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Machine learning assisted high-precision temperature sensor in a multimode microcavity
- Source :
- Results in Physics, Vol 62, Iss , Pp 107806- (2024)
- Publication Year :
- 2024
- Publisher :
- Elsevier, 2024.
-
Abstract
- Whispering gallery mode (WGM) microcavities are excellent platforms for ultra-sensitive sensing due to high-quality factor and small mode volume. However, the conventional sensing method by tracking single-mode changes is difficult to fully utilize the sensing information, which limits the measurement precision and dynamical range. Here, we demonstrate a high-precision temperature sensor based on the multimode sensing method in a packaged microbubble resonator (PMBR). Remarkably, a low-cost broadband spectrum source is used as probe light to provide more sensing modes for high-precision measurement. Empowered by a machine learning method, the multimode spectral information are fully utilized, and the true temperature is precisely readout with mean-squared error (MSE) of 0.0138. The detection limit is lower three times than single-mode sensing method, capable of reaching 0.117 °C. In addition, the correlation coefficient (R2) between predictions and truth is as high as 0.9996 within the measurement range of 25–45 °C. With the low-cost laser source and high detection precision, this work provides a new perspective for intelligent optical microcavity sensors and their engineering applications.
- Subjects :
- WGM microcavity
Multimode sensing
Temperature sensor
Machine Learning
Physics
QC1-999
Subjects
Details
- Language :
- English
- ISSN :
- 22113797
- Volume :
- 62
- Issue :
- 107806-
- Database :
- Directory of Open Access Journals
- Journal :
- Results in Physics
- Publication Type :
- Academic Journal
- Accession number :
- edsdoj.13945c7d342d983132f7686d181ec
- Document Type :
- article
- Full Text :
- https://doi.org/10.1016/j.rinp.2024.107806