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Saetchnikov A V, Tcherniavskaia E A, Saetchnikov V A, Ostendorf A. Deep-learning powered whispering gallery mode sensor based on multiplexed imaging at fixed frequency. Opto-Electron Adv 3, 200048 (2020). doi: 10.29026/oea.2020.200048
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Original Article Open Access
Deep-learning powered whispering gallery mode sensor based on multiplexed imaging at fixed frequency
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Abstract
During the last decades the whispering gallery mode based sensors have become a prominent solution for label-free sensing of various physical and chemical parameters. At the same time, the widespread utilization of the approach is hindered by the restricted applicability of the known configurations for ambient variations quantification outside the laboratory conditions and their low affordability, where necessity on the spectrally-resolved data collection is among the main limiting factors. In this paper we demonstrate the first realization of an affordable whispering gallery mode sensor powered by deep learning and multi-resonator imaging at a fixed frequency. It has been shown that the approach enables refractive index unit (RIU) prediction with an absolute error at 3×10-6 level for dynamic range of the RIU variations from 0 to 2×10-3 with temporal resolution of several milliseconds and instrument-driven detection limit of 3×10-5. High sensing accuracy together with instrumental affordability and production simplicity places the reported detector among the most cost-effective realizations of the whispering gallery mode approach. The proposed solution is expected to have a great impact on the shift of the whole sensing paradigm away from the model-based and to the flexible self-learning solutions.-
Keywords:
- optical microresonator /
- sensing /
- machine learning /
- whispering gallery mode /
- multiplexing
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References
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Saetchnikov A V, Tcherniavskaia E A, Saetchnikov V A, Ostendorf A. Deep-learning powered whispering gallery mode sensor based on multiplexed imaging at fixed frequency. Opto-Electron Adv 3, 200048 (2020). doi: 10.29026/oea.2020.200048
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Figure 1.
Overview of the instrument configuration: 1-laser diode; 2-collimation lens; 3-camera; 4-beam dump; 5-right angle optical prism; 6-adhesive thin layer; 7-microresonator.
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Figure 2.
Overview of the WGM signal.
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Figure 3.
Comparison of the experimental data collected in the laser frequency sweeping and the fixed frequency schemes for the same sensor sample under changing ambient refractive index.
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Figure 4.
Sensor response on the refractive index changes.
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Figure 5.
(a) Distribution of the absolute error values between the measured refractive indexes and the values predicted with different processing approaches: deep neural network (dNN), linear regression (LR), random forest (RF), general regression neural network (GRNN), gradient boosting (GB), and support vector regression (SVR). (b) Statistics on the performance of the refractive index prediction with dNN approach with different combinations of weights optimization methods (Adam, RMSprop, Nadam, Adagrad, and Adadelta) and activation functions (tanh, sigmoid, relu, selu, linear, and softplus).
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Figure 6.
Statistics on the refractive index prediction accuracy represented as absolute error values for different dNN configurations with varying number of neurons (N) in the input layer (n = 16, 32, 48, 56, 64, 80, 96, 112, 128, 256, 512, 1024) and hidden layers (L) number (3, 4, 5, 6) with 2n neurons.
