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Title: Trace-gas Spectroscopy of Methane on a Silicon Photonic Chip

Abstract

Recent advances in hybrid integrated silicon photonic (SiPh) technologies are enabling the migration of conventional free-space optical spectroscopic sensors onto a compact on-chip platform [1-3]. In addition to the small spatial footprint and power efficiency, we envision such sensors to be scalably manufactured using existing CMOS-compatible foundry processes, thus providing disruptive SWaP-C (size, weight, power, and cost) benefits in contrast to commercially available optical sensors. Initial demonstration of evanescent TDLAS (tunable diode laser absorption spectroscopy) of methane (CH4) on a passive SiPh waveguide has indicated minimum fractional absorption of (αL)min = 3.3×10-5 Hz-1/2, which is on-par with state-of-art open-path TDLAS sensor systems [4]. Given the general recent movement toward cleaner fuels, CH4 fugitive emissions monitoring is of significant interest given the extremely high radiative forcing potential [5]. For a nominal waveguide length of 30 cm with Γ = 25 % evanescent exposure, this corresponds to ~ 10 ppmv detection sensitivity at 1 s integration time, and further sensitivity enhancement is expected with even longer waveguides, as the laser RIN typically dominates our measurements at nominal waveguide lengths. Despite the excellent sensitivities for short-term integration periods, long-term measurements (> 10 s) are potentially limited on a silicon platform due to themore » high material thermo-optic coefficient, resulting in significant susceptibility of Fabry-Perot etalons to drift in the presence of even small (~ 1 mK) thermal fluctuations. To this end, customized spectral fitting algorithms have played a significant role in both fringe drift mitigation and peak detection fidelity (e.g. in the presence of a passing CH4 plume), which are crucial for enhancing long-term stability without the need for frequent sensor recalibration. A variety of spectral algorithms have been designed for this purpose, and details will be presented at the meeting.« less

Authors:
 [1];  [1];  [1];  [1];  [1];  [1];  [1];  [2];  [2];  [1]
  1. IBM, Yorktown Heights, NY (United States). Thomas J. Watson Research Center
  2. Princeton Univ., NJ (United States). Electrical Engineering Dept.
Publication Date:
Research Org.:
IBM, Yorktown Heights, NY (United States). Thomas J. Watson Research Center
Sponsoring Org.:
USDOE Advanced Research Projects Agency - Energy (ARPA-E)
Contributing Org.:
Princeton University
OSTI Identifier:
1380222
Report Number(s):
DOE-IBM-0000540-12
DOE Contract Number:  
AR0000540
Resource Type:
Conference
Resource Relation:
Conference: EMN Meeting on Photonics, Budapest, Hungary, September 2017.
Country of Publication:
United States
Language:
English
Subject:
03 NATURAL GAS

Citation Formats

Zhang, Eric, Xiong, Chi, Martin, Yves, Orcutt, Jason, Khater, Marwan, Schares, Laurent, Barwicz, Tymon, Teng, Cheyenne, Wysocki, Gerard, and Green, William. Trace-gas Spectroscopy of Methane on a Silicon Photonic Chip. United States: N. p., 2017. Web. doi:10.1364/CLEO_SI.2016.SF2H.1.
Zhang, Eric, Xiong, Chi, Martin, Yves, Orcutt, Jason, Khater, Marwan, Schares, Laurent, Barwicz, Tymon, Teng, Cheyenne, Wysocki, Gerard, & Green, William. Trace-gas Spectroscopy of Methane on a Silicon Photonic Chip. United States. doi:10.1364/CLEO_SI.2016.SF2H.1.
Zhang, Eric, Xiong, Chi, Martin, Yves, Orcutt, Jason, Khater, Marwan, Schares, Laurent, Barwicz, Tymon, Teng, Cheyenne, Wysocki, Gerard, and Green, William. Tue . "Trace-gas Spectroscopy of Methane on a Silicon Photonic Chip". United States. doi:10.1364/CLEO_SI.2016.SF2H.1. https://www.osti.gov/servlets/purl/1380222.
@article{osti_1380222,
title = {Trace-gas Spectroscopy of Methane on a Silicon Photonic Chip},
author = {Zhang, Eric and Xiong, Chi and Martin, Yves and Orcutt, Jason and Khater, Marwan and Schares, Laurent and Barwicz, Tymon and Teng, Cheyenne and Wysocki, Gerard and Green, William},
abstractNote = {Recent advances in hybrid integrated silicon photonic (SiPh) technologies are enabling the migration of conventional free-space optical spectroscopic sensors onto a compact on-chip platform [1-3]. In addition to the small spatial footprint and power efficiency, we envision such sensors to be scalably manufactured using existing CMOS-compatible foundry processes, thus providing disruptive SWaP-C (size, weight, power, and cost) benefits in contrast to commercially available optical sensors. Initial demonstration of evanescent TDLAS (tunable diode laser absorption spectroscopy) of methane (CH4) on a passive SiPh waveguide has indicated minimum fractional absorption of (αL)min = 3.3×10-5 Hz-1/2, which is on-par with state-of-art open-path TDLAS sensor systems [4]. Given the general recent movement toward cleaner fuels, CH4 fugitive emissions monitoring is of significant interest given the extremely high radiative forcing potential [5]. For a nominal waveguide length of 30 cm with Γ = 25 % evanescent exposure, this corresponds to ~ 10 ppmv detection sensitivity at 1 s integration time, and further sensitivity enhancement is expected with even longer waveguides, as the laser RIN typically dominates our measurements at nominal waveguide lengths. Despite the excellent sensitivities for short-term integration periods, long-term measurements (> 10 s) are potentially limited on a silicon platform due to the high material thermo-optic coefficient, resulting in significant susceptibility of Fabry-Perot etalons to drift in the presence of even small (~ 1 mK) thermal fluctuations. To this end, customized spectral fitting algorithms have played a significant role in both fringe drift mitigation and peak detection fidelity (e.g. in the presence of a passing CH4 plume), which are crucial for enhancing long-term stability without the need for frequent sensor recalibration. A variety of spectral algorithms have been designed for this purpose, and details will be presented at the meeting.},
doi = {10.1364/CLEO_SI.2016.SF2H.1},
journal = {},
number = ,
volume = ,
place = {United States},
year = {Tue Sep 05 00:00:00 EDT 2017},
month = {Tue Sep 05 00:00:00 EDT 2017}
}

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