AI-powered MMI fiber sensors for wide-range refractive index detection using neural networks algorithm

• Published in: Optical fiber technology Vol. 90; p. 104113
• Main Authors: Adilla Zaidi, Nurul Farah, Mohd Noor, Muhammad Yusof, Huda Saris, Nur Najahatul, Salim, Mohd Rashidi, Ambran, Sumiaty, Azizan, Azizul, Raja Ibrahim, Raja Kamarulzaman, Ahmad, Fauzan, Daud, Nurul Ashikin, Ali, Norazida, Nawawi, Norizan Mohamed, Yulianti, Ian, Peng, Gang-Ding
• Format: Journal Article
• Language: English
• Published: Elsevier Inc 01.03.2025
• Subjects: Artificial intelligence (AI); machine learning (ML); multimode interference (MMI); Neural Network (NN); No-core fiber (NCF); Refractive index fiber sensor; Regression; Artificial intelligence (AI); Regression; No-core fiber (NCF); machine learning (ML); Refractive index fiber sensor; multimode interference (MMI); Neural Network (NN)
• ISSN: 1068-5200
• DOI: 10.1016/j.yofte.2024.104113

This research presents an artificial intelligence (AI)-driven machine learning (ML) approach for accurately measuring refractive index (RI) values across both lower and higher regimes than the fiber material’s RI, using a simple single multimode interference (MMI) fiber sensor. The sensor configuration consists of a no-core fiber (NCF) segment between two single-mode fiber (SMF) sections. A Bilayer Neural Network (BNN) regression model is employed to predict both low refractive index (LRI) and high refractive index (HRI) regimes, achieving a broad dynamic measurement range from 1.3000 RIU to 1.3900 RIU for LRI regime and from 1.4600 RIU to 1.5500 RIU for HRI regime. The model demonstrates 99.7% accuracy and a low root mean square error (RMSE) of 0.0044, ensuring that predicted RI values closely match actual measurements without any RI ambiguity. Furthermore, the all-silica NCF structure is inherently resistant to temperature fluctuations, enabling its deployment in environments with varying temperatures without requiring additional temperature compensation mechanisms.