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arXiv · 2004.08696

Photonic Temperature and Wavelength Metrology by Spectral Pattern Recognition

Abstract

Spectral pattern recognition is used to measure temperature and generate calibrated wavelength/frequency combs using a single silicon waveguide ring resonator. The ring generates two incommensurate interleaving TE and TM spectral combs that shift independently with temperature to create a spectral pattern that is unique at every temperature. Following an initial calibration, the ring temperature can be determined by recognizing the spectral resonance pattern, and as a consequence the wavelength of every resonance is also known. Two methods of pattern based temperature retrieval are presented. In the first method, the ring is locked to a previously determined temperature set-point defined by the coincidence of only two specific TE and TM cavity modes. Based on a prior calibration at the set-point, the ring temperature and hence all resonance wavelengths are then known and the resulting comb can be used as a wavelength calibration reference. In this configuration, all reference comb wavelengths have been reproduced within a 5 pm accuracy across an 80 nm range by using an on-chip micro-heater to tune the ring. For more general photonic thermometry, a spectral correlation algorithm is developed to recognize a resonance pattern across a 30 nm wide spectral window and thereby determine ring temperature continuously to 50 mK accuracy. The correlation method is extended to simultaneously determine temperature and to identify and correct for wavelength calibration errors in the interrogating light source. The temperature and comb wavelength accuracy is limited primarily by the linewidth of the ring resonances, with accuracy and resolution scaling with the ring quality factor.

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Siegfried Janz, Ross Cheriton, Dan-Xia Xu, Aadam Densmore, Sergey Dedyulin, Andrew Todd, Jens Schmid, Pavel Cheben, Martin Vachon, Mohsen Kamandar Dezfouli, Daniele Melati. 2020-04-18. Photonic Temperature and Wavelength Metrology by Spectral Pattern Recognition. https://doi.org/10.1364/oe.394642

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