University of Illinois Urbana-Champaign

UTILIZING FPGA TECHNOLOGY FOR REAL-TIME SPECTROSCOPIC ANALYSIS IN CAVITY RING-DOWN COMB SPECTROSCOPY WITH MACHINE LEARNING-ASSISTED COMB LINE ALIGNMENT

Huang, Yi-Jan

Content Files
7343.pdf
WD09_1.pptx

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https://hdl.handle.net/2142/122715

Description

Title
UTILIZING FPGA TECHNOLOGY FOR REAL-TIME SPECTROSCOPIC ANALYSIS IN CAVITY RING-DOWN COMB SPECTROSCOPY WITH MACHINE LEARNING-ASSISTED COMB LINE ALIGNMENT
Author(s)
Huang, Yi-Jan
Contributor(s)
  • Chen, Tzu-Ling
  • Okumura, Mitchio
  • Bagheri, Mahmood
  • Meyer, Jerry R
  • Frez, Clifford
  • Vurgaftman, Igor
  • Canedy, Chadwick L
  • Ober, Douglas
  • Sterczewski, Lukasz A.
  • Markus, Charles R.
Issue Date
2023-06-21
Keyword(s)
Instrument/Technique Demonstration
Abstract
In this work, we propose a novel approach for spectroscopic analysis in Cavity Ring-Down Comb Vernier Spectroscopy (CRDCVS) by exploiting FPGA architecture and an Arm-based embedded Linux system. This approach takes advantage of the high-speed data processing and acquisition capabilities of FPGA. In the Vernier configuration, a mode-resolved comb spectrum should align non-equidistant comb peaks in different scans and fitting the ring down time for each comb line. Especially a significant challenge in spectroscopic analysis is presented when the sample accumulation is required for improved sensitivity or temporal resolution. To address this challenge, we developed a machine-learning framework to predict comb line appearance in the operating platform. This enables ring-down-time accumulation for different comb peaks in CRDCVS. We demonstrated the proof-of-principle performance of the developed system with CRDCS measurements of toluene using a 3.3um chip-scale Interband Cascade Laser (ICL) comb. We will discuss the overall performance and the potential to advance the field of CRDCVS by improving measurement accuracy and reliability.
Publisher
International Symposium on Molecular Spectroscopy
Type of Resource
Text
Genre of Resource
Conference Paper / Presentation
Language
eng
Handle URL
https://hdl.handle.net/2142/122715
DOI
https://doi.org/10.15278/isms.2023.7343

Owning Collections

Abstracts & Presentations - 2023 International Symposium on Molecular Spectroscopy PRIMARY