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. (C) 2022 Optica Publishing Group under the terms of the Optica Open Access Publishing Agreement