Convolutional Neural Network-Enabled Optical Fiber SPR Sensors for RI Prediction

• Published in: IEEE sensors journal Vol. 25; no. 4; pp. 6371 - 6379
• Main Authors: Liao, Xiao-Xiao, Yang, Hong, Wu, Qiang, Liu, Juan, Hu, Yingying, Zhang, Yue, Liu, Wei-Qing, Fu, Yue, Pike, Andrew R., Liu, Bin
• Format: Journal Article
• Language: English
• Published: New York IEEE 15.02.2025; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: 1-D convolutional neural network (1D-CNN); Artificial intelligence; Artificial neural networks; Convolution; Convolutional neural networks; Demodulation; Feature extraction; Machine learning; Multimode-core-less multimode structure; Neural networks; Optical fiber networks; Optical fiber sensors; Optical fibers; Predictions; refractive index (RI) sensing; Refractivity; Sensor phenomena and characterization; Sensors; Spectrum analysis; Surface plasmon resonance; surface plasmonic resonance
• ISSN: 1530-437X; 1558-1748
• DOI: 10.1109/JSEN.2024.3523272

The advancement of artificial intelligence technology has led to the widespread adoption of deep learning techniques within spectral analysis over recent years. In this study, we introduce an advanced demodulation approach utilizing a 1-D convolutional neural network (1D-CNN) for feature extraction and the analysis of spectral signals from surface plasmon resonance (SPR) fiber refractive index (RI) sensors featuring a multimode-no-core-multimode (MNM) structure while simultaneously forecasting changes in RI due to environmental factors. Through segmentation-based predictive training on spectral signals, our approach achieves an average prediction accuracy exceeding 98%, even at low resolutions. Experimental findings demonstrate superior demodulation performance using our intelligent demodulation technique based on 1D-CNN compared to conventional methods. Furthermore, our method is adaptable across diverse and intricate structures enabling observation of parameter correlations spanning their entire range, thereby enhancing measurement capabilities within SPR sensing systems with significant potential applications.