Machine learning assisted plasmonic metascreen for enhanced broadband absorption in ultra-thin silicon films

Waqas W. Ahmed; Haicheng Cao; Changqing Xu; Mohamed Farhat; Muhammad Amin; Xiaohang Li; Xiangliang Zhang; Ying Wu

doi:10.1038/s41377-024-01723-8

Go to Nature Website

Your Location：

Home >

Browse articles >

Machine learning assisted plasmonic metascreen for enhanced broadband absorption in ultra-thin silicon films

Original Articles

- Machine learning assisted plasmonic metascreen for enhanced broadband absorption in ultra-thin silicon films
- Machine learning assisted plasmonic metascreen for enhanced broadband absorption in ultra-thin silicon films
- LSA 2025年14卷第2期页码：411-421
- Affiliations：
  
  1.Division of Computer, Electrical and Mathematical Sciences and Engineering, King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Saudi Arabia
  2.College of Engineering, Taibah University, Madinah 42353, Saudi Arabia
  3.Division of Physical Science and Engineering, King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Saudi Arabia
  4.Department of Computer Science and Engineering, University of Notre Dame, Notre Dame, IN 46556, USA
- Author bio：
  
  Xiangliang Zhang (xzhang33@nd.edu)
  Ying Wu (ying.wu@kaust.edu.sa)
- Funds：
- DOI：10.1038/s41377-024-01723-8
  中图分类号：
- 收稿日期：2024-03-20，
  
  修回日期：2024-11-04，
  
  录用日期：2024-12-16，
  
  网络出版日期：2025-01-09，
  
  纸质出版日期：2025-02-28
- Accepted：
Scan QR Code
Machine learning assisted plasmonic metascreen for enhanced broadband absorption in ultra-thin silicon films[J]. LSA, 2025,14(2):411-421.

Ahmed, W. W. et al. Machine learning assisted plasmonic metascreen for enhanced broadband absorption in ultra-thin silicon films. Light: Science & Applications, 14, 411-421 (2025).
Machine learning assisted plasmonic metascreen for enhanced broadband absorption in ultra-thin silicon films[J]. LSA, 2025,14(2):411-421. DOI： 10.1038/s41377-024-01723-8.

Ahmed, W. W. et al. Machine learning assisted plasmonic metascreen for enhanced broadband absorption in ultra-thin silicon films. Light: Science & Applications, 14, 411-421 (2025). DOI： 10.1038/s41377-024-01723-8.

摘要

Abstract

We propose and demonstrate a data-driven plasmonic metascreen that efficiently absorbs incident light over a wide spectral range in an ultra-thin silicon film. By embedding a double-nanoring silver array within a 20 nm ultrathin amorphous silicon (a-Si) layer

we achieve a significant enhancement of light absorption. This enhancement arises from the interaction between the resonant cavity modes and localized plasmonic modes

requiring precise tuning of plasmon resonances to match the absorption region of the silicon active layer. To facilitate the device design and improve light absorption without increasing the thickness of the active layer

we develop a deep learning framework

which learns to map from the absorption spectra to the design space. This inverse design strategy helps to tune the absorption for selective spectral functionalities. Our optimized design surpasses the bare silicon planar device

exhibiting a remarkable enhancement of over 100%. Experimental validation confirms the broadband enhancement of light absorption in the proposed configuration. The proposed metascreen absorber holds great potential for light harvesting applications and may be leveraged to improve the light conversion efficiency of ultra-thin silicon solar cells

photodetectors

and optical filters.

关键词

Keywords

references

Watts, C. M., Liu, X. L. & Padilla, W. J. Metamaterial electromagnetic wave absorbers. Adv. Mater. 24 , OP98–OP120 (2012)..

Yu, R. et al. Nanomaterials and nanostructures for efficient light absorption and photovoltaics. Nano Energy 1 , 57–72 (2012)..

Yu, H. K. et al. Plasmon-enhanced light–matter interactions and applications. npj Comput. Mater. 5 , 45 (2019)..

Möller, H. J. Semiconductors for Solar Cells (Artech House Publishers, Boston, 1993).

Teperik, T. V. et al. Omnidirectional absorption in nanostructured metal surfaces. Nat. Photonics 2 , 299–301 (2008)..

Chong, Y. D. et al. Coherent perfect absorbers: time-reversed lasers. Phys. Rev. Lett. 105 , 053901 (2010)..

Peumans, P., Yakimov, A. & Forrest, S. R. Small molecular weight organic thin-film photodetectors and solar cells. J. Appl. Phys. 93 , 3693–3723 (2003)..

Tiedje, T. et al. Limiting efficiency of silicon solar cells. IEEE Trans. Electron Devices 31 , 711–716 (1984)..

Stolz Roman, L. et al. Trapping light in polymer photodiodes with soft embossed gratings. Adv. Mater. 12 , 189–195 (2000)..

Campbell, P. & Green, M. A. Light trapping properties of pyramidally textured surfaces. J. Appl. Phys. 62 , 243–249 (1987)..

Yablonovitch, E. Statistical ray optics. J. Opt. Soc. Am. 72 , 899–907 (1982)..

Green, M. A. & Pillai, S. Harnessing plasmonics for solar cells. Nat. Photonics 6 , 130–132 (2012)..

Schuller, J. A. et al. Plasmonics for extreme light concentration and manipulation. Nat. Mater. 9 , 193–204 (2010)..

Cui, Y. X. et al. Plasmonic and metamaterial structures as electromagnetic absorbers. Laser Photonics Rev. 8 , 495–520 (2014)..

Farhat, M., Kais, S. & Alharbi, F. H. Plasmonically enhanced Schottky photovoltaic devices. Sci. Rep. 7 , 14253 (2017)..

Atwater, H. A. & Polman, A. Plasmonics for improved photovoltaic devices. Nat. Mater. 9 , 205–213 (2010)..

Landy, N. I. et al. Perfect metamaterial absorber. Phys. Rev. Lett. 100 , 207402 (2008)..

Liu, X. L. et al. Infrared spatial and frequency selective metamaterial with near-unity absorbance. Phys. Rev. Lett. 104 , 207403 (2010)..

Amin, M., Farhat, M. & Bağcı, H. An ultra-broadband multilayered graphene absorber. Opt. Express 21 , 29938–29948 (2013)..

Akselrod, G. M. et al. Large‐area metasurface perfect absorbers from visible to near‐infrared. Adv. Mater. 27 , 8028–8034 (2015)..

Hedayati, M. K. et al. Design of a perfect black absorber at visible frequencies using plasmonic metamaterials. Adv. Mater. 23 , 5410–5414 (2011)..

Aydin, K. et al. Broadband polarization-independent resonant light absorption using ultrathin plasmonic super absorbers. Nat. Commun. 2 , 517 (2011)..

Cui, Y. X. et al. Ultrabroadband light absorption by a sawtooth anisotropic metamaterial slab. Nano Lett. 12 , 1443–1447 (2012)..

Guo, C. F. et al. Metallic nanostructures for light trapping in energy-harvesting devices. Light Sci. Appl. 3 , e161 (2014)..

Tittl, A. et al. A switchable mid-infrared plasmonic perfect absorber with multispectral thermal imaging capability. Adv. Mater. 27 , 4597–4603 (2015)..

Diem, M., Koschny, T. & Soukoulis, C. M. Wide-angle perfect absorber/thermal emitter in the terahertz regime. Phys. Rev. B 79 , 033101 (2009)..

Yang, M. Y. et al. Enhanced radio-frequency sensors based on a self-dual emitter–absorber. Phys. Rev. Appl. 15 , 014026 (2021)..

Dao, T. D. et al. Hole array perfect absorbers for spectrally selective midwavelength infrared pyroelectric detectors. ACS Photonics 3 , 1271–1278 (2016)..

Liu, N. et al. Infrared perfect absorber and its application as plasmonic sensor. Nano Lett. 10 , 2342–2348 (2010)..

Goldberg, D. E. & Holland, J. H. Genetic algorithms and machine learning. Mach. Learn. 3 , 95–99 (1988)..

Campbell, S. D. et al. Review of numerical optimization techniques for meta-device design [Invited ] . Opt. Mater. Express 9 , 1842–1863 (2019)..

Jensen, J. S. & Sigmund, O. Topology optimization for nano‐photonics. Laser Photonics Rev. 5 , 308–321 (2011)..

Jordan, M. I. & Mitchell, T. M. Machine learning: trends, perspectives, and prospects. Science 349 , 255–260 (2015)..

LeCun, Y., Bengio, Y. & Hinton, G. Deep learning. Nature 521 , 436–444 (2015)..

Ma, W. et al. Deep learnin g for the design of photonic structures. Nat. Photonics 15 , 77–90 (2021)..

Molesky, S. et al. Inverse design in nanophotonics. Nat. Photonics 12 , 659–670 (2018)..

Baldi, P., Sadowski, P. & Whiteson, D. Searching for exotic particles in high-energy physics with deep learning. Nat. Commun. 5 , 4308 (2014)..

Carrasquilla, J. & Melko, R. G. Machine learning phases of matter. Nat. Phys. 13 , 431–434 (2017)..

Pilozzi, L. et al. Machine learning inverse problem for topological photonics. Commun. Phys. 1 , 57 (2018)..

Hughes, T. W. et al. Wave physics as an analog recurrent neural network. Sci. Adv. 5 , eaay6946 (2019)..

Qian, C. et al. Deep-learning-enabled self-adaptive microwave cloak without human intervention. Nat. Photonics 14 , 383–390 (2020)..

Zhang, Y. et al. Machine learning in electronic-quantum-matter imaging experiments. Nature 570 , 484–490 (2019)..

Loh, C. et al. Surrogate- and invariance-boosted contrastive learning for data-scarce applications in science. Nat. Commun. 13 , 4223 (2022)..

Ma, W., Cheng, F. & Liu, Y. M. Deep-learning-enabled on-demand design of chiral metamaterials. ACS Nano 12 , 6326–6334 (2018)..

Malkiel, I. et al. Plasmonic nanostructure design and characterization via deep learning. Light Sci. Appl. 7 , 60 (2018)..

Wiecha, P. R. & Muskens, O. L. Deep learning meets nanophotonics: a generalized accurate predictor for near fields and far fields of arbitrary 3D nanostructures. Nano Lett. 20 , 329–338 (2020)..

Ma, W. et al. Probabilistic representation and inverse design of metamaterials based on a deep generative model with semi-supervised learning strategy. Adv. Mater. 31 , 1901111 (2019)..

Kudyshev, Z. A. et al. Machine-learning-assisted metasurface design for high-efficiency thermal emitter optimization. Appl. Phys. Rev. 7 , 021407 (2020)..

Zhu, D. Y. et al. Building multifunctional metasystems via algorithmic construction. ACS Nano 15 , 2318–2326 (2021)..

Ma, W. et al. Pushing the limits of functionality-multiplexing capability in metasurface design based on statistical machine learning. Adv. Mater. 34 , 2110022 (2022)..

Xu, Y. H. et al. Software-defined nanophotonic devices and systems empowered by machine learning. Prog. Quantum Electron. 89 , 100469 (2023)..

Xiong, B. et al. Deep learning design for multiwavelength infrared image sensors based on dielectric freeform metasurface. Adv. Opt. Mater. 12 , 2302200 (2024)..

COMSOL Multiphysics. V5.6 (Build: 280). at https://www.comsol.com https://www.comsol.com .

Benning, M. & Burger, M. Modern regularization methods for inverse problems. Acta Numer. 27 , 1–111 (2018)..

Qiu, T. S. et al. Deep learning: a rapid and efficient route to automatic metasurface design. Adv. Sci. 6 , 1900128 (2019)..

Liu, D. J. et al. Training deep neural networks for the inverse design of nanophotonic structures. ACS Photonics 5 , 1365–1369 (2018)..

Ahmed, W. W. et al. Deterministic and probabilistic deep learning models for inverse design of broadband acoustic cloak. Phys. Rev. Res. 3 , 013142 (2021)..

Peurifoy, J. et al. Nanophotonic particle simulation and inverse design using artificial neural networks. Sci. Adv. 4 , eaar4206 (2018)..

Kiarashinejad, Y., Abdollahramezani, S. & Adibi, A. Deep learning approach based on dimensionality reduction for designing electromagnetic nanostructures. npj Comput. Mater. 6 , 12 (2020)..

Liu, Z. C. et al. Tackling photonic inverse design with machine learning. Adv. Sci. 8 , 2002923 (2021)..

Borsic, A. & Adler, A. A primal–dual interior-point framework for using the L1 or L2 norm on the data and regularization terms of inverse problems. Inverse Probl. 28 , 095011 (2012)..

Goodfellow, I., Bengio, Y. & Courville, A. Deep Learning. Adaptive Computation and Machine Learning Series (The MIT Press, Cambridge, 2016).

浏览量

Downloads

CSCD

文章被引用时，请邮件提醒。

Submit

工具集

关联资源

暂无数据