Artificial Intelligence for Precision Design of Evanescent Field Sensors on SOI Platforms for Brain Cancer Applications

This chapter explores the transformative role of artificial intelligence (AI) in the design and optimization of evanescent field sensors using slot waveguides on silicon-on-insulator platforms, specifically for brain cancer detection. The integration of physics-informed neural networks and inverse design techniques enables highly efficient and adaptive sensor configurations. Structural enhancements such as uniform strips within slot waveguides significantly improve the evanescent field ratio and reduce propagation losses. The chapter evaluates AI's impact on reducing computational time, increasing real-time adaptability, and enhancing signal detection through anomaly detection and self-tuning algorithms. Experimental results show optimized propagation loss and field ratios across different structural variations. Future directions include the deployment of AI-optimized reconfigurable sensors and scalable fabrication methods for smart medical diagnostics. By merging nanophotonics and machine learning, the chapter envisions a path toward intelligent, efficient, and sustainable photonic systems for precision medicine.