On-chip Fourier-transform spectrometers and machine learning : a new route to smart photonic sensors

Tools

Herrero-Bermello, Alaine and Li, Jiangfeng and Khazaei, Mohammad and Grinberg, Yuri and Vellasco, Aitor V. and Vachon, Martin and Cheben, Pavel and Stankovic, Lina and Stankovic, Vladimir and Xu, Dan-Xia and Schmid, Jens H. and Alonso-Ramos, Carlos (2019) On-chip Fourier-transform spectrometers and machine learning : a new route to smart photonic sensors. Optics Letters, 44 (23). pp. 5840-5843. ISSN 0146-9592 (https://doi.org/10.1364/OL.44.005840)

[thumbnail of Herrero-Bermello-etal-OL2019-On-chip-Fourier-transform-spectrometers-and-machine-learning]
Preview
 Text. Filename: Herrero_Bermello_etal_OL2019_On_chip_Fourier_transform_spectrometers_and_machine_learning.pdf
Accepted Author Manuscript
Download (1MB)| Preview

Abstract

Miniaturized silicon photonics spectrometers capable of detecting specific absorption features have great potential for mass market applications in medicine, environmental monitoring, and hazard detection. However, state-of-the-art silicon spectrometers are limited by fabrication imperfections and environmental conditions, especially temperature variations, since uncontrolled temperature drifts of only 0.1 °C distort the retrieved spectrum precluding the detection and classification of the absorption features. Here, we present a new strategy that exploits the robustness of machine learning algorithms to signal imperfections, enabling recognition of specific absorption features in a wide range of environmental conditions. We combine on-chip spatial heterodyne Fourier-transform spectrometers and supervised learning to classify different input spectra in the presence of fabrication errors, without temperature stabilization or monitoring. We experimentally show differentiation of four different input spectra under an uncontrolled 10 °C range of temperatures, about 100x increase in operational range, with a success rate up to 82.5% using state-of-the-art support vector machines and artificial neural networks.

ORCID iDs

Herrero-Bermello, Alaine, Li, Jiangfeng, Khazaei, Mohammad, Grinberg, Yuri, Vellasco, Aitor V., Vachon, Martin, Cheben, Pavel, Stankovic, Lina ORCID logoORCID: https://orcid.org/0000-0002-8112-1976, Stankovic, Vladimir ORCID logoORCID: https://orcid.org/0000-0002-1075-2420, Xu, Dan-Xia, Schmid, Jens H. and Alonso-Ramos, Carlos;
  • Item type:Article
    ID code:70439
    Dates:
    Date
    Event
    1 December 2019
    Published
    28 November 2019
    Published Online
    4 November 2019
    Accepted
    27 June 2019
    Submitted
    Notes:© 2019 Optical Society of America. One print or electronic copy may be made for personal use only. Systematic reproduction and distribution, duplication of any material in this paper for a fee or for commercial purposes, or modifications of the content of this paper are prohibited. Alaine Herrero-Bermello, Jiangfeng Li, Mohammad Khazaei, Yuri Grinberg, Aitor V. Velasco, Martin Vachon, Pavel Cheben, Lina Stankovic, Vladimir Stankovic, Dan-Xia Xu, Jens H. Schmid, and Carlos Alonso-Ramos, "On-chip Fourier-transform spectrometers and machine learning: a new route to smart photonic sensors," Opt. Lett. 44, 5840-5843 (2019)
    Subjects:Science > Physics
    Department:Faculty of Engineering > Electronic and Electrical Engineering
    Depositing user: Pure Administrator
    Date deposited:06 Nov 2019 13:01
    Last modified:12 Dec 2024 08:53
    Related URLs:
    URI:https://strathprints.strath.ac.uk/id/eprint/70439
CORE (COnnecting REpositories)