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A wavelength-scale black phosphorus spectrometer

Abstract

On-chip spectrometers with compact footprints are being extensively investigated owing to their promising future in critical applications such as sensing, surveillance and spectral imaging. Most existing miniaturized spectrometers use large arrays of photodetection elements to capture different spectral components of incident light, from which its spectrum is reconstructed. Here, we demonstrate a mid-infrared spectrometer in the 2–9 µm spectral range, utilizing a single tunable black phosphorus photodetector with an active area footprint of only 9 × 16 µm2, along with a unique spectral learning procedure. Such a single-detector spectrometer has a compact size at the scale of the operational wavelength. Leveraging the wavelength and bias-dependent responsivity matrix learned from the spectra of a tunable blackbody source, we reconstruct unknown spectra from their corresponding photoresponse vectors. Enabled by the strong Stark effect and the tunable light–matter interactions in black phosphorus, our single-detector spectrometer shows remarkable potential in the reconstruction of the spectra of both monochromatic and broadband light. Furthermore, its ultracompact structure that is free from bulky interferometers and gratings, together with its electrically reconfigurable nature, may open up pathways towards on-chip mid-infrared spectroscopy and spectral imaging.

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Fig. 1: The operational principle of the single-detector BP spectrometer consists of three steps: learning, sampling and reconstruction.
Fig. 2: Characterization of the tunable BP spectrometer.
Fig. 3: Spectral responsivity matrix learning.
Fig. 4: Spectroscopy demonstrations.

Data availability

The data that support the plots within this paper are available from the corresponding author upon reasonable request.

Code availability

The code and algorithm in this paper are available from the corresponding author upon reasonable request.

References

  1. Hollas, J. M. High Resolution Spectroscopy (Butterworth-Heinemann, 1982).

  2. Griffiths, P. R. Fourier-transform infrared spectrometry. Science 222, 297–302 (1983).

    ADS  Article  Google Scholar 

  3. Bao, J. & Bawendi, M. G. A colloidal quantum dot spectrometer. Nature 523, 67–69 (2015).

    ADS  Article  Google Scholar 

  4. Tittl, A. et al. Imaging-based molecular barcoding with pixelated dielectric metasurfaces. Science 360, 1105–1109 (2018).

    ADS  MathSciNet  Article  Google Scholar 

  5. Yang, Z. Y. et al. Single-nanowire spectrometers. Science 365, 1017–1020 (2019).

    ADS  Article  Google Scholar 

  6. Wang, Z. et al. Single-shot on-chip spectral sensors based on photonic crystal slabs. Nat. Commun. 10, 1020 (2019).

    ADS  Article  Google Scholar 

  7. Meng, J. J., Cadusch, J. J. & Crozier, K. B. Detector-only spectrometer based on structurally colored silicon nanowires and a reconstruction algorithm. Nano Lett. 20, 320–328 (2020).

    ADS  Article  Google Scholar 

  8. Le Coarer, E. et al. Wavelength-scale stationary-wave integrated Fourier-transform spectrometry. Nat. Photon. 1, 473–478 (2007).

    ADS  Article  Google Scholar 

  9. Kita, D. M. et al. High-performance and scalable on-chip digital Fourier transform spectroscopy. Nat. Commun. 9, 4405 (2018).

    ADS  Article  Google Scholar 

  10. Shrestha, V. R. et al. Mid- to long-wave infrared computational spectroscopy with a graphene metasurface modulator. Sci. Rep. 10, 5377 (2020).

    ADS  Article  Google Scholar 

  11. Liu, H. et al. Phosphorene: an unexplored 2D semiconductor with a high hole mobility. ACS Nano 8, 4033–4041 (2014).

    Article  Google Scholar 

  12. Li, L. K. et al. Black phosphorus field-effect transistors. Nat. Nanotechnol. 9, 372–377 (2014).

    ADS  Article  Google Scholar 

  13. Xia, F., Wang, H. & Jia, Y. C. Rediscovering black phosphorus as an anisotropic layered material for optoelectronics and electronics. Nat. Commun. 5, 4458 (2014).

    ADS  Article  Google Scholar 

  14. Kim, J. et al. Observation of tunable band gap and anisotropic Dirac semimetal state in black phosphorus. Science 349, 723–726 (2015).

    Article  Google Scholar 

  15. Liu, Y. P. et al. Gate-tunable giant Stark effect in few-layer black phosphorus. Nano Lett. 17, 1970–1977 (2017).

    ADS  Article  Google Scholar 

  16. Whitney, W. S. et al. Field effect optoelectronic modulation of quantum-confined carriers in black phosphorus. Nano Lett. 17, 78–84 (2017).

    ADS  Article  Google Scholar 

  17. Chen, X. L. et al. Widely tunable black phosphorus mid-infrared photodetector. Nat. Commun. 8, 1672 (2017).

    ADS  Article  Google Scholar 

  18. Chen, C. et al. Widely tunable mid-infrared light emission in thin-film black phosphorus. Sci. Adv. 6, eaay6134 (2020).

    ADS  Article  Google Scholar 

  19. Mak, K. F., Ju, L., Wang, F. & Heinz, T. F. Optical spectroscopy of graphene: from the far infrared to the ultraviolet. Solid State Commun. 152, 1341–1349 (2012).

    ADS  Article  Google Scholar 

  20. Yan, H. et al. Infrared spectroscopy of wafer-scale graphene. ACS Nano 5, 9854–9860 (2011).

    Article  Google Scholar 

  21. Dean, C. R. et al. Boron nitride substrates for high-quality graphene electronics. Nat. Nanotechnol. 5, 722–726 (2010).

    ADS  Article  Google Scholar 

  22. Rogalski, A., Martyniuk, P. & Kopytko, M. Challenges of small-pixel infrared detectors: a review. Rep. Prog. Phys. 79, 4 (2016).

    Article  Google Scholar 

  23. Sandsten, J., Weibring, P., Edner, H. & Svanberg, S. Real-time gas-correlation imaging employing thermal background radiation. Opt. Express 6, 92–103 (2000).

    ADS  Article  Google Scholar 

  24. Gaszczak, A., Breckon, T. P. & Han, J. Real-time people and vehicle detection from UAV imagery. Proc. SPIE 7878, 78780B (2011).

    ADS  Article  Google Scholar 

  25. Hwang, S., Park, J., Kim, N., Choi, Y. & Kweon, I. S. Multispectral pedestrian detection: benchmark dataset and baseline. In Proc. Conf. on Computer Vision and Pattern Recognition, 1037–1045 (IEEE, 2015).

  26. Underwood, E. C., Ustin, S. L. & Ramirez, C. M. A comparison of spatial and spectral image resolution for mapping invasive plants in coastal California. Environ. Manag. 39, 63–83 (2007).

    ADS  Article  Google Scholar 

  27. Guo, Q. et al. Black phosphorus mid-infrared photodetectors with high gain. Nano Lett. 16, 4648–4655 (2016).

    ADS  Article  Google Scholar 

  28. Deng, B. et al. Efficient electrical control of thin-film black phosphorus bandgap. Nat. Commun. 8, 14474 (2017).

    ADS  Article  Google Scholar 

  29. FT-IR Spectrometer Detectors (Newport, 2021); https://www.newport.com/f/detectors-for-oriel-mir8035-ft-spectrometers

  30. VERTEX Series: Advanced Research FT-IR Spectrometers (Bruker, 2018); https://www.bruker.com/fileadmin/user_upload/8-PDF-Docs/OpticalSpectrospcopy/FT-IR/VERTEX/Brochures/VERTEXseries_Brochure_EN.pdf

  31. Gabor, N. M. et al. Hot carrier-assisted intrinsic photoresponse in graphene. Science 334, 648–652 (2011).

    ADS  Article  Google Scholar 

  32. Selberherr, S. Analysis and Simulation of Semiconductor Devices (Springer, 1984).

  33. Planck, M. & Masius, M. The Theory of Heat Radiation (P. Blakiston’s Son & Co., 1914).

    Google Scholar 

  34. SR-2 High Temperature Cavity Blackbody (CI Systems, 2019); https://www.ci-systems.com/Files/Source%20-%20SR-2%20%20Cavity%20BB.pdf

  35. Tikhonov, A. N., Goncharsky, A. V., Stepanov, V. V. & Yagola, A. G. Numerical Methods for the Solution of Ill-Posed Problems (Kluwer Academic, 1995).

  36. Hansen, P. C. Rank-Deficient and Discrete Ill-Posed Problems: Numerical Aspects of Linear Inversion (Society for Industrial and Applied Mathematics, 2005).

  37. Tibshirani, R. Regression shrinkage and selection via the lasso. J. R. Stat. Soc. Ser. B 58, 267–288 (1996).

    MathSciNet  MATH  Google Scholar 

  38. Proakis, J. G. & Manolakis, D. G. Digital Signal Processing: Principles, Algorithms, and Applications 3rd edn (Prentice Hall, 1996).

  39. Carbon Dioxide Infrared Spectrum (NIST Chemistry WebBook, 2018); https://webbook.nist.gov/cgi/cbook.cgi?ID=C124389&Units=SI&Type=IR-SPEC&Index=1#IR-SPEC

  40. Candès, J. E. & Wakin, B. M. An introduction to compressive sampling. IEEE Signal Process. Mag. 25, 21–30 (2008).

    ADS  Article  Google Scholar 

  41. Eldar, Y. C. & Kutyniok, G. Compressed Sensing: Theory and Applications (Cambridge Univ. Press, 2012).

    Book  Google Scholar 

  42. Candès, J. E., Romberg, K. J. & Tao, T. Stable signal recovery from incomplete and inaccurate measurements. Commun. Pure Appl. Math. 59, 1207–1223 (2006).

    MathSciNet  Article  Google Scholar 

  43. Arora, S. & Barak, B. Computational Complexity: A Modern Approach (Cambridge Univ. Press, 2009).

    Book  Google Scholar 

  44. Samuel, A. L. Some studies in machine learning using the game of checkers. IBM J. Res. Dev. 3, 210–229 (1959).

    MathSciNet  Article  Google Scholar 

  45. Polikar, R., Upda, L., Upda, S. S. & Honavar, V. Learn++: an incremental learning algorithm for supervised neural networks. IEEE Trans. Syst. Man Cybern. C 31, 497–508 (2001).

    Article  Google Scholar 

  46. Goodfellow, I., Bengio, Y. & Courville, A. Deep Learning (MIT Press, 2016).

    MATH  Google Scholar 

  47. Wang, L. et al. One-dimensional electrical contact to a two-dimensional material. Science 342, 614–617 (2013).

    ADS  Article  Google Scholar 

  48. Chen, X. L. et al. High-quality sandwiched black phosphorus heterostructure and its quantum oscillations. Nat. Commun. 6, 7315 (2015).

    ADS  Article  Google Scholar 

  49. Golub, G. H., Heath, M. & Wahba, G. Generalized cross-validation as a method for choosing a good ridge parameter. Technometrics 21, 215–223 (1979).

    MathSciNet  Article  Google Scholar 

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Acknowledgements

F.X. and S.Y. acknowledge financial support by the Israel Ministry of Defense. The measurements at Yale also leveraged some instruments acquired through the Air Force Office of Scientific Research (AFOSR) Defense University Research Instrumentation Program (DURIP) with contract number FA9550-19-1-0109. D.N. would like to thank the Israel Science Foundation (grant number 1055/15) and the Directorate of Defense Research and Development at the Israel Ministry of Defense for the generous support of this research. Growth of hexagonal boron nitride crystals by K.W. and T.T. was supported by the Elemental Strategy Initiative conducted by the Japan Ministry of Education, Culture, Sports, Science and Technology (MEXT; grant number JPMXP0112101001), the Japan Society for the Promotion of Science Grants-in-Aid for Scientific Research (JSPS KAKENHI; grant number JP20H00354) and the Japan Science and Technology Agency Core Research for Evolutional Science and Technology (CREST; grant number JPMJCR15F3). We also thank C. Chen, A. Levi, R. Snitkoff, O. Nager, B. Deng and C. Ma, and our previous group member X. Chen for their help and support.

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Authors

Contributions

S.Y., D.N. and F.X. conceived the idea and initiated the project. S.Y. performed the experiments, collected all the data and developed the analysing computer programs. K.W. and T.T. synthesized the hBN crystals. All the authors discussed the results and commented on the manuscript.

Corresponding author

Correspondence to Fengnian Xia.

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Competing interests

S.Y., D.N. and F.X. are applying for an international patent based on the spectroscopy scheme and results presented in this work. The remaining authors declare no competing interests.

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Peer review information Nature Photonics thanks the anonymous reviewers for their contribution to the peer review of this work.

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Supplementary Information

Supplementary Figs. 1 and 2 and Table 1.

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Yuan, S., Naveh, D., Watanabe, K. et al. A wavelength-scale black phosphorus spectrometer. Nat. Photon. 15, 601–607 (2021). https://doi.org/10.1038/s41566-021-00787-x

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