Simulation and Optimization of SNAP-Taper Coupling System in Displacement Sensing

Regulation and Liquid Sensing of Electromagnetically Induced Transparency-like Phenomena Implemented in a SNAP Microresonator

Optical microresonators supporting whispering-gallery modes (WGMs) have become a versatile platform for achieving electromagnetically induced transparency-like (EIT-like) phenomena. We theoretically and experimentally demonstrated the tunable coupled-mode induced transparency based on the surface nanoscale axial photonics (SNAP) microresonator. Single-EIT-like and double-EIT-like (DEIT-like) effects with one or more transparent windows are achieved due to dense mode families and tunable resonant frequencies. The experimental results can be well-fitted by the coupled mode theory. An automatically adjustable EIT-like effect is discovered by immersing the sensing region of the SNAP microresonator into an aqueous environment. The sharp lineshape and high slope of the transparent window allow us to achieve a liquid refractive index sensitivity of 2058.8 pm/RIU. Furthermore, we investigated a displacement sensing phenomenon by monitoring changes in the slope of the transparent window. We believe that the above results pave the way for multi-channel all-optical switching devices, multi-channel optical communications, and biochemical sensing processing.

Rapid and high-precision displacement sensing based on the multiple mode dip areas in a SNAP microresonator

Whispering gallery mode (WGM) microresonators offer significant potential for precise displacement measurement owing to their compact size, ultrahigh sensitivity, and rapid response. However, conventional WGM displacement sensors are prone to noise interference, resulting in accuracy loss, while the demodulation process for displacement often exhibits prolonged duration. To address these limitations, this study proposes a rapid and high-precision displacement sensing method based on the dip areas of multiple resonant modes in a surface nanoscale axial photonics microresonator. By employing a neural network to fit the nonlinear relationship between displacement and the areas of multiple resonant dips, we achieve displacement prediction with an accuracy better than 0.03 µm over a range of 200 µm. In comparison to alternative sensing approaches, this method exhibits resilience to temperature variations, and its sensing performance remains comparable to that in a noise-free environment as long as the signal-to-noise ratio is greater than 25 dB. Furthermore, the extraction of the dip area enables significantly enhanced speed in displacement measurement, providing an effective solution for achieving rapid and highly accurate displacement sensing.

Displacement sensing in a multimode SNAP microcavity by an artificial neural network

Benefiting from the coupling between the Surface Nanoscale Axial Photonics (SNAP) microcavity and the waveguide, i.e., influenced by their abrupt field overlap, multiple axial modes in the transmission spectrum form a functional relationship with the coupling position, thus enabling displacement sensing. However, this functional relationship is complex and nonlinear, which is difficult to be fitted using analytical methods. We introduce a back-propagation neural network (BPNN) to model this functional relationship. The numerical results show that the multimode sensing scheme has great potential for practical large-range, high-precision displacement sensing platforms compared with the single-mode sensing based on the whispering gallery mode (WGM) resonators.