Computational Modeling and 1D-CNN-Assisted Optimization of a Cu/ZnO/Si/MXene Multilayer SPR Biosensor for High-Sensitivity Multi-Analyte Detection

Cancer biomarkers, glucose molecules, and low-refractive-index analytes require sensitive and reliable optical sensing platforms. This study proposes a structurally optimized surface plasmon resonance (SPR) biosensor developed using COMSOL Multiphysics, where multilayer structural parameters are systematically varied to enhance electromagnetic-field confinement and resonance tuning. A onedimensional convolutional neural network (1D-CNN) is integrated to predict sensor performance under varying structural conditions. The optimized sensor demonstrates stable resonance behavior across a refractive-index range of 1.36–1.401 RIU, achieving a maximum angular sensitivity of 480°/RIU, a peak figure of merit (FOM) of 93 RIU -1 , and quality factors ranging from 10.301 to 11.370. Strong plasmonic coupling occurs at 79°, confirming enhanced light–matter interaction at the sensor–analyte interface. A highly linear resonance-angle response is obtained with R 2 > 0.98 and minimal prediction error. Overall, the proposed platform offers high sensitivity, stable performance, and machine-learningassisted optimization for diverse analyte detection.