A data-efficient self-supervised deep learning model for design and characterization of nanophotonic structures
Tóm tắt
With its tremendous success in many machine learning and pattern recognition tasks, deep learning, as one type of data-driven models, has also led to many breakthroughs in other disciplines including physics, chemistry and material science. Nevertheless, the supremacy of deep learning over conventional optimization approaches heavily depends on the huge amount of data collected in advance to train the model, which is a common bottleneck of such a data-driven technique. In this work, we present a comprehensive deep learning model for the design and characterization of nanophotonic structures, where a self-supervised learning mechanism is introduced to alleviate the burden of data acquisition. Taking reflective metasurfaces as an example, we demonstrate that the self-supervised deep learning model can effectively utilize randomly generated unlabeled data during training, with the total test loss and prediction accuracy improved by about 15% compared with the fully supervised counterpart. The proposed self-supervised learning scheme provides an efficient solution for deep learning models in some physics-related tasks where labeled data are limited or expensive to collect.
Tài liệu tham khảo
N. Yu, P. Genevet, M. A. Kats, F. Aieta, J. P. Tetienne, F. Capasso, and Z. Gaburro, Science 334, 333 (2011).
C. M. Watts, X. Liu, and W. J. Padilla, Adv. Mater. 24, OP98 (2012).
X. Zhang, and Z. Liu, Nat. Mater. 7, 435 (2008).
J. A. Schuller, E. S. Barnard, W. Cai, Y. C. Jun, J. S. White, and M. L. Brongersma, Nat. Mater. 9, 193 (2010).
W. X. Tang, Z. L. Mei, and T. J. Cui, Sci. China-Phys. Mech. Astron. 58, 127001 (2015).
X. Luo, ACS Photon. 5, 4724 (2018).
S. Molesky, Z. Lin, A. Y. Piggott, W. Jin, J. Vucković, and A. W. Rodriguez, Nat. Photon. 12, 659 (2018).
Y. Lecun, Y. Bengio, and G. Hinton, Nature 521, 436 (2015).
B. Sanchez-Lengeling, and A. Aspuru-Guzik, Science 361, 360 (2018).
G. B. Goh, N. O. Hodas, and A. Vishnu, J. Comput. Chem. 38, 1291 (2017).
T. Zahavy, A. Dikopoltsev, D. Moss, G. I. Haham, O. Cohen, S. Mannor, and M. Segev, Optica 5, 666 (2018).
P. Baldi, P. Sadowski, and D. Whiteson, Nat. Commun. 5, 4308 (2014).
J. Carrasquilla, and R. G. Melko, Nat. Phys. 13, 431 (2017).
L. Pilozzi, F. A. Farrelly, G. Marcucci, and C. Conti, Commun. Phys. 1, 1 (2018).
D. Melati, Y. Grinberg, M. Kamandar Dezfouli, S. Janz, P. Cheben, J. H. Schmid, A. Sánchez-Postigo, and D. X. Xu, Nat. Commun. 10, 1 (2019).
M. H. Tahersima, K. Kojima, T. Koike-Akino, D. Jha, B. Wang, C. Lin, and K. Parsons, Sci. Rep. 9, 1 (2019).
O. Hemmatyar, S. Abdollahramezani, Y. Kiarashinejad, M. Zandeh-shahvar, and A. Adibi, Nanoscale 11, 21266 (2019).
I. Sajedian, T. Badloe, and J. Rho, Opt. Express 27, 5874 (2019).
J. Jiang, D. Sell, S. Hoyer, J. Hickey, J. Yang, and J. A. Fan, ACS Nano 13, 8872 (2019).
Z. Liu, D. Zhu, K.-T. Lee, A. S. Kim, L. Raju, and W. Cai, Adv. Mater. 32, 1904790 (2020).
W. Ma, F. Cheng, and Y. Liu, ACS Nano 12, 6326 (2018).
Y. Chen, J. Zhu, Y. Xie, N. Feng, and Q. H. Liu, Nanoscale 11, 9749 (2019).
W. Ma, F. Cheng, Y. Xu, Q. Wen, and Y. Liu, Adv. Mater. 31, 1901111 (2019).
J. Peurifoy, Y. Shen, L. Jing, Y. Yang, F. Cano-Renteria, B. G. De-Lacy, J. D. Joannopoulos, M. Tegmark, and M. Soljačić, Sci. Adv. 4, eaar4206 (2018).
S. So, J. Mun, and J. Rho, ACS Appl. Mater. Interfaces 11, 24264 (2019).
I. Sajedian, J. Kim, and J. Rho, Microsyst. Nanoeng. 5, 27 (2019).
Z. Liu, D. Zhu, S. P. Rodrigues, K. T. Lee, and W. Cai, Nano Lett. 18, 6570 (2018).
I. Malkiel, M. Mrejen, A. Nagler, U. Arieli, L. Wolf, and H. Su-chowski, Light Sci Appl 7, 1 (2018).
T. Zhang, J. Wang, Q. Liu, J. Zhou, J. Dai, X. Han, Y. Zhou, and K. Xu, Photon. Res. 7, 368 (2019).
T. Asano, and S. Noda, Opt. Express 26, 32704 (2018).
D. P. Kingma, S. Mohamed, D. J. Rezende, and M. Welling, in 37th Advances in Neural Information Processing Systems, Montreal, 9–13 December 2014. pp. 3581–3589.
M. Raissi, P. Perdikaris, and G. E. Karniadakis, J. Comput. Phys. 378, 686 (2019).
Y. Qu, L. Jing, Y. Shen, M. Qiu, and M. Soljačić, ACS Photon. 6, 1168 (2019).
A. V. Kildishev, A. Boltasseva, and V. M. Shalaev, Science 339, 1232009 (2013).
N. Yu, and F. Capasso, Nat. Mater. 13, 139 (2014).
F. Aieta, P. Genevet, M. A. Kats, N. Yu, R. Blanchard, Z. Gaburro, and F. Capasso, Nano Lett. 12, 4932 (2012).
W. Ma, Z. Huang, X. Bai, P. Zhan, and Y. Liu, ACS Photonics 4, 1770 (2017).
S. Wang, P. C. Wu, V. C. Su, Y. C. Lai, M. K. Chen, H. Y. Kuo, B. H. Chen, Y. H. Chen, T. T. Huang, J. H. Wang, R. M. Lin, C. H. Kuan, T. Li, Z. Wang, S. Zhu, and D. P. Tsai, Nat. Nanotech. 13, 227 (2018).
W. T. Chen, A. Y. Zhu, V. Sanjeev, M. Khorasaninejad, Z. Shi, E. Lee, and F. Capasso, Nat. Nanotech. 13, 220 (2018).
F. Aieta, P. Genevet, N. Yu, M. A. Kats, Z. Gaburro, and F. Capasso, Nano Lett. 12, 1702 (2012).
A. Pors, and S. I. Bozhevolnyi, Opt. Express 21, 2942 (2013).
C. Pfeiffer, N. K. Emani, A. M. Shaltout, A. Boltasseva, V. M. Sha-laev, and A. Grbic, Nano Lett. 14, 2491 (2014).
J. Neu, R. Beigang, and M. Rahm, Appl. Phys. Lett. 103, 041109 (2013).
X. Ni, A. V. Kildishev, and V. M. Shalaev, Nat. Commun. 4, 2807 (2013).
G. Zheng, H. Mühlenbernd, M. Kenney, G. Li, T. Zentgraf, and S. Zhang, Nat. Nanotech. 10, 308 (2015).
S. Sun, Q. He, S. Xiao, Q. Xu, X. Li, and L. Zhou, Nat. Mater. 11, 426 (2012).
Y. Liu, and X. Zhang, Appl. Phys. Lett. 103, 141101 (2013).
A. A. High, R. C. Devlin, A. Dibos, M. Polking, D. S. Wild, J. Perczel, N. P. de Leon, M. D. Lukin, and H. Park, Nature 522, 192 (2015).
Z. Wang, K. Yao, M. Chen, H. Chen, and Y. Liu, Phys. Rev. Lett. 117, 157401 (2016).
Z. Su, F. Cheng, L. Li, and Y. Liu, ACS Photon. 6, 1947 (2019).
Y. Pu, Z. Gan, R. Henao, X. Yuan, C. Li, A. Stevens, and L. Carin, in 30th Advances in Neural Information Processing Systems, Barcelona, 2016. pp. 2352–2360.
D. P. Kingma and M. Welling, in Proceedings of the 2nd International Conference on Learning Representations (ICLR), Banff, 2014. pp. 14–16.
A. Kolesnikov, X. Zhai, and L. Beyer, in Proceedings of the 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, 15–20 June 2019. pp. 1920–1929.