SpecCMamba–assisted fiber sensor based on cascaded 3D-printed fabry-perot interferometers toward high–accuracy refractive index prediction

We demonstrate a high-sensitivity fiber-optic sensor formed by cascading two Fabry-Perot (FP) interferometers (FP1 and FP2) for precise refractive index (RI) prediction. Both FP1 and FP2 are fabricated directly on the fiber tip via two-photon polymerization (2PP) based 3D printing, with an open-ended structure designed for operation in liquid environments. Meanwhile, the cascading FP2 with FP1 exploits the vernier effect, which amplifies the sensitivity of FP1, and experimental results show high sensitivity. The sensitivity of FP1 is 1143.78 nm/RIU (0.162 nm/(mg/mL)), while the vernier–effect of the sensor achieves a higher sensitivity of 3478.23 nm/RIU (0.494 nm/(mg/mL)), representing a 3.041–fold enhancement. A physics-guided data augmentation strategy is introduced to train SpecCMamba, and a hybrid deep learning model that integrates a convolutional neural network for local feature extraction with a Mamba encoder for global sequence modeling. The SpecCMamba enables precise RI prediction, effectively handling spectral overlap and non-linearity in the data. The model achieves a determination coefficient ( R 2 ) of 0.9996 and a Root Mean Square Error of 2.855 × 10 −5 . With its simple structure and high sensitivity, the proposed sensor assisted by the SpecCMamba offers significant potential for applications in highly precise refractive index prediction and related fields.