Shaping photonic crystal fibers: geometric optimization for SPR sensor performance

Abstract This work integrates computational simulations with a hybrid machine learning framework to investigate the nonlinear relationships between plasmonic layer geometry, refractive index variations, and spectral response in a photonic crystal fiber (PCF) surface plasmon resonance (SPR) sensor. The proposed approach achieves reliable detection of small refrative index chances from a simple yet optimized PCF SPR sensing structure, reaching competitive sensitivity levels in the refractive index range of 1.33–1.39. Accurate predictions were obtained with $$R^{2}> 0.99$$ and minimal error ( $$\epsilon < 0.1$$ ). A central contribution of this work is the simultaneous optimization of multiple optical metrics. Beyond maximizing wavelength sensitivity, the methodology balances sensitivity, figure of merit, Q-factor, and FWHM. This multiobjective strategy enables precise tailoring of the plasmonic layer geometry, producing sharp resonances, high-quality factors, and robust performance. Overall, the results demonstrate how plasmonic engineering in photonic crystal fibers can drive high-performance SPR sensing platforms. The methodology provides valuable insights into the geometry–plasmonics interplay while opening avenues for practical implementations in biochemical detection, environmental monitoring, and chemical sensing.