This work proposes a simple and affordable technology for the manufacturing of a miniature end-face fiber-optic temperature sensor based on a Fabry–Perot interferometer formed from a transparent UV-curable resin. For the manufactured working prototype of the sensor, the sensitivity and operating temperature range were determined, and the methods for their enhancement were proposed. Due to its small size, the proposed type of sensor can be used in high-precision and minimally invasive temperature measurements, in biology for microscale sample monitoring, and in medicine during operations using high-power lasers. A microwave photonic method is proposed that enables the interrogation of the sensor without using an optical spectrum analyzer.

In this paper, an application of an artificial neural network algorithm is proposed to enhance the accuracy of temperature measurement using a fiber-optic sensor based on a Fabry–Perot interferometer (FPI). It is assumed that the interrogation of the FPI is carried out using an optical comb generator realizing a microwave photonic approach. Firstly, modelling of the reflection spectrum of a Fabry–Perot interferometer is implemented. Secondly, probing of the obtained spectrum using a comb-generator model is performed. The resulting electrical signal of the photodetector is processed and is used to create a sample for artificial neural network training aimed at temperature detection. It is demonstrated that the artificial neural network implementation can predict temperature variations with an accuracy equal to 0.018 °C in the range from −10 to +10 °C and 0.147 in the range from −15 to +15 °C.

A neural-network-assisted microwave-photonic method for interrogating fiber-optic sensing elements is proposed and numerically investigated. A multi-tone optical signal (MTOS), implemented as an optical frequency comb (OFC), is used to probe the spectral response of the sensing element. Square-law photodetection converts the interaction between the OFC lines and the sensing-element spectral response into beat components at difference frequencies, whose amplitudes form a multidimensional microwave-photonic response vector (MPRV). A unified numerical model based on transfer- and scattering-matrix formalisms was developed for fiber Bragg gratings (FBGs), Fabry–Pérot interferometers (FPIs), and combined FBG–FPI structures, including temperature-induced parameter variations and additive–multiplicative noise. A fully connected artificial neural network was trained to reconstruct the sensor state directly from the MPRV, with temperature used as the target measurand in the numerical study. It is shown that reconstruction accuracy is governed primarily by the distinguishability of sensor states in the MPRV space and by the practical one-to-one nature of the spectral position–MPRV mapping, rather than by neural-network complexity. Restricting the operating range to a practically one-to-one mapping region reduced the temperature reconstruction MAE from 0.198 to 0.021 °C at an SNR of 30 dB. Under increased noise, the proposed method exhibited slower accuracy degradation than spectral-peak, fixed-calibration, correlation-matching, and residual-minimization approaches, achieving the lowest MAE of 0.312 °C at an SNR of 15 dB. The method also demonstrated reduced sensitivity to OFC line-amplitude nonuniformity and residual optical-power drift. After training, the reconstruction time did not exceed 0.1 ms, indicating the potential of the proposed approach for high-speed fiber-optic sensor interrogation without direct optical-spectrum scanning.