Multimode Optical Fiber Materials for Enhanced Mode Conversion and Holographic Reconstruction Using Machine Learning and Spatial Division Multiplexing

With the growing need for high-density data and real-time communication, multimode optical fibers (MMFs) have emerged as a vital technology due to their ability to transmit multiple optical modes simultaneously through spatial division multiplexing (SDM). However, issues such as mode coupling and dispersion continue to compromise signal clarity. This study presents a quantitative analysis of machine learning applications for enhancing mode conversion and holographic reconstruction in MMF systems. We demonstrate that a shallow artificial neural network can predict reconstruction accuracy, information transfer capacity, and adaptive system efficiency based on mode coupling and dispersion inputs, achieving prediction errors below 4% on case-study data. A convolutional neural network achieves 93.7% accuracy for mode separation, with a 62% reduction in mode error rate compared to conventional reconstruction methods. Hybrid physics-informed learning approaches incorporating energy conservation and phase stability constraints are explored, showing promise for improved generalization. Our results indicate that machine learning significantly enhances reconstruction precision and information transmission in MMFs, providing a foundation for next-generation communication and imaging systems. Despite certain training challenges, these models effectively process complex data, demonstrating their reliability and predictive capabilities.