Robust displacement sensing based on the resonance spectrum normalization using a bat-shaped SNAP microcavity

Surface nanoscale axial photonics (SNAP) microcavities exhibit a regular transmission spectrum and encompass multiple axial modes, making them highly relevant for precise and wide-range displacement sensing applications. However, conventional SNAP microcavity shapes, such as parabolic and Gaussian curves, demonstrate limitations in handling noise interference from external environments, compromising displacement sensing accuracy. In this study, we propose a robust displacement sensing approach based on the bat-shaped SNAP microcavity. This unique profile supports a uniform first-order axial field mode. By utilizing the first-order axial mode as a reference, we apply Resonance Spectra Normalization (RSN) to standardize the resonance spectrum, reducing the impact of external perturbations. Extensive simulations validate the effectiveness of this technique. When coupling parameters deviate by up to 8%, our sensing method achieves a prediction error confined within a 4 μm range, compared to 14 μm in conventional displacement sensing solutions. This advancement enhances the sensor’s immunity to environmental noise, potentially revolutionizing microcavity displacement sensing, particularly in challenging environments beyond controlled cleanrooms.