High-performance and scalable on-chip digital Fourier transform spectroscopy
© 2018, The Author(s). On-chip spectrometers have the potential to offer dramatic size, weight, and power advantages over conventional benchtop instruments for many applications such as spectroscopic sensing, optical network performance monitoring, hyperspectral imaging, and radio-frequency spectrum...
| Main Authors: | , , , , , , , |
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| Other Authors: | |
| Format: | Article |
| Language: | English |
| Published: |
Springer Science and Business Media LLC
2021
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| Online Access: | https://hdl.handle.net/1721.1/136373 |
| _version_ | 1826211255420977152 |
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| author | Kita, Derek M Miranda, Brando Favela, David Bono, David Michon, Jérôme Lin, Hongtao Gu, Tian Hu, Juejun |
| author2 | Massachusetts Institute of Technology. Department of Materials Science and Engineering |
| author_facet | Massachusetts Institute of Technology. Department of Materials Science and Engineering Kita, Derek M Miranda, Brando Favela, David Bono, David Michon, Jérôme Lin, Hongtao Gu, Tian Hu, Juejun |
| author_sort | Kita, Derek M |
| collection | MIT |
| description | © 2018, The Author(s). On-chip spectrometers have the potential to offer dramatic size, weight, and power advantages over conventional benchtop instruments for many applications such as spectroscopic sensing, optical network performance monitoring, hyperspectral imaging, and radio-frequency spectrum analysis. Existing on-chip spectrometer designs, however, are limited in spectral channel count and signal-to-noise ratio. Here we demonstrate a transformative on-chip digital Fourier transform spectrometer that acquires high-resolution spectra via time-domain modulation of a reconfigurable Mach-Zehnder interferometer. The device, fabricated and packaged using industry-standard silicon photonics technology, claims the multiplex advantage to dramatically boost the signal-to-noise ratio and unprecedented scalability capable of addressing exponentially increasing numbers of spectral channels. We further explore and implement machine learning regularization techniques to spectrum reconstruction. Using an ‘elastic-D1’ regularized regression method that we develop, we achieved significant noise suppression for both broad (>600 GHz) and narrow (<25 GHz) spectral features, as well as spectral resolution enhancement beyond the classical Rayleigh criterion. |
| first_indexed | 2024-09-23T15:03:04Z |
| format | Article |
| id | mit-1721.1/136373 |
| institution | Massachusetts Institute of Technology |
| language | English |
| last_indexed | 2024-09-23T15:03:04Z |
| publishDate | 2021 |
| publisher | Springer Science and Business Media LLC |
| record_format | dspace |
| spelling | mit-1721.1/1363732023-03-01T20:47:29Z High-performance and scalable on-chip digital Fourier transform spectroscopy Kita, Derek M Miranda, Brando Favela, David Bono, David Michon, Jérôme Lin, Hongtao Gu, Tian Hu, Juejun Massachusetts Institute of Technology. Department of Materials Science and Engineering MIT Materials Research Laboratory Center for Brains, Minds, and Machines Massachusetts Institute of Technology. Department of Mechanical Engineering © 2018, The Author(s). On-chip spectrometers have the potential to offer dramatic size, weight, and power advantages over conventional benchtop instruments for many applications such as spectroscopic sensing, optical network performance monitoring, hyperspectral imaging, and radio-frequency spectrum analysis. Existing on-chip spectrometer designs, however, are limited in spectral channel count and signal-to-noise ratio. Here we demonstrate a transformative on-chip digital Fourier transform spectrometer that acquires high-resolution spectra via time-domain modulation of a reconfigurable Mach-Zehnder interferometer. The device, fabricated and packaged using industry-standard silicon photonics technology, claims the multiplex advantage to dramatically boost the signal-to-noise ratio and unprecedented scalability capable of addressing exponentially increasing numbers of spectral channels. We further explore and implement machine learning regularization techniques to spectrum reconstruction. Using an ‘elastic-D1’ regularized regression method that we develop, we achieved significant noise suppression for both broad (>600 GHz) and narrow (<25 GHz) spectral features, as well as spectral resolution enhancement beyond the classical Rayleigh criterion. 2021-10-27T20:35:05Z 2021-10-27T20:35:05Z 2018 2019-09-20T16:37:55Z Article http://purl.org/eprint/type/JournalArticle https://hdl.handle.net/1721.1/136373 en 10.1038/S41467-018-06773-2 Nature Communications Creative Commons Attribution 4.0 International license https://creativecommons.org/licenses/by/4.0/ application/pdf Springer Science and Business Media LLC Nature |
| spellingShingle | Kita, Derek M Miranda, Brando Favela, David Bono, David Michon, Jérôme Lin, Hongtao Gu, Tian Hu, Juejun High-performance and scalable on-chip digital Fourier transform spectroscopy |
| title | High-performance and scalable on-chip digital Fourier transform spectroscopy |
| title_full | High-performance and scalable on-chip digital Fourier transform spectroscopy |
| title_fullStr | High-performance and scalable on-chip digital Fourier transform spectroscopy |
| title_full_unstemmed | High-performance and scalable on-chip digital Fourier transform spectroscopy |
| title_short | High-performance and scalable on-chip digital Fourier transform spectroscopy |
| title_sort | high performance and scalable on chip digital fourier transform spectroscopy |
| url | https://hdl.handle.net/1721.1/136373 |
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