Ben Sun, Kun Huang, Huijie Ma, Jianan Fang, Tingting Zheng, Ruiyang Qin, Yongyuan Chu, Hairun Guo, Yan Liang, Heping Zeng
{"title":"中红外单光子压缩光谱学","authors":"Ben Sun, Kun Huang, Huijie Ma, Jianan Fang, Tingting Zheng, Ruiyang Qin, Yongyuan Chu, Hairun Guo, Yan Liang, Heping Zeng","doi":"10.1002/lpor.202401099","DOIUrl":null,"url":null,"abstract":"Sensitive mid-infrared (MIR) spectroscopy plays an indispensable role in various photon-starved conditions. However, the detection sensitivity of conventional MIR spectrometers is severely limited by excessive noises of the involved infrared sensors, especially for multi-pixel arrays in parallel spectral acquisition. Here, an ultra-sensitive MIR single-pixel spectrometer is devised and implemented, which relies on high-fidelity spectral upconversion and wavelength-encoding compressive measurement. Specifically, a MIR nanophotonic supercontinuum from 3.1 to 3.9 µm is nonlinearly converted to the NIR band via synchronous chirped-pulse pumping, which facilitates both the precise spectral mapping and sensitive upconversion detection. The upconverted signal is then spatially dispersed onto a programmable digital micromirror device, before being registered by a single-element silicon detector. Consequently, the spectral information can be deciphered from the correlation between encoded patterns and recorded measurements, which results in a spectral resolution of 0.5 <span data-altimg=\"/cms/asset/863e4674-84af-43c9-9e3f-5d0ea6a1f068/lpor202401099-math-0001.png\"></span><mjx-container ctxtmenu_counter=\"1\" ctxtmenu_oldtabindex=\"1\" jax=\"CHTML\" role=\"application\" sre-explorer- style=\"font-size: 103%; position: relative;\" tabindex=\"0\"><mjx-math aria-hidden=\"true\" location=\"graphic/lpor202401099-math-0001.png\"><mjx-semantics><mjx-msup data-semantic-children=\"0,3\" data-semantic- data-semantic-role=\"unknown\" data-semantic-speech=\"c m Superscript negative 1\" data-semantic-type=\"superscript\"><mjx-mi data-semantic-font=\"normal\" data-semantic- data-semantic-parent=\"4\" data-semantic-role=\"unknown\" data-semantic-type=\"identifier\"><mjx-c></mjx-c><mjx-c></mjx-c></mjx-mi><mjx-script style=\"vertical-align: 0.363em;\"><mjx-mrow data-semantic-annotation=\"clearspeak:simple\" data-semantic-children=\"2\" data-semantic-content=\"1\" data-semantic- data-semantic-parent=\"4\" data-semantic-role=\"negative\" data-semantic-type=\"prefixop\" size=\"s\"><mjx-mo data-semantic- data-semantic-operator=\"prefixop,−\" data-semantic-parent=\"3\" data-semantic-role=\"subtraction\" data-semantic-type=\"operator\" rspace=\"1\"><mjx-c></mjx-c></mjx-mo><mjx-mn data-semantic-annotation=\"clearspeak:simple\" data-semantic-font=\"normal\" data-semantic- data-semantic-parent=\"3\" data-semantic-role=\"integer\" data-semantic-type=\"number\"><mjx-c></mjx-c></mjx-mn></mjx-mrow></mjx-script></mjx-msup></mjx-semantics></mjx-math><mjx-assistive-mml display=\"inline\" unselectable=\"on\"><math altimg=\"urn:x-wiley:18638880:media:lpor202401099:lpor202401099-math-0001\" display=\"inline\" location=\"graphic/lpor202401099-math-0001.png\" xmlns=\"http://www.w3.org/1998/Math/MathML\"><semantics><msup data-semantic-=\"\" data-semantic-children=\"0,3\" data-semantic-role=\"unknown\" data-semantic-speech=\"c m Superscript negative 1\" data-semantic-type=\"superscript\"><mi data-semantic-=\"\" data-semantic-font=\"normal\" data-semantic-parent=\"4\" data-semantic-role=\"unknown\" data-semantic-type=\"identifier\">cm</mi><mrow data-semantic-=\"\" data-semantic-annotation=\"clearspeak:simple\" data-semantic-children=\"2\" data-semantic-content=\"1\" data-semantic-parent=\"4\" data-semantic-role=\"negative\" data-semantic-type=\"prefixop\"><mo data-semantic-=\"\" data-semantic-operator=\"prefixop,−\" data-semantic-parent=\"3\" data-semantic-role=\"subtraction\" data-semantic-type=\"operator\">−</mo><mn data-semantic-=\"\" data-semantic-annotation=\"clearspeak:simple\" data-semantic-font=\"normal\" data-semantic-parent=\"3\" data-semantic-role=\"integer\" data-semantic-type=\"number\">1</mn></mrow></msup>${\\rm cm}^{-1}$</annotation></semantics></math></mjx-assistive-mml></mjx-container> under an illumination flux down to 0.01 photons nm<sup>–1</sup> pulse<sup>–1</sup>. Moreover, faithful reconstructions at sub-Nyquist sampling rates are demonstrated using the compressive sensing algorithm, which leads to a 95% reduction in data acquisition time. The presented single-pixel computational spectrometer features wavelength multiplexing, high throughput, and efficient sampling, which thus paves a new way for sensitive and fast spectroscopic analysis at the single-photon level.","PeriodicalId":204,"journal":{"name":"Laser & Photonics Reviews","volume":null,"pages":null},"PeriodicalIF":9.8000,"publicationDate":"2024-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Mid-Infrared Single-Photon Compressive Spectroscopy\",\"authors\":\"Ben Sun, Kun Huang, Huijie Ma, Jianan Fang, Tingting Zheng, Ruiyang Qin, Yongyuan Chu, Hairun Guo, Yan Liang, Heping Zeng\",\"doi\":\"10.1002/lpor.202401099\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Sensitive mid-infrared (MIR) spectroscopy plays an indispensable role in various photon-starved conditions. However, the detection sensitivity of conventional MIR spectrometers is severely limited by excessive noises of the involved infrared sensors, especially for multi-pixel arrays in parallel spectral acquisition. Here, an ultra-sensitive MIR single-pixel spectrometer is devised and implemented, which relies on high-fidelity spectral upconversion and wavelength-encoding compressive measurement. Specifically, a MIR nanophotonic supercontinuum from 3.1 to 3.9 µm is nonlinearly converted to the NIR band via synchronous chirped-pulse pumping, which facilitates both the precise spectral mapping and sensitive upconversion detection. The upconverted signal is then spatially dispersed onto a programmable digital micromirror device, before being registered by a single-element silicon detector. Consequently, the spectral information can be deciphered from the correlation between encoded patterns and recorded measurements, which results in a spectral resolution of 0.5 <span data-altimg=\\\"/cms/asset/863e4674-84af-43c9-9e3f-5d0ea6a1f068/lpor202401099-math-0001.png\\\"></span><mjx-container ctxtmenu_counter=\\\"1\\\" ctxtmenu_oldtabindex=\\\"1\\\" jax=\\\"CHTML\\\" role=\\\"application\\\" sre-explorer- style=\\\"font-size: 103%; position: relative;\\\" tabindex=\\\"0\\\"><mjx-math aria-hidden=\\\"true\\\" location=\\\"graphic/lpor202401099-math-0001.png\\\"><mjx-semantics><mjx-msup data-semantic-children=\\\"0,3\\\" data-semantic- data-semantic-role=\\\"unknown\\\" data-semantic-speech=\\\"c m Superscript negative 1\\\" data-semantic-type=\\\"superscript\\\"><mjx-mi data-semantic-font=\\\"normal\\\" data-semantic- data-semantic-parent=\\\"4\\\" data-semantic-role=\\\"unknown\\\" data-semantic-type=\\\"identifier\\\"><mjx-c></mjx-c><mjx-c></mjx-c></mjx-mi><mjx-script style=\\\"vertical-align: 0.363em;\\\"><mjx-mrow data-semantic-annotation=\\\"clearspeak:simple\\\" data-semantic-children=\\\"2\\\" data-semantic-content=\\\"1\\\" data-semantic- data-semantic-parent=\\\"4\\\" data-semantic-role=\\\"negative\\\" data-semantic-type=\\\"prefixop\\\" size=\\\"s\\\"><mjx-mo data-semantic- data-semantic-operator=\\\"prefixop,−\\\" data-semantic-parent=\\\"3\\\" data-semantic-role=\\\"subtraction\\\" data-semantic-type=\\\"operator\\\" rspace=\\\"1\\\"><mjx-c></mjx-c></mjx-mo><mjx-mn data-semantic-annotation=\\\"clearspeak:simple\\\" data-semantic-font=\\\"normal\\\" data-semantic- data-semantic-parent=\\\"3\\\" data-semantic-role=\\\"integer\\\" data-semantic-type=\\\"number\\\"><mjx-c></mjx-c></mjx-mn></mjx-mrow></mjx-script></mjx-msup></mjx-semantics></mjx-math><mjx-assistive-mml display=\\\"inline\\\" unselectable=\\\"on\\\"><math altimg=\\\"urn:x-wiley:18638880:media:lpor202401099:lpor202401099-math-0001\\\" display=\\\"inline\\\" location=\\\"graphic/lpor202401099-math-0001.png\\\" xmlns=\\\"http://www.w3.org/1998/Math/MathML\\\"><semantics><msup data-semantic-=\\\"\\\" data-semantic-children=\\\"0,3\\\" data-semantic-role=\\\"unknown\\\" data-semantic-speech=\\\"c m Superscript negative 1\\\" data-semantic-type=\\\"superscript\\\"><mi data-semantic-=\\\"\\\" data-semantic-font=\\\"normal\\\" data-semantic-parent=\\\"4\\\" data-semantic-role=\\\"unknown\\\" data-semantic-type=\\\"identifier\\\">cm</mi><mrow data-semantic-=\\\"\\\" data-semantic-annotation=\\\"clearspeak:simple\\\" data-semantic-children=\\\"2\\\" data-semantic-content=\\\"1\\\" data-semantic-parent=\\\"4\\\" data-semantic-role=\\\"negative\\\" data-semantic-type=\\\"prefixop\\\"><mo data-semantic-=\\\"\\\" data-semantic-operator=\\\"prefixop,−\\\" data-semantic-parent=\\\"3\\\" data-semantic-role=\\\"subtraction\\\" data-semantic-type=\\\"operator\\\">−</mo><mn data-semantic-=\\\"\\\" data-semantic-annotation=\\\"clearspeak:simple\\\" data-semantic-font=\\\"normal\\\" data-semantic-parent=\\\"3\\\" data-semantic-role=\\\"integer\\\" data-semantic-type=\\\"number\\\">1</mn></mrow></msup>${\\\\rm cm}^{-1}$</annotation></semantics></math></mjx-assistive-mml></mjx-container> under an illumination flux down to 0.01 photons nm<sup>–1</sup> pulse<sup>–1</sup>. 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Sensitive mid-infrared (MIR) spectroscopy plays an indispensable role in various photon-starved conditions. However, the detection sensitivity of conventional MIR spectrometers is severely limited by excessive noises of the involved infrared sensors, especially for multi-pixel arrays in parallel spectral acquisition. Here, an ultra-sensitive MIR single-pixel spectrometer is devised and implemented, which relies on high-fidelity spectral upconversion and wavelength-encoding compressive measurement. Specifically, a MIR nanophotonic supercontinuum from 3.1 to 3.9 µm is nonlinearly converted to the NIR band via synchronous chirped-pulse pumping, which facilitates both the precise spectral mapping and sensitive upconversion detection. The upconverted signal is then spatially dispersed onto a programmable digital micromirror device, before being registered by a single-element silicon detector. Consequently, the spectral information can be deciphered from the correlation between encoded patterns and recorded measurements, which results in a spectral resolution of 0.5 under an illumination flux down to 0.01 photons nm–1 pulse–1. Moreover, faithful reconstructions at sub-Nyquist sampling rates are demonstrated using the compressive sensing algorithm, which leads to a 95% reduction in data acquisition time. The presented single-pixel computational spectrometer features wavelength multiplexing, high throughput, and efficient sampling, which thus paves a new way for sensitive and fast spectroscopic analysis at the single-photon level.
期刊介绍:
Laser & Photonics Reviews is a reputable journal that publishes high-quality Reviews, original Research Articles, and Perspectives in the field of photonics and optics. It covers both theoretical and experimental aspects, including recent groundbreaking research, specific advancements, and innovative applications.
As evidence of its impact and recognition, Laser & Photonics Reviews boasts a remarkable 2022 Impact Factor of 11.0, according to the Journal Citation Reports from Clarivate Analytics (2023). Moreover, it holds impressive rankings in the InCites Journal Citation Reports: in 2021, it was ranked 6th out of 101 in the field of Optics, 15th out of 161 in Applied Physics, and 12th out of 69 in Condensed Matter Physics.
The journal uses the ISSN numbers 1863-8880 for print and 1863-8899 for online publications.