Decision Tree Regression Supervised Machine Learning Assisted Large Dynamic Range Refractive Index Detection Using MMI Coreless Multimode Fiber Sensor

A supervised machine learning (ML) algorithm is proposed for measuring refractive index (RI) values both below and above the RI of the fiber material using a multimode interference (MMI) fiber sensor. The sensor is constructed by splicing a coreless multimode fiber (CMF) segment between two single-mode fiber (SMF) leads. Measurement of low and high RI regimes is accomplished through the Decision Tree (DT) regression algorithm. The trained model algorithm demonstrates a wide dynamic range in RI measurement, covering the ranges of 1.30 to 1.39 (low RI regime) and 1.46 to 1.55 (high RI regime) without any RI ambiguity, achieving model accuracy of 99.77%. The guided and leaky modes mechanisms within the all-silica-based structure of the CMF are fundamentally insensitive to temperature, making it highly practical for deployment in conditions with varying temperatures without the need for any compensating scheme.