ANN-PSO Hybrid ML-Optimization of a Hollow-Disk Resonator-Based Photonic Crystal Optical Sensor for HeLa Cell Tumor Detection

In this study, we propose a novel optical sensor architecture based on two-dimensional photonic crystals for the early detection of cervical cancer (HeLa). The structure consists of a central hollow-disk micro-cavity designed to accommodate biosamples, surrounded by a periodic array of GaAs rods. The detection principle relies on variations in the biosample refractive index, inducing a spectral shift in the resonance. To overcome the limitations of conventional 2D-FDTD method parametric sweeps, an artificial intelligence framework was developed to optimize the geometric parameters of the proposed photonic crystal optical sensor. First, a Random Forest algorithm was employed to identify promising regions of the geometric design space. Next, a multilayer artificial neural network (ANN-MLP) was trained as a high-fidelity surrogate model (R2 = 98.58%) and coupled with a Particle Swarm Optimization (PSO) algorithm to determine the optimal structural configuration. The optimized sensor geometry subsequently achieved an average sensitivity of 5512.91 nm/RIU, a quality factor of 6139.15 and a detection limit of 5.64×10−5 RIU, demonstrating the effectiveness of the proposed AI-assisted design strategy. The optimized design reduces classical performance trade-offs and exhibits high tolerance to nanometric fabrication deviations below ±20 nm.