Projects per year
Abstract
Miniaturized silicon photonics spectrometers have great potential for mass market applications like medicine and hazard detection. However, the performance of state-of-the-art silicon spectrometers is limited by fabrication imperfections and temperature variations. In this work, we present a fundamentally new strategy that combines machine learning algorithms and on-chip spatial heterodyne Fourier-transform spectroscopy to identify specific absorption features operated under a wide range of temperatures in the presence of fabrication imperfections. We experimentally show differentiation of four different input spectra with unknown temperature variations as large as 10 °C. This is about 100x increase in operational range, compared to state-of-the-art retrieval techniques.
Original language | English |
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Number of pages | 1 |
Publication status | Published - 6 Feb 2020 |
Event | SPIE Photonics West OPTO - San Francisco, United States Duration: 1 Feb 2020 → 6 Feb 2020 https://spie.org/PWO/conferencedetails/smart-photonics-and-oeics |
Conference
Conference | SPIE Photonics West OPTO |
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Country/Territory | United States |
City | San Francisco |
Period | 1/02/20 → 6/02/20 |
Internet address |
Keywords
- silicon
- spectrometers
- machine learning
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Dive into the research topics of 'Smart on-chip Fourier-transform spectrometers harnessing machine learning algorithms'. Together they form a unique fingerprint.Projects
- 1 Finished
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SENSIBLE: SENSors and Intelligence in BuiLt Environment (SENSIBLE) MSCA RISE
Stankovic, L. (Principal Investigator), Glesk, I. (Co-investigator), Gleskova, H. (Co-investigator) & Stankovic, V. (Co-investigator)
European Commission - Horizon Europe + H2020
1/01/17 → 31/12/20
Project: Research