High-performance and scalable on-chip digital Fourier transform spectroscopy
Name
s41467-018-06773-2.pdf
Description
Published version
Size
1.15 MB
Format
Adobe PDF
Checksum (MD5)
70e1d9edbbe9a8e66f3c0e2ab36ec8c7
Author(s)
Kita, Derek M
Miranda, Brando
Favela, David
Bono, David
Michon, Jérôme
Lin, Hongtao
Gu, Tian
Hu, Juejun
Date Issued
2018
Journal
Nature Communications
Publisher
Springer Science and Business Media LLC
Version
Final published version
Abstract
© 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.
MIT Department
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
Terms of Use
Creative Commons Attribution 4.0 International license
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1038/S41467-018-06773-2
