Abstract

Metallic plasmonic nanostructures have been widely used for ultra-sensitive, label-free and real-time chemical and biological molecule sensors. Computational modeling is the key for plasmonic sensor design and performance optimization, which relies on time-consuming electromagnetic simulations, and only the optimized result is useful while all other computation results are wasted. Deep learning method enabled by artificial neural networks provides a powerful and efficient tool to construct accurate correlation between plasmonic geometric parameters and resonance spectra. Without the need to run any costly simulations, the spectra of millions of different nanostructures can be obtained and the cost is only a one-time investment of two thousand groups of training data. This approach can be easily applied to other similar types of nanophotonic system which can help eliminate the simulation step and expedite the photonic sensor design process.

© 2019 Optical Society of America under the terms of the OSA Open Access Publishing Agreement

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References

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  1. H. Im, H. Shao, Y. I. Park, V. M. Peterson, C. M. Castro, R. Weissleder, and H. Lee, “Label-free detection and molecular profiling of exosomes with a nano-plasmonic sensor,” Nat. Biotechnol. 32(5), 490–495 (2014).
    [Crossref]
  2. P. Strobbia, E. R. Languirand, and B. M. Cullum, “Recent advances in plasmonic nanostructures for sensing: a review,” Opt. Eng. 54(10), 100902 (2015).
    [Crossref]
  3. M. I. Stockman, “Nanoplasmonic sensing and detection,” Science 348(6232), 287–288 (2015).
    [Crossref]
  4. J. N. Anker, W. P. Hall, O. Lyandres, N. C. Shah, J. Zhao, and R. P. V. Duyne, “Biosensing with plasmonic nanosensors,” Nat. Mater. 7(6), 442–453 (2008).
    [Crossref]
  5. O. Limaj, D. Etezadi, N. J. Wittenberg, D. Rodrigo, D. Yoo, S. H. Oh, and H. Altug, “Infrared plasmonic biosensor for real-time and label-free monitoring of lipid membranes,” Nano Lett. 16(2), 1502–1508 (2016).
    [Crossref]
  6. B. Špačková, P. Wrobel, M. Bocková, and J. Homola, “Optical biosensors based on plasmonic nanostructures: a review,” Proc. IEEE 104(12), 2380–2408 (2016).
    [Crossref]
  7. B. Liedberg, C. Nylander, and I. Lunström, “Surface plasmon resonance for gas detection and biosensing,” Sens. Actuators 4, 299–304 (1983).
    [Crossref]
  8. W. Ma, F. Cheng, and Y. Liu, “Deep-learning-enabled on-demand design of chiral metamaterials,” ACS Nano 12(6), 6326–6334 (2018).
    [Crossref]
  9. Z. Liu, D. Zhu, S. P. Rodrigues, K. T. Lee, and W. Cai, “Generative model for the inverse design of metasurfaces,” Nano Lett. 18(10), 6570–6576 (2018).
    [Crossref]
  10. E. Bor, O. Alparslan, M. Turduev, Y. S. Hanay, H. Kurt, S. I. Arakawa, and M. Murata, “Integrated silicon photonic device design by attractor selection mechanism based on artificial neural networks: optical coupler and asymmetric light transmitter,” Opt. Express 26(22), 29032–29044 (2018).
    [Crossref]
  11. J. Baxter, A. C. Lesina, J. M. Guay, A. Weck, P. Berini, and L. Ramunno, “Plasmonic colours predicted by deep learning,” Sci. Rep. 9(1), 8074 (2019).
    [Crossref]
  12. V. G. Kravets, A. V. Kabashin, W. L. Barnes, and A. N. Grigorenko, “Plasmonic surface lattice resonances: a review of properties and applications,” Chem. Rev. 118(12), 5912–5951 (2018).
    [Crossref]
  13. C. Valsecchi and A. G. Brolo, “Periodic metallic nanostructures as plasmonic chemical sensors,” Langmuir 29(19), 5638–5649 (2013).
    [Crossref]
  14. J. Peurifoy, Y. Shen, L. Jing, Y. Yang, F. Cano-Renteria, B. G. DeLacy, J. D. Joannopoulos, M. Tegmark, and M. Soljačić, “Nanophotonic particle simulation and inverse design using artificial neural networks,” Sci. Adv. 4(6), eaar4206 (2018).
    [Crossref]
  15. D. Liu, Y. Tan, E. Khoram, and Z. Yu, “Training deep neural networks for the inverse design of nanophotonic structures,” ACS Photonics 5(4), 1365–1369 (2018).
    [Crossref]
  16. 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 361(6406), 1004–1008 (2018).
    [Crossref]

2019 (1)

J. Baxter, A. C. Lesina, J. M. Guay, A. Weck, P. Berini, and L. Ramunno, “Plasmonic colours predicted by deep learning,” Sci. Rep. 9(1), 8074 (2019).
[Crossref]

2018 (7)

V. G. Kravets, A. V. Kabashin, W. L. Barnes, and A. N. Grigorenko, “Plasmonic surface lattice resonances: a review of properties and applications,” Chem. Rev. 118(12), 5912–5951 (2018).
[Crossref]

W. Ma, F. Cheng, and Y. Liu, “Deep-learning-enabled on-demand design of chiral metamaterials,” ACS Nano 12(6), 6326–6334 (2018).
[Crossref]

Z. Liu, D. Zhu, S. P. Rodrigues, K. T. Lee, and W. Cai, “Generative model for the inverse design of metasurfaces,” Nano Lett. 18(10), 6570–6576 (2018).
[Crossref]

E. Bor, O. Alparslan, M. Turduev, Y. S. Hanay, H. Kurt, S. I. Arakawa, and M. Murata, “Integrated silicon photonic device design by attractor selection mechanism based on artificial neural networks: optical coupler and asymmetric light transmitter,” Opt. Express 26(22), 29032–29044 (2018).
[Crossref]

J. Peurifoy, Y. Shen, L. Jing, Y. Yang, F. Cano-Renteria, B. G. DeLacy, J. D. Joannopoulos, M. Tegmark, and M. Soljačić, “Nanophotonic particle simulation and inverse design using artificial neural networks,” Sci. Adv. 4(6), eaar4206 (2018).
[Crossref]

D. Liu, Y. Tan, E. Khoram, and Z. Yu, “Training deep neural networks for the inverse design of nanophotonic structures,” ACS Photonics 5(4), 1365–1369 (2018).
[Crossref]

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 361(6406), 1004–1008 (2018).
[Crossref]

2016 (2)

O. Limaj, D. Etezadi, N. J. Wittenberg, D. Rodrigo, D. Yoo, S. H. Oh, and H. Altug, “Infrared plasmonic biosensor for real-time and label-free monitoring of lipid membranes,” Nano Lett. 16(2), 1502–1508 (2016).
[Crossref]

B. Špačková, P. Wrobel, M. Bocková, and J. Homola, “Optical biosensors based on plasmonic nanostructures: a review,” Proc. IEEE 104(12), 2380–2408 (2016).
[Crossref]

2015 (2)

P. Strobbia, E. R. Languirand, and B. M. Cullum, “Recent advances in plasmonic nanostructures for sensing: a review,” Opt. Eng. 54(10), 100902 (2015).
[Crossref]

M. I. Stockman, “Nanoplasmonic sensing and detection,” Science 348(6232), 287–288 (2015).
[Crossref]

2014 (1)

H. Im, H. Shao, Y. I. Park, V. M. Peterson, C. M. Castro, R. Weissleder, and H. Lee, “Label-free detection and molecular profiling of exosomes with a nano-plasmonic sensor,” Nat. Biotechnol. 32(5), 490–495 (2014).
[Crossref]

2013 (1)

C. Valsecchi and A. G. Brolo, “Periodic metallic nanostructures as plasmonic chemical sensors,” Langmuir 29(19), 5638–5649 (2013).
[Crossref]

2008 (1)

J. N. Anker, W. P. Hall, O. Lyandres, N. C. Shah, J. Zhao, and R. P. V. Duyne, “Biosensing with plasmonic nanosensors,” Nat. Mater. 7(6), 442–453 (2008).
[Crossref]

1983 (1)

B. Liedberg, C. Nylander, and I. Lunström, “Surface plasmon resonance for gas detection and biosensing,” Sens. Actuators 4, 299–304 (1983).
[Crossref]

Alparslan, O.

Altug, H.

O. Limaj, D. Etezadi, N. J. Wittenberg, D. Rodrigo, D. Yoo, S. H. Oh, and H. Altug, “Infrared plasmonic biosensor for real-time and label-free monitoring of lipid membranes,” Nano Lett. 16(2), 1502–1508 (2016).
[Crossref]

Anker, J. N.

J. N. Anker, W. P. Hall, O. Lyandres, N. C. Shah, J. Zhao, and R. P. V. Duyne, “Biosensing with plasmonic nanosensors,” Nat. Mater. 7(6), 442–453 (2008).
[Crossref]

Arakawa, S. I.

Barnes, W. L.

V. G. Kravets, A. V. Kabashin, W. L. Barnes, and A. N. Grigorenko, “Plasmonic surface lattice resonances: a review of properties and applications,” Chem. Rev. 118(12), 5912–5951 (2018).
[Crossref]

Baxter, J.

J. Baxter, A. C. Lesina, J. M. Guay, A. Weck, P. Berini, and L. Ramunno, “Plasmonic colours predicted by deep learning,” Sci. Rep. 9(1), 8074 (2019).
[Crossref]

Berini, P.

J. Baxter, A. C. Lesina, J. M. Guay, A. Weck, P. Berini, and L. Ramunno, “Plasmonic colours predicted by deep learning,” Sci. Rep. 9(1), 8074 (2019).
[Crossref]

Bocková, M.

B. Špačková, P. Wrobel, M. Bocková, and J. Homola, “Optical biosensors based on plasmonic nanostructures: a review,” Proc. IEEE 104(12), 2380–2408 (2016).
[Crossref]

Bor, E.

Brolo, A. G.

C. Valsecchi and A. G. Brolo, “Periodic metallic nanostructures as plasmonic chemical sensors,” Langmuir 29(19), 5638–5649 (2013).
[Crossref]

Cai, W.

Z. Liu, D. Zhu, S. P. Rodrigues, K. T. Lee, and W. Cai, “Generative model for the inverse design of metasurfaces,” Nano Lett. 18(10), 6570–6576 (2018).
[Crossref]

Cano-Renteria, F.

J. Peurifoy, Y. Shen, L. Jing, Y. Yang, F. Cano-Renteria, B. G. DeLacy, J. D. Joannopoulos, M. Tegmark, and M. Soljačić, “Nanophotonic particle simulation and inverse design using artificial neural networks,” Sci. Adv. 4(6), eaar4206 (2018).
[Crossref]

Castro, C. M.

H. Im, H. Shao, Y. I. Park, V. M. Peterson, C. M. Castro, R. Weissleder, and H. Lee, “Label-free detection and molecular profiling of exosomes with a nano-plasmonic sensor,” Nat. Biotechnol. 32(5), 490–495 (2014).
[Crossref]

Cheng, F.

W. Ma, F. Cheng, and Y. Liu, “Deep-learning-enabled on-demand design of chiral metamaterials,” ACS Nano 12(6), 6326–6334 (2018).
[Crossref]

Cullum, B. M.

P. Strobbia, E. R. Languirand, and B. M. Cullum, “Recent advances in plasmonic nanostructures for sensing: a review,” Opt. Eng. 54(10), 100902 (2015).
[Crossref]

DeLacy, B. G.

J. Peurifoy, Y. Shen, L. Jing, Y. Yang, F. Cano-Renteria, B. G. DeLacy, J. D. Joannopoulos, M. Tegmark, and M. Soljačić, “Nanophotonic particle simulation and inverse design using artificial neural networks,” Sci. Adv. 4(6), eaar4206 (2018).
[Crossref]

Duyne, R. P. V.

J. N. Anker, W. P. Hall, O. Lyandres, N. C. Shah, J. Zhao, and R. P. V. Duyne, “Biosensing with plasmonic nanosensors,” Nat. Mater. 7(6), 442–453 (2008).
[Crossref]

Etezadi, D.

O. Limaj, D. Etezadi, N. J. Wittenberg, D. Rodrigo, D. Yoo, S. H. Oh, and H. Altug, “Infrared plasmonic biosensor for real-time and label-free monitoring of lipid membranes,” Nano Lett. 16(2), 1502–1508 (2016).
[Crossref]

Grigorenko, A. N.

V. G. Kravets, A. V. Kabashin, W. L. Barnes, and A. N. Grigorenko, “Plasmonic surface lattice resonances: a review of properties and applications,” Chem. Rev. 118(12), 5912–5951 (2018).
[Crossref]

Guay, J. M.

J. Baxter, A. C. Lesina, J. M. Guay, A. Weck, P. Berini, and L. Ramunno, “Plasmonic colours predicted by deep learning,” Sci. Rep. 9(1), 8074 (2019).
[Crossref]

Hall, W. P.

J. N. Anker, W. P. Hall, O. Lyandres, N. C. Shah, J. Zhao, and R. P. V. Duyne, “Biosensing with plasmonic nanosensors,” Nat. Mater. 7(6), 442–453 (2008).
[Crossref]

Hanay, Y. S.

Homola, J.

B. Špačková, P. Wrobel, M. Bocková, and J. Homola, “Optical biosensors based on plasmonic nanostructures: a review,” Proc. IEEE 104(12), 2380–2408 (2016).
[Crossref]

Im, H.

H. Im, H. Shao, Y. I. Park, V. M. Peterson, C. M. Castro, R. Weissleder, and H. Lee, “Label-free detection and molecular profiling of exosomes with a nano-plasmonic sensor,” Nat. Biotechnol. 32(5), 490–495 (2014).
[Crossref]

Jarrahi, M.

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 361(6406), 1004–1008 (2018).
[Crossref]

Jing, L.

J. Peurifoy, Y. Shen, L. Jing, Y. Yang, F. Cano-Renteria, B. G. DeLacy, J. D. Joannopoulos, M. Tegmark, and M. Soljačić, “Nanophotonic particle simulation and inverse design using artificial neural networks,” Sci. Adv. 4(6), eaar4206 (2018).
[Crossref]

Joannopoulos, J. D.

J. Peurifoy, Y. Shen, L. Jing, Y. Yang, F. Cano-Renteria, B. G. DeLacy, J. D. Joannopoulos, M. Tegmark, and M. Soljačić, “Nanophotonic particle simulation and inverse design using artificial neural networks,” Sci. Adv. 4(6), eaar4206 (2018).
[Crossref]

Kabashin, A. V.

V. G. Kravets, A. V. Kabashin, W. L. Barnes, and A. N. Grigorenko, “Plasmonic surface lattice resonances: a review of properties and applications,” Chem. Rev. 118(12), 5912–5951 (2018).
[Crossref]

Khoram, E.

D. Liu, Y. Tan, E. Khoram, and Z. Yu, “Training deep neural networks for the inverse design of nanophotonic structures,” ACS Photonics 5(4), 1365–1369 (2018).
[Crossref]

Kravets, V. G.

V. G. Kravets, A. V. Kabashin, W. L. Barnes, and A. N. Grigorenko, “Plasmonic surface lattice resonances: a review of properties and applications,” Chem. Rev. 118(12), 5912–5951 (2018).
[Crossref]

Kurt, H.

Languirand, E. R.

P. Strobbia, E. R. Languirand, and B. M. Cullum, “Recent advances in plasmonic nanostructures for sensing: a review,” Opt. Eng. 54(10), 100902 (2015).
[Crossref]

Lee, H.

H. Im, H. Shao, Y. I. Park, V. M. Peterson, C. M. Castro, R. Weissleder, and H. Lee, “Label-free detection and molecular profiling of exosomes with a nano-plasmonic sensor,” Nat. Biotechnol. 32(5), 490–495 (2014).
[Crossref]

Lee, K. T.

Z. Liu, D. Zhu, S. P. Rodrigues, K. T. Lee, and W. Cai, “Generative model for the inverse design of metasurfaces,” Nano Lett. 18(10), 6570–6576 (2018).
[Crossref]

Lesina, A. C.

J. Baxter, A. C. Lesina, J. M. Guay, A. Weck, P. Berini, and L. Ramunno, “Plasmonic colours predicted by deep learning,” Sci. Rep. 9(1), 8074 (2019).
[Crossref]

Liedberg, B.

B. Liedberg, C. Nylander, and I. Lunström, “Surface plasmon resonance for gas detection and biosensing,” Sens. Actuators 4, 299–304 (1983).
[Crossref]

Limaj, O.

O. Limaj, D. Etezadi, N. J. Wittenberg, D. Rodrigo, D. Yoo, S. H. Oh, and H. Altug, “Infrared plasmonic biosensor for real-time and label-free monitoring of lipid membranes,” Nano Lett. 16(2), 1502–1508 (2016).
[Crossref]

Lin, X.

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 361(6406), 1004–1008 (2018).
[Crossref]

Liu, D.

D. Liu, Y. Tan, E. Khoram, and Z. Yu, “Training deep neural networks for the inverse design of nanophotonic structures,” ACS Photonics 5(4), 1365–1369 (2018).
[Crossref]

Liu, Y.

W. Ma, F. Cheng, and Y. Liu, “Deep-learning-enabled on-demand design of chiral metamaterials,” ACS Nano 12(6), 6326–6334 (2018).
[Crossref]

Liu, Z.

Z. Liu, D. Zhu, S. P. Rodrigues, K. T. Lee, and W. Cai, “Generative model for the inverse design of metasurfaces,” Nano Lett. 18(10), 6570–6576 (2018).
[Crossref]

Lunström, I.

B. Liedberg, C. Nylander, and I. Lunström, “Surface plasmon resonance for gas detection and biosensing,” Sens. Actuators 4, 299–304 (1983).
[Crossref]

Luo, Y.

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 361(6406), 1004–1008 (2018).
[Crossref]

Lyandres, O.

J. N. Anker, W. P. Hall, O. Lyandres, N. C. Shah, J. Zhao, and R. P. V. Duyne, “Biosensing with plasmonic nanosensors,” Nat. Mater. 7(6), 442–453 (2008).
[Crossref]

Ma, W.

W. Ma, F. Cheng, and Y. Liu, “Deep-learning-enabled on-demand design of chiral metamaterials,” ACS Nano 12(6), 6326–6334 (2018).
[Crossref]

Murata, M.

Nylander, C.

B. Liedberg, C. Nylander, and I. Lunström, “Surface plasmon resonance for gas detection and biosensing,” Sens. Actuators 4, 299–304 (1983).
[Crossref]

Oh, S. H.

O. Limaj, D. Etezadi, N. J. Wittenberg, D. Rodrigo, D. Yoo, S. H. Oh, and H. Altug, “Infrared plasmonic biosensor for real-time and label-free monitoring of lipid membranes,” Nano Lett. 16(2), 1502–1508 (2016).
[Crossref]

Ozcan, A.

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 361(6406), 1004–1008 (2018).
[Crossref]

Park, Y. I.

H. Im, H. Shao, Y. I. Park, V. M. Peterson, C. M. Castro, R. Weissleder, and H. Lee, “Label-free detection and molecular profiling of exosomes with a nano-plasmonic sensor,” Nat. Biotechnol. 32(5), 490–495 (2014).
[Crossref]

Peterson, V. M.

H. Im, H. Shao, Y. I. Park, V. M. Peterson, C. M. Castro, R. Weissleder, and H. Lee, “Label-free detection and molecular profiling of exosomes with a nano-plasmonic sensor,” Nat. Biotechnol. 32(5), 490–495 (2014).
[Crossref]

Peurifoy, J.

J. Peurifoy, Y. Shen, L. Jing, Y. Yang, F. Cano-Renteria, B. G. DeLacy, J. D. Joannopoulos, M. Tegmark, and M. Soljačić, “Nanophotonic particle simulation and inverse design using artificial neural networks,” Sci. Adv. 4(6), eaar4206 (2018).
[Crossref]

Ramunno, L.

J. Baxter, A. C. Lesina, J. M. Guay, A. Weck, P. Berini, and L. Ramunno, “Plasmonic colours predicted by deep learning,” Sci. Rep. 9(1), 8074 (2019).
[Crossref]

Rivenson, Y.

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 361(6406), 1004–1008 (2018).
[Crossref]

Rodrigo, D.

O. Limaj, D. Etezadi, N. J. Wittenberg, D. Rodrigo, D. Yoo, S. H. Oh, and H. Altug, “Infrared plasmonic biosensor for real-time and label-free monitoring of lipid membranes,” Nano Lett. 16(2), 1502–1508 (2016).
[Crossref]

Rodrigues, S. P.

Z. Liu, D. Zhu, S. P. Rodrigues, K. T. Lee, and W. Cai, “Generative model for the inverse design of metasurfaces,” Nano Lett. 18(10), 6570–6576 (2018).
[Crossref]

Shah, N. C.

J. N. Anker, W. P. Hall, O. Lyandres, N. C. Shah, J. Zhao, and R. P. V. Duyne, “Biosensing with plasmonic nanosensors,” Nat. Mater. 7(6), 442–453 (2008).
[Crossref]

Shao, H.

H. Im, H. Shao, Y. I. Park, V. M. Peterson, C. M. Castro, R. Weissleder, and H. Lee, “Label-free detection and molecular profiling of exosomes with a nano-plasmonic sensor,” Nat. Biotechnol. 32(5), 490–495 (2014).
[Crossref]

Shen, Y.

J. Peurifoy, Y. Shen, L. Jing, Y. Yang, F. Cano-Renteria, B. G. DeLacy, J. D. Joannopoulos, M. Tegmark, and M. Soljačić, “Nanophotonic particle simulation and inverse design using artificial neural networks,” Sci. Adv. 4(6), eaar4206 (2018).
[Crossref]

Soljacic, M.

J. Peurifoy, Y. Shen, L. Jing, Y. Yang, F. Cano-Renteria, B. G. DeLacy, J. D. Joannopoulos, M. Tegmark, and M. Soljačić, “Nanophotonic particle simulation and inverse design using artificial neural networks,” Sci. Adv. 4(6), eaar4206 (2018).
[Crossref]

Špacková, B.

B. Špačková, P. Wrobel, M. Bocková, and J. Homola, “Optical biosensors based on plasmonic nanostructures: a review,” Proc. IEEE 104(12), 2380–2408 (2016).
[Crossref]

Stockman, M. I.

M. I. Stockman, “Nanoplasmonic sensing and detection,” Science 348(6232), 287–288 (2015).
[Crossref]

Strobbia, P.

P. Strobbia, E. R. Languirand, and B. M. Cullum, “Recent advances in plasmonic nanostructures for sensing: a review,” Opt. Eng. 54(10), 100902 (2015).
[Crossref]

Tan, Y.

D. Liu, Y. Tan, E. Khoram, and Z. Yu, “Training deep neural networks for the inverse design of nanophotonic structures,” ACS Photonics 5(4), 1365–1369 (2018).
[Crossref]

Tegmark, M.

J. Peurifoy, Y. Shen, L. Jing, Y. Yang, F. Cano-Renteria, B. G. DeLacy, J. D. Joannopoulos, M. Tegmark, and M. Soljačić, “Nanophotonic particle simulation and inverse design using artificial neural networks,” Sci. Adv. 4(6), eaar4206 (2018).
[Crossref]

Turduev, M.

Valsecchi, C.

C. Valsecchi and A. G. Brolo, “Periodic metallic nanostructures as plasmonic chemical sensors,” Langmuir 29(19), 5638–5649 (2013).
[Crossref]

Veli, M.

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 361(6406), 1004–1008 (2018).
[Crossref]

Weck, A.

J. Baxter, A. C. Lesina, J. M. Guay, A. Weck, P. Berini, and L. Ramunno, “Plasmonic colours predicted by deep learning,” Sci. Rep. 9(1), 8074 (2019).
[Crossref]

Weissleder, R.

H. Im, H. Shao, Y. I. Park, V. M. Peterson, C. M. Castro, R. Weissleder, and H. Lee, “Label-free detection and molecular profiling of exosomes with a nano-plasmonic sensor,” Nat. Biotechnol. 32(5), 490–495 (2014).
[Crossref]

Wittenberg, N. J.

O. Limaj, D. Etezadi, N. J. Wittenberg, D. Rodrigo, D. Yoo, S. H. Oh, and H. Altug, “Infrared plasmonic biosensor for real-time and label-free monitoring of lipid membranes,” Nano Lett. 16(2), 1502–1508 (2016).
[Crossref]

Wrobel, P.

B. Špačková, P. Wrobel, M. Bocková, and J. Homola, “Optical biosensors based on plasmonic nanostructures: a review,” Proc. IEEE 104(12), 2380–2408 (2016).
[Crossref]

Yang, Y.

J. Peurifoy, Y. Shen, L. Jing, Y. Yang, F. Cano-Renteria, B. G. DeLacy, J. D. Joannopoulos, M. Tegmark, and M. Soljačić, “Nanophotonic particle simulation and inverse design using artificial neural networks,” Sci. Adv. 4(6), eaar4206 (2018).
[Crossref]

Yardimci, N. T.

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 361(6406), 1004–1008 (2018).
[Crossref]

Yoo, D.

O. Limaj, D. Etezadi, N. J. Wittenberg, D. Rodrigo, D. Yoo, S. H. Oh, and H. Altug, “Infrared plasmonic biosensor for real-time and label-free monitoring of lipid membranes,” Nano Lett. 16(2), 1502–1508 (2016).
[Crossref]

Yu, Z.

D. Liu, Y. Tan, E. Khoram, and Z. Yu, “Training deep neural networks for the inverse design of nanophotonic structures,” ACS Photonics 5(4), 1365–1369 (2018).
[Crossref]

Zhao, J.

J. N. Anker, W. P. Hall, O. Lyandres, N. C. Shah, J. Zhao, and R. P. V. Duyne, “Biosensing with plasmonic nanosensors,” Nat. Mater. 7(6), 442–453 (2008).
[Crossref]

Zhu, D.

Z. Liu, D. Zhu, S. P. Rodrigues, K. T. Lee, and W. Cai, “Generative model for the inverse design of metasurfaces,” Nano Lett. 18(10), 6570–6576 (2018).
[Crossref]

ACS Nano (1)

W. Ma, F. Cheng, and Y. Liu, “Deep-learning-enabled on-demand design of chiral metamaterials,” ACS Nano 12(6), 6326–6334 (2018).
[Crossref]

ACS Photonics (1)

D. Liu, Y. Tan, E. Khoram, and Z. Yu, “Training deep neural networks for the inverse design of nanophotonic structures,” ACS Photonics 5(4), 1365–1369 (2018).
[Crossref]

Chem. Rev. (1)

V. G. Kravets, A. V. Kabashin, W. L. Barnes, and A. N. Grigorenko, “Plasmonic surface lattice resonances: a review of properties and applications,” Chem. Rev. 118(12), 5912–5951 (2018).
[Crossref]

Langmuir (1)

C. Valsecchi and A. G. Brolo, “Periodic metallic nanostructures as plasmonic chemical sensors,” Langmuir 29(19), 5638–5649 (2013).
[Crossref]

Nano Lett. (2)

Z. Liu, D. Zhu, S. P. Rodrigues, K. T. Lee, and W. Cai, “Generative model for the inverse design of metasurfaces,” Nano Lett. 18(10), 6570–6576 (2018).
[Crossref]

O. Limaj, D. Etezadi, N. J. Wittenberg, D. Rodrigo, D. Yoo, S. H. Oh, and H. Altug, “Infrared plasmonic biosensor for real-time and label-free monitoring of lipid membranes,” Nano Lett. 16(2), 1502–1508 (2016).
[Crossref]

Nat. Biotechnol. (1)

H. Im, H. Shao, Y. I. Park, V. M. Peterson, C. M. Castro, R. Weissleder, and H. Lee, “Label-free detection and molecular profiling of exosomes with a nano-plasmonic sensor,” Nat. Biotechnol. 32(5), 490–495 (2014).
[Crossref]

Nat. Mater. (1)

J. N. Anker, W. P. Hall, O. Lyandres, N. C. Shah, J. Zhao, and R. P. V. Duyne, “Biosensing with plasmonic nanosensors,” Nat. Mater. 7(6), 442–453 (2008).
[Crossref]

Opt. Eng. (1)

P. Strobbia, E. R. Languirand, and B. M. Cullum, “Recent advances in plasmonic nanostructures for sensing: a review,” Opt. Eng. 54(10), 100902 (2015).
[Crossref]

Opt. Express (1)

Proc. IEEE (1)

B. Špačková, P. Wrobel, M. Bocková, and J. Homola, “Optical biosensors based on plasmonic nanostructures: a review,” Proc. IEEE 104(12), 2380–2408 (2016).
[Crossref]

Sci. Adv. (1)

J. Peurifoy, Y. Shen, L. Jing, Y. Yang, F. Cano-Renteria, B. G. DeLacy, J. D. Joannopoulos, M. Tegmark, and M. Soljačić, “Nanophotonic particle simulation and inverse design using artificial neural networks,” Sci. Adv. 4(6), eaar4206 (2018).
[Crossref]

Sci. Rep. (1)

J. Baxter, A. C. Lesina, J. M. Guay, A. Weck, P. Berini, and L. Ramunno, “Plasmonic colours predicted by deep learning,” Sci. Rep. 9(1), 8074 (2019).
[Crossref]

Science (2)

M. I. Stockman, “Nanoplasmonic sensing and detection,” Science 348(6232), 287–288 (2015).
[Crossref]

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 361(6406), 1004–1008 (2018).
[Crossref]

Sens. Actuators (1)

B. Liedberg, C. Nylander, and I. Lunström, “Surface plasmon resonance for gas detection and biosensing,” Sens. Actuators 4, 299–304 (1983).
[Crossref]

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Figures (4)

Fig. 1.
Fig. 1. Schematic illustration of the plasmonic nanostructure, NN, and spectra data. (a) Structure of the periodic gold nanodisks. The studied geometry parameters include the diameter (D) and height (H) of nanodisks, and the period (P) of repeating units. (b) Architecture of a basic NN. (c) A sample of the spectra consist of discrete spectra data.
Fig. 2.
Fig. 2. Results of the applied NN. (a) Architecture of the best trained NN, including four hidden layers and 330 nodes per layer. The input layer has three nodes which represent P, D, H. The output layer has 81 nodes, representing the discrete spectra points. (b) The loss curve of training and validation. The final value of training loss and validation loss are 7.38×10−6 and 3.86×10−5, respectively.
Fig. 3.
Fig. 3. Results of the test samples. (a) Error distribution of the test samples. (b) Statistics of the samples divided by different ranges of errors.
Fig. 4.
Fig. 4. Representative results of NN predicted spectra compared with EM simulated spectra. (a) Spectrum with relative error at 2.01%. (b) Spectrum with relative error at 4.25%. (c) Spectrum with relative error at 6.18%. (d) Spectrum with relative error at 16.89%.

Equations (1)

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σ   =   λ 1 λ 2 | X ( λ ) Y ( λ ) | d λ λ 1 λ 2 Y ( λ ) d λ

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