Design of surface plasmon resonance sensor enhanced by graphene deposition for breast cancer detection

This paper presents a systematic design procedure for optimizing a graphene-enhanced surface plasmon resonance (SPR) sensor for breast cancer detection. The study establishes a comprehensive methodology to determine the optimal sensor structure by evaluating multiple performance parameters through numerical simulations. Using a trapezoidal prism-based biochip, we develop a weighted optimization approach that balances sensitivity ( S n s ) and quality factor ( Q ) across three metal films (Ag, Au, Cu) and various graphene layer configurations. The design procedure incorporates tissue-specific refractive index modeling and employs the multilayer matrix method to simulate SPR responses. Our optimization framework identifies distinct optimal configurations: 6 graphene layers for Ag (achieving 5100 nm/RIU sensitivity), 1 layer for Au (optimal χ = 27.00 RIU −1 ), and 8 layers for Cu (77% Q enhancement). The procedure reveals that while Ag offers maximum sensitivity (5200 nm/RIU for carcinoma), Au provides the best overall performance with χ = 39.92 RIU −1 . This work proposes a replicable design methodology for developing SPR biosensors, demonstrating particular effectiveness for cancer detection applications while establishing general principles for performance optimization in plasmonic sensing systems. • Graphene layers enhance breast cancer detection in plasmonic sensors. • Weighted design method optimizes sensitivity of SPR sensors. • Silver achieves highest sensitivity; gold exhibits the highest quality factor. • Optimal graphene layers: six for Ag, one for Au, eight for Cu films. • Framework provides a replicable route for next-generation biosensor design.