femtojoule all-optical switching using a photonic-crystal

nanocavity”, Nature Photonics, vol. 4, pp. 477-483, 2010.

6. M. Nomura, K. Tanabe, S. Iwamoto, and Y. Arakawa, “Design of a high-Q

H0 photonic crystal nanocavity for cavity QED”, Phys. Stat. Sol. C, vol. 8,

no. 2, pp. 340-342, 2011.

7. Y. Ota, D. Takamiya, R. Ohta, H. Takagi, N. Kumagai, S. Iwamoto, and Y.

Arakawa, “Large vacuum Rabi splitting between a single quantum dot and

an H0 photonic crystal nanocavity”, Appl. Phys. Lett. Vol. 112, no. 11, pp.

093101, 2018.

8. T. Asano, Y. Ohchi, Y. Takahashi, K. Kishimoto, and S. Noda, “Photonic

crystal nanocavity with a Q factor exceeding eleven million”, Opt. Express,

vol. 25, no. 3, pp. 1769-1777, 2017.

9. T. Hashimoto, T. Saida, I. Ogawa, M. Kohtoku, T. Shibata, and H. Takahashi,

“Optical circuit design based on a wavefront-matching method”, Opt.

Lett., vol. 30, no. 19, pp. 2620-2622, 2005.

10. A. Y. Piggott, J. Lu, K. G. Lagoudakis, J. Petykiewicz, T. M. Babinec, and J.

Vučković, “Inverse design and demonstration of a compact and

broadband on-chip wavelength demultiplexer”, Nature Photonics, vol. 9,

pp. 374-377, 2015.

11. Y. Shen, N. C. Harris, S. Skirlo, M. Prabhu, T. Baehr-Jones, M. Hochberg, X.

Sun, S. Zhao, H. Larochelle, D. Englund, and M. Soljačić, “Deep learning

with coherent nanophotonic circuits”, Nature Photon., vol. 11, pp. 441446, 2017.

12. X. Lin, Y. Rivenson, N. T. Yardimci, M. Veli, Y. Luo, M. Jarrahi, and A. Ozcan,

“All-optical machine learning using diffractive deep neural networks”,

Science, vol. 361, pp. 1004-1008, 2018.

13. J. Bueno, S. Maktoobi, L. Froehly, I. Fischer, M. Jacquot, L. Larger, and D.

Brunner, ”Reinforcement learning in a large-scale photonic recurrent

neural network”, Optica, vol. 5,no. 6, pp. 756-760, 2018.

14. T. F. de Lima, H. T. Peng, A. N. Tait, M. A. Nahmias, H. B. Miller, B. J.

Shastri, and P. R. Prucnal, “Machine Learning With Neuromorphic

Photonics”, J. Lightwave Technol., vol. 37, no. 5, pp. 1515-1534, 2019.

15. I. Malkiel, M. Mrejen, A. Nagler, U. Arieli, L. Wolf, and H. Suchowski,

“Plasmonic nanostructure design and characterization via Deep learning”,

Light Sci. Appl., vol. 7, no. 60, 2018.

16. J. Zhoou, B. Huang, Z. Yan, and J. G. Bünzli, “Emerging role of machine

learning I light-matter interaction”, Light Sci. Appl., vol. 8, no. 84, 2019.

17. Y. Kiarashinejad, S. Abdollahramezani, M. Zandehshahvar, O. Hemmatyar,

and A. Adibi, “Deep learning reveals underlying physics of light–matter

interactions in nanophotonic devices,” Adv. Theory Simulations vol. 2, pp.

1900088, 2019.

18. T. Asano, and S. Noda, “Optimization of photonic crystal nanocavities

based on deep learning”, Opt. Express, vol. 26, no. 25, pp. 32704-32716,

2018.

19. S. Kita, S. Hachuda, K. Nozaki, and T. Baba, “Nanoslot laser”, Appl. Phys.

Lett., vol. 97, no. 16, pp. 161108, 2010.

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