Measuring device and method based on PCF-SPR refractive index sensing

Abstract

The invention provides a measuring device and method based on PCF-SPR refractive index sensing, and the device comprises an optical transmitting assembly, a sensing main body, an optical receiving assembly and a prediction module, the optical transmitting assembly is used for transmitting an optical signal and adjusting the polarization state of the optical signal; the sensing main body is connected with the optical emission assembly, the sensing main body is of a photonic crystal fiber structure and is provided with double layers of air holes, an Au / TiO2 composite film layer is deposited in a groove channel of the sensing main body, and the Au / TiO2 composite film layer interacts with an analyte to excite a surface plasma resonance effect so as to modulate an optical signal; the optical receiving assembly is connected with the sensing main body, and the optical receiving assembly is used for receiving the modulated optical signal so as to obtain a fundamental mode loss spectrum according to the modulated optical signal; the prediction module is connected with the optical receiving assembly, and the prediction module is used for rapidly predicting the sensor performance under different structural parameters; therefore, high-precision and high-efficiency refractive index measurement is realized.

Description

Technical Field

[0001] This invention relates to the field of optical sensing technology, and in particular to a measurement device and a measurement method based on PCF-SPR refractive index sensing. Background Technology

[0002] In related technologies, refractive index, as a fundamental physical quantity of matter, has key applications in environmental monitoring, chemical and biological sensing, and industrial process control. Fiber optic sensors have gained widespread attention in the field of refractive index measurement due to their resistance to electromagnetic interference, strong corrosion resistance, compact size, and good biocompatibility. Currently, various fiber optic sensor technologies have been proposed, such as fiber Bragg gratings, Fabry-Perot cavities, and Mach-Zehnder interferometers.

[0003] Among them, fiber optic sensors based on the surface plasmon resonance (SPR) effect have become an important technology for high-precision refractive index measurement due to their high sensitivity to fluctuations in the refractive index of the surrounding environment, and are widely used in fields such as biomedical detection, environmental monitoring, and energy analysis. Compared with traditional fiber optic SPR sensors, photonic crystal fibers (PCFs), due to their flexible structural design and excellent optical field control capabilities, can enhance the interaction between the optical field and the analyte by optimizing the air hole structure and channel design, thereby improving the sensing sensitivity. Therefore, PCF-SPR refractive index sensors have become a current research hotspot.

[0004] However, despite the high sensitivity of PCF-SPR sensors in refractive index measurements, existing designs still face the following challenges: the complex geometry of PCF fibers makes determining the optimal structural parameters for a given optical performance specification a difficult problem. Existing design methods mainly rely on numerical simulations (such as the finite element method). While these methods can provide high-precision predictions, they suffer from low optimization efficiency in high-dimensional parameter spaces, consume significant computational resources, and require considerable time for design and optimization. Furthermore, traditional iterative-trial-and-error methods struggle to achieve global optimization in complex parameter spaces, severely limiting sensor performance improvement and design efficiency. To address these issues, artificial neural networks (ANNs) have been increasingly applied in photonics in recent years due to their powerful nonlinear fitting capabilities and high-dimensional parameter optimization potential. By learning from large amounts of simulation or experimental data, ANNs can construct mapping models between structural parameters and optical responses, significantly reducing reliance on repetitive numerical simulations and accelerating response prediction. However, existing methods still fall short in terms of computational efficiency and prediction accuracy, failing to meet the demands for rapid optimization in complex parameter spaces. Therefore, a new method that can significantly improve optimization efficiency while maintaining prediction accuracy is urgently needed. Summary of the Invention

[0005] The present invention aims to at least partially solve one of the technical problems in the aforementioned technologies. Therefore, the first objective of the present invention is to propose a measurement device based on PCF-SPR refractive index sensing. This device improves sensing performance by designing an Au / TiO2 composite thin film-enhanced grooved PCF structure, constructs an ANN model to achieve rapid prediction of the sensor's loss spectrum and amplitude sensitivity, reduces design costs, and achieves high-precision, high-efficiency refractive index measurement.

[0006] The second objective of this invention is to propose a measurement method based on PCF-SPR refractive index sensing.

[0007] To achieve the above objectives, a first aspect of the present invention provides a measurement device based on PCF-SPR refractive index sensing, comprising: an optical emitting component for emitting an optical signal and adjusting the polarization state of the optical signal; a sensing body connected to the optical emitting component, the sensing body being a photonic crystal fiber structure with double-layer air holes, wherein an Au / TiO2 composite thin film layer is deposited in its groove channel, the Au / TiO2 composite thin film layer interacting with the analyte to excite a surface plasmon resonance effect to modulate the optical signal; and an optical receiving component, wherein the optical signal is received... An optical receiving component is connected to the sensing body. The optical receiving component is used to receive the modulated optical signal to obtain the real-time loss spectrum based on the modulated optical signal. A prediction module is connected to the optical receiving component. The prediction module is trained using a training dataset and constructs a functional relationship between refractive index and loss spectrum based on the trained prediction model. The refractive index of the analyte is obtained based on the real-time loss spectrum and the functional relationship between refractive index and loss spectrum. The training dataset includes the wavelength of the light source, the refractive index of the analyte, the thickness of the titanium dioxide film and its higher powers, and the corresponding loss spectrum.

[0008] The measurement device based on PCF-SPR refractive index sensing proposed in this invention has the following advantages: by combining PCF-SPR sensing technology with ANN modeling technology, an Au / TiO2 composite thin film-enhanced grooved PCF structure is designed, and an ANN prediction model with good generalization ability is constructed. This model can quickly predict sensor performance in a complex parameter space, significantly improving design efficiency and accuracy.

[0009] In addition, the measurement device based on PCF-SPR refractive index sensing proposed in the above embodiments of the present invention may also have the following additional technical features: Optionally, the optical emission assembly includes a light source and a polarization controller, the light source being connected to the polarization controller, the light source being used to emit optical signals, and the polarization controller being used to adjust the polarization state of the incident light.

[0010] Optionally, the Au / TiO2 composite thin film layer includes a metal layer and a dielectric layer. Optionally, the metal layer is a gold film with a thickness of 35 nm, the dielectric layer includes a titanium dioxide film and a background material silicon dioxide, the thickness of the titanium dioxide film is adjusted in the range of 50-65 nm, and the dispersion relation of the background material silicon dioxide can be expressed by the Sellmeier equation. Optionally, the optical receiving component includes a single-mode optical fiber and a spectrometer, wherein the single-mode optical fiber is used to transmit the optical signal modulated by the sensing subject, and the spectrometer is used to collect the loss spectrum.

[0011] Optionally, the prediction module is an ANN model, including an input layer, three hidden layers and an output layer. The input layer includes 12 neurons, each hidden layer includes 50 neurons, and the output layer includes 2 neurons. The activation function is the ReLU function.

[0012] To achieve the above objectives, a second aspect of the present invention proposes a measurement method based on PCF-SPR refractive index sensing, applied to the measurement device based on PCF-SPR refractive index sensing as described in any of the first aspects, comprising the following steps: injecting an analyte into the analyte-filling region of the sensing body to obtain the light source wavelength, the refractive index of the analyte, the titanium dioxide film thickness and its higher powers, and the corresponding loss spectrum; inputting the light source wavelength, the refractive index of the analyte, the titanium dioxide film thickness and its higher powers, and the corresponding loss spectrum into a constructed ANN model for training to obtain a trained prediction model, and constructing a functional relationship between refractive index and loss spectrum based on the trained prediction model; injecting the analyte to be tested into the analyte-filling region of the sensing body, and acquiring the real-time loss spectrum using a spectrometer; and obtaining the refractive index of the analyte to be tested based on the real-time loss spectrum and the functional relationship between refractive index and loss spectrum.

[0013] The measurement method based on PCF-SPR refractive index sensing proposed in this invention has the following advantages: by combining PCF-SPR sensing technology with ANN modeling technology, an Au / TiO2 composite thin film-enhanced grooved PCF structure is designed, and an ANN prediction model with good generalization ability is constructed. This model can quickly predict sensor performance in a complex parameter space, significantly improving design efficiency and accuracy. Attached Figure Description

[0014] Figure 1 This is a schematic diagram of a measurement device based on PCF-SPR refractive index sensing according to an embodiment of the present invention. Figure 2 This is a schematic flowchart of a measurement method based on PCF-SPR refractive index sensing according to an embodiment of the present invention. Figure 3 The refractive index of the analyte according to an embodiment of the present invention At 1.40, the dispersion relationship between the fiber core fundamental mode and the SPP mode, and the fundamental mode loss spectrum; Figure 4 To illustrate the fundamental mode loss spectrum and resonant wavelength response caused by the refractive index change of the analyte according to an embodiment of the present invention, wherein... Figure 4 (a) represents the fundamental mode loss spectrum response. Figure 4 (b) represents the resonant wavelength response; Figure 5 To illustrate the amplitude sensitivity of different analytes according to an embodiment of the present invention; Figure 6 The impact of feature engineering on neural network training convergence according to an embodiment of the present invention; Figure 7 The mean squared error of the training and validation datasets according to an embodiment of the present invention; Figure 8 This is a comparison diagram of actual data and predictions under different refractive indices of the test object according to an embodiment of the present invention, wherein... Figure 8 (a) Comparison of actual and predicted data for the fundamental mode loss spectrum. Figure 8 (b) Comparison of actual and predicted amplitude sensitivity data; Figure 9 This is a scatter plot of predicted and actual values ​​according to an embodiment of the present invention, wherein... Figure 9 (a) is a scatter plot of the predicted and actual values ​​of the fundamental mode loss spectrum. Figure 9 (b) is a scatter plot of the predicted and actual values ​​of amplitude sensitivity; Figure 10 In accordance with an embodiment of the present invention When t = 1.33, different t TiO2 The fundamental mode loss spectrum and amplitude sensitivity at (50, 55, 60, 65 nm) are shown, among which... Figure 10 (a) shows the fundamental mode loss spectrum. Figure 10 (b) is the amplitude sensitivity.

[0015] Label Explanation: 1. Optical transmitting component; 11. Light source; 12. Polarization controller; 2. Sensor body; 3. Optical receiving component; 31. Single-mode fiber; 32. Spectrometer; 4. Prediction module. Detailed Implementation

[0016] Embodiments of the present invention are described in detail below, examples of which are illustrated in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain the present invention, and should not be construed as limiting the present invention.

[0017] To better understand the above technical solutions, exemplary embodiments of the present invention will be described in more detail below with reference to the accompanying drawings. Although exemplary embodiments of the present invention are shown in the drawings, it should be understood that the present invention can be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided to enable a more thorough understanding of the present invention and to fully convey the scope of the invention to those skilled in the art.

[0018] To better understand the above technical solutions, the following will provide a detailed explanation of the technical solutions in conjunction with the accompanying drawings and specific implementation methods.

[0019] Figure 1 This is a schematic diagram of a measurement device based on PCF-SPR refractive index sensing according to an embodiment of the present invention, as shown below. Figure 1 As shown, the measuring device includes: an optical emitting component 1, a sensing body 2, an optical receiving component 3, and a prediction module 4.

[0020] The system comprises the following components: an optical emitting component for emitting optical signals and adjusting their polarization state; a sensing element connected to the optical emitting component, which is a photonic crystal fiber structure with double-layered air holes and an Au / TiO2 composite thin film layer deposited within its grooved channel; the Au / TiO2 composite thin film layer interacts with the analyte to excite surface plasmon resonance, thereby modulating the optical signal; an optical receiving component connected to the sensing element for receiving the modulated optical signal to obtain the fundamental mode loss spectrum and the refractive index of the analyte; and a prediction module connected to the optical receiving component, trained using a training dataset to quickly predict sensor performance under different structural parameters, including the fundamental mode loss spectrum and amplitude sensitivity. The training dataset includes the light source wavelength, the refractive index of the analyte, the titanium dioxide thin film thickness, and their higher powers.

[0021] It should be noted that the Au / TiO2 composite thin film layer includes a metal layer and a dielectric layer; the metal layer is a gold film, and its thickness is... =35 nm, the dielectric layer consists of a titanium dioxide thin film and a background material silicon dioxide, the titanium dioxide thin film thickness is... The dispersion relation of the background material, silicon dioxide, can be adjusted within the range of 50-65 nm and can be represented by the Sellmeier equation.

[0022] Specifically, the sensing element consists of a grooved PCF body and an Au / TiO2 composite thin film layer. The PCF body has a double-layer air hole structure, with the outer air hole diameter... =0.5μm, inner layer air pore diameter =1μm, the outermost air pore lattice constant Λ=3μm, the background material is silicon dioxide, and its dispersion relation is characterized by the Sellmeier equation:

[0023] Where n represents the refractive index of silicon dioxide at the corresponding wavelength, and λ represents the wavelength.

[0024] In addition, the complex permittivity of gold can be obtained through the Drude–Lorentz model:

[0025] in, This represents the dielectric constant of the gold film; =1.36×10 16 Indicates the oscillation frequency of the plasma; =1.45×10 14 Indicates the scattering frequency of electrons. This represents the dielectric constant of gold at high frequencies. It represents the angular frequency of light.

[0026] Furthermore, the refractive index of titanium dioxide is defined by the following formula:

[0027] in, This indicates the refractive index of titanium dioxide. Indicates wavelength.

[0028] As one embodiment, the optical emission assembly 1 includes a light source 11 and a polarization controller 12. The light source 11 is connected to the polarization controller 12. The light source 11 is used to emit optical signals, and the polarization controller 12 is used to adjust the polarization state of the incident light.

[0029] In other words, the polarization controller 12 ensures that the incident light is in a specific polarization state to match the PCF sensing requirements.

[0030] As one embodiment, the optical receiving component 3 includes a single-mode optical fiber 31 and a spectrum analyzer 32. The single-mode optical fiber 31 is used to transmit the optical signal modulated by the sensing subject, and the spectrum analyzer 32 is used to collect the loss spectrum.

[0031] As an example, prediction module 4 is an ANN model, including an input layer, three hidden layers and an output layer. The input layer includes 12 neurons, each hidden layer includes 50 neurons, and the output layer includes 2 neurons. The activation function is the ReLU function.

[0032] In other words, prediction module 4 is an artificial neural network prediction model, including one input layer, three hidden layers, and one output layer. The input layer has 12 neurons (corresponding to wavelength, refractive index of the analyte, titanium dioxide thickness, and the square, cubic, and fourth powers of these three values), each hidden layer has 50 neurons, and the output layer has 2 neurons (corresponding to the fundamental mode loss spectrum and amplitude sensitivity). The activation function for each layer is the ReLU function. The model is trained on a training set (…). Optimize parameters for 50nm, 55nm, and 65nm, on the test set ( The MSE on the 60nm core is 0.0021, indicating good generalization ability.

[0033] As a specific embodiment, the light signal emitted by the light source is polarized by a polarization controller and then incident on the analyte-filled area of ​​the PCF body through a single-mode fiber. The analyte (such as sucrose solutions of different concentrations with a refractive index range of 1.33-1.40 RIU) fills the air holes and surrounding areas of the PCF body. The Au / TiO2 composite thin film layer interacts with the analyte, exciting the surface plasmon resonance effect. After the light signal is modulated by SPR, it is transmitted to the spectrometer through a single-mode fiber, and the loss spectrum is collected by the spectrometer. Calculate the loss value using the following formula:

[0034] In the formula, λ represents the operating wavelength; Im( ) represents the imaginary part of the effective refractive index of the fiber fundamental mode.

[0035] Calculate wavelength sensitivity using the following formula:

[0036] In the formula, Δn represents the change in the refractive index of the analyte. This indicates the offset between two resonance peaks.

[0037] Calculate the resolution using the following formula:

[0038] In the formula, Δ Minimum wavelength resolution; Calculate the amplitude sensitivity using the following formula:

[0039] In the formula, δα(λ, α(λ, λ) represents the difference in the loss spectrum of the refractive index of two adjacent test objects. ) indicates that when the refractive index of the test object is The amplitude of time; the change in refractive index of adjacent test objects is denoted as δ. .

[0040] When measuring the refractive index of the analyte, the analyte is injected into the groove channel of the PCF body, and the real-time loss spectrum is obtained by a spectrometer. The refractive index of the analyte is obtained based on the real-time loss spectrum and the functional relationship between refractive index and loss spectrum, thus completing the sensing demodulation.

[0041] like Figure 1 As shown, the entire PCF structure consists of two layers with diameters of [missing information]. =0.5μm, The sensor consists of air pores of 1 μm, with a lattice constant (spacing) of Λ = 3 μm between the outermost air pores. Gold was chosen as the material to excite the SPR effect due to its excellent resonant displacement capability and chemical stability. Furthermore, to enhance the sensitivity of the sensing channel, a titanium dioxide film was deposited on the surface of the gold film in the right-side groove to strengthen the interaction between the evanescent field and the analyte. The thicknesses of the gold film and the titanium dioxide film are respectively... =35 nm and =60 nm.

[0042] Figure 3 The refractive index of the analyte is shown. At a wavelength of 1.40, the dispersion relation and loss spectrum of the fiber core fundamental mode and the SPP mode are shown. The red and black lines represent the real parts of the effective refractive index of the SPP mode and the fiber fundamental mode, respectively. The light blue line represents the change in fiber fundamental mode loss with wavelength. As shown in the figure, the real parts of the effective refractive index of both the fiber core fundamental mode and the SPP mode gradually decrease with increasing wavelength. When their real parts are equal (satisfying the phase matching condition), the two modes resonantly couple, and the loss spectrum of the fundamental mode exhibits a sharp loss peak. The wavelength corresponding to this peak is defined as the resonant wavelength. The optical field distribution when the fundamental mode and the SPP mode resonate is shown in the figure. Figure 3 As shown in the illustration.

[0043] Figure 4 The examples illustrate the refractive index of the analyte. The fundamental mode loss spectrum as it changes from 1.33 to 1.4. (From...) Figure 4 (a) It can be seen that the resonance peak of the fundamental mode loss spectrum redshifts as the refractive index of the analyte increases. This is because the effective refractive index of the SPP mode increases with the refractive index of the analyte. Figure 4 (b) shows the refractive index of the analyte. The dependence on the resonant wavelength. Based on the loss value calculation formula, linear fitting yields the wavelength sensitivity of the proposed PCF-SPR sensor to be 2594 nm / RIU, with a fitting coefficient R0. 2 =0.99. The corresponding wavelength resolution, calculated using the resolution calculation formula, is 3.86 × 10⁻⁹. −5 RIU.

[0044] Figure 5 Examples of amplitude sensitivity for different analytes are shown. As the refractive index of the analyte increases, the amplitude sensitivity of the fundamental mode generally shows an upward trend. This is because the higher the refractive index of the analyte, the stronger the energy coupling between the SPP mode and the fundamental mode, thus resulting in a higher AS value. At a value of 1.39, the proposed PCF-SPR sensor achieved its maximum amplitude sensitivity of 1492 RIU. -1 .

[0045] Figure 6 The diagram illustrates the impact of feature engineering on training neural networks. Feature engineering refers to adding higher-order input variables to construct a richer feature space. The training loss w / i FE and training loss w / o FE represent the changes in training loss with the number of training iterations after using feature engineering and without any feature engineering, respectively. As shown in the figure, the neural network employing feature engineering converges faster during training and has a lower final loss value, indicating improved overall model performance. This performance improvement mainly stems from the fact that the introduction of higher-order features enhances the expressive power of the input data, enabling the network to more effectively capture the nonlinear relationship between input and output, thereby improving training convergence and prediction accuracy. Figure 7The mean squared error of the training and validation datasets for the example is shown. The architectural parameters of the artificial neural network and the generated dataset are discussed. The modeled ANN architecture contains one input layer, one output layer, and three hidden layers. The ANN has a total of 164 neurons: 12 in the input layer, 50 in the hidden layers, and 2 in the output layer. The activation function used in each layer is the common ReLU function. The input variables in the constructed dataset include wavelength (λ = 0.75–1.05 μm), the refractive index of the analyte (1.33–1.39 RIU), and the thickness of titanium dioxide (50–60 nm). Feature engineering techniques can enhance the model's representational power by capturing nonlinear relationships, increasing flexibility, and improving predictive performance. Therefore, feature engineering in this paper is implemented by adding higher powers of the input variables, i.e., the square, cubic, and fourth powers of the original input variables. The output variables are the fundamental mode loss spectrum and amplitude sensitivity obtained based on the finite element method. Notably, the imaginary part of the effective refractive index is not included in the input variables to determine whether the model can predict constraint loss and sensitivity without simulation software data. The training, validation, and test datasets were divided based on the thickness of titanium dioxide (TiO2): the training dataset contained 516 samples (TiO2 thicknesses of 50 nm, 55 nm, and 65 nm), the validation dataset contained 130 samples (TiO2 thicknesses of 50 nm, 55 nm, and 65 nm, consistent with the training set parameters but with no sample overlap), and the test dataset contained a separate set of 216 samples with a TiO2 thickness of 60 nm (this thickness did not appear in either the training or validation sets). Therefore, the trained model never encountered sample data with a TiO2 thickness of 60 nm. The mean squared error (MSE) of the model on the training and validation sets during and after training is as follows: Figure 7 As shown, the MSE value of the training dataset continuously decreased over 1000 periods, eventually reaching 0.0025 on the validation dataset. The trained model achieved an MSE of 0.0021 on the test dataset, demonstrating that the model generalizes well to data with a titanium dioxide film thickness of 60 nm, which is not present in the training and validation datasets. This implies that the model can be used to predict data for other titanium dioxide film thicknesses.

[0046] To further illustrate the effects of this application, further tests were conducted on the embodiments. Figure 8 The fundamental mode loss spectrum and amplitude sensitivity are compared with the actual data and predictions under different refractive indices of the test objects in the example. Figure 8 (a) and Figure 8 (b) shows the actual and predicted datasets (60 nm, for the refractive index values ​​of seven different analytes not present in the training dataset). Figure 8The predicted curves largely coincide with the actual curves. The results show that the proposed model can accurately predict the fundamental mode loss spectrum and amplitude sensitivity from data with a titanium dioxide film thickness of 60 nm.

[0047] Figure 9 In Figure 9 (a) and Figure 9 (b) shows regression plots of predicted and actual data for the training, validation, and test datasets for the fundamental mode loss spectrum and amplitude sensitivity of the embodiment.

[0048] Figure 10 An example is shown in When =1.33, different The fundamental mode loss spectrum and amplitude sensitivity at (50, 55, 60, 65 nm) were obtained. =1.33 condition, Figure 10 (a) and Figure 10 (b) The actual and predicted values ​​of the loss spectrum and sensitivity are shown for titanium dioxide film thicknesses of 50, 55, 60, and 65 nm, respectively. This demonstrates that, with a fixed refractive index... Under the condition of 1.33, comparing the sensitivity and loss at different titanium dioxide thicknesses is feasible and effective, and a more suitable titanium dioxide thickness can be selected to optimize the overall performance of the sensor. This comparison method is also applicable to other analyte refractive index values ​​and can be used in any... The same analysis and optimization are carried out under the same conditions.

[0049] In summary, the measurement device based on PCF-SPR refractive index sensing provided by the embodiments of the present invention includes an optical emitting component, a sensing body, an optical receiving component, and a prediction module. The optical emitting component is used to emit optical signals and adjust the polarization state of the optical signals. The sensing body is connected to the optical emitting component and has a photonic crystal fiber structure with double-layer air holes. An Au / TiO2 composite thin film layer is deposited in its groove channel. The Au / TiO2 composite thin film layer interacts with the analyte to excite a surface plasmon resonance effect to modulate the optical signal. The optical receiving component is connected to the sensing body and is used to receive the modulated optical signal. The system uses a novel optical receiver to obtain the fundamental mode loss spectrum from the modulated optical signal and then to determine the refractive index of the analyte. A prediction module, connected to the optical receiver, is trained using a training dataset to quickly predict sensor performance under different structural parameters, including the fundamental mode loss spectrum and amplitude sensitivity. The training dataset includes the light source wavelength, the refractive index of the analyte, the titanium dioxide film thickness, and their higher powers. Therefore, by designing an Au / TiO2 composite film-enhanced grooved PCF structure, the sensing performance is improved. An ANN model is constructed to achieve rapid prediction of the sensor's loss spectrum and amplitude sensitivity, reducing design costs and enabling high-precision, high-efficiency refractive index measurement.

[0050] To achieve the above embodiments, such as Figure 2 As shown in the figure, this embodiment of the invention also proposes a measurement method based on PCF-SPR refractive index sensing, including the following steps: S101, inject the analyte into the analyte-filled region of the sensing body to obtain the light source wavelength, analyte refractive index, titanium dioxide film thickness and its higher powers and the corresponding loss spectrum.

[0051] S102, the wavelength of the light source, the refractive index of the analyte, the thickness of the titanium dioxide film and its higher powers and the corresponding loss spectrum are input into the constructed ANN model for training to obtain a trained prediction model, and a functional relationship between the refractive index and the loss spectrum is constructed based on the trained prediction model.

[0052] S103, the analyte to be tested is injected into the analyte-filling area of ​​the sensor body, and the real-time loss spectrum is obtained by a spectrometer.

[0053] S104. Obtain the refractive index of the analyte based on the real-time loss spectrum and the functional relationship between the refractive index and the loss spectrum.

[0054] In other words, firstly, the loss spectrum is acquired through an optical receiving component: the emitted light signal, after its polarization state is adjusted by a polarization controller, is incident on the analyte-filled region of the PCF body through a single-mode fiber; the analyte is injected into the analyte-filled region; the Au / TiO2 composite thin film layer interacts with the analyte, exciting the SPR effect, and the modulated light signal is transmitted through the PCF body to the single-mode fiber, and then from the single-mode fiber to the spectrometer; the loss spectrum is acquired; then, the ANN model is trained and validated: at wavelengths (0.75-1.05μm). (1.33-1.39 RIU) (50-65 nm) and their higher powers are used as inputs, and the calculation results (loss spectrum) obtained from the above steps are used as outputs. The dataset is divided into a training set (516 data points), a validation set (130 data points), and a test set (216 data points). =60 nm); iterative training for 1000 epochs reduced the MSE of the training set to 0.0025, and the MSE of the validation set stabilized at 0.0021; input test set data to verify the model's prediction accuracy for untrained thicknesses, thereby obtaining the functional relationship between refractive index and loss spectrum; finally, in actual refractive index measurement: the analyte is injected into the PCF groove channel, and the real-time loss spectrum is obtained through a spectrometer. The refractive index of the analyte is obtained based on the real-time loss spectrum and the functional relationship between refractive index and loss spectrum, thus completing the sensing demodulation.

[0055] Therefore, adopting the above technical solution has the following advantages: Performance Enhancement: By depositing Au / TiO2 composite films within the grooved channels of a PCF structure, this invention significantly enhances the interaction between the light field and the analyte, thereby improving the sensor's sensitivity. Experimental results show that, within the refractive index range of 1.33–1.40 RIU, this method and system can achieve a wavelength sensitivity of 2594 nm / RIU and a sensitivity of 3.86 × 10⁻⁶ nm / RIU. -5 RIU resolution and 1492 RIU -1 Its maximum amplitude sensitivity far exceeds the performance level of traditional PCF-SPR sensors.

[0056] Improved Design Efficiency: Significantly Reduced Computational Resource Consumption: Traditional PCF-SPR sensor design relies heavily on numerical calculations (such as the finite element method), which is particularly inefficient when optimizing high-dimensional parameter spaces. In contrast, this invention introduces an ANN for modeling and prediction, greatly reducing the reliance on repetitive simulation calculations and enabling rapid and accurate prediction of sensor performance in complex design spaces. This method can optimize structural parameters in a short time, significantly improving design efficiency and reducing computational costs and design cycles.

[0057] High-precision and fast prediction: The ANN model of this invention is trained based on a large amount of simulation data and feature engineering (including high powers of input variables). Validation has shown that the model can perform predictions even on unseen data (such as...). The ability to accurately predict sensor performance even at 60 nm demonstrates its excellent generalization ability and adaptability. Through this optimization, the present invention enables rapid evaluation of sensor performance under various structural parameters in the early stages of design, reducing the need for experimental verification and accelerating the product design process.

[0058] Wide applicability: This PCF-SPR sensor is not only suitable for environmental monitoring, chemical analysis, and biosensing, but can also be extended to industrial process control, food safety testing, and medical diagnostics. The flexibility of the ANN model allows the system to adjust the sensor structure according to different application needs, meeting the refractive index measurement requirements of different environments or analytes, thus possessing high versatility and customization capabilities.

[0059] Technological Breakthrough: A major challenge facing traditional PCF-SPR sensors is achieving optimization within a complex geometric parameter space. Traditional iterative-trial-and-error methods are not only computationally inefficient but also struggle to achieve global optimization. By combining deep learning and ANN technologies, this invention successfully constructs a system capable of automatic and rapid optimization and prediction, overcoming the computational bottleneck in PCF-SPR sensor design and significantly improving optimization efficiency and accuracy.

[0060] It should be noted that the device used in the measurement method based on PCF-SPR refractive index sensing in this embodiment is the aforementioned measurement device based on PCF-SPR refractive index sensing. Therefore, the explanation and description of the aforementioned embodiment of the measurement device based on PCF-SPR refractive index sensing also applies to the measurement method based on PCF-SPR refractive index sensing in this embodiment, and will not be repeated here.

[0061] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0062] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0063] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0064] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0065] It should be noted that any reference signs placed between parentheses in the claims should not be construed as limiting the claims. The word "comprising" does not exclude the presence of components or steps not listed in the claims. The word "a" or "an" preceding a component does not exclude the presence of a plurality of such components. The invention can be implemented by means of hardware comprising several different components and by means of a suitably programmed computer. In a unit claim enumerating several means, several of these means may be embodied by the same item of hardware. The use of the words first, second, and third, etc., does not indicate any order. These words can be interpreted as names.

[0066] Although preferred embodiments of the invention have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including both the preferred embodiments and all changes and modifications falling within the scope of the invention.

[0067] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

[0068] In the description of this invention, it should be understood that the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Therefore, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of this invention, "a plurality of" means two or more, unless otherwise explicitly specified.

[0069] In this invention, unless otherwise explicitly specified and limited, the terms "installation," "connection," "linking," and "fixing," etc., should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral part; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; they can refer to the internal communication of two components or the interaction between two components. Those skilled in the art can understand the specific meaning of the above terms in this invention according to the specific circumstances.

[0070] In this invention, unless otherwise explicitly specified and limited, "above" or "below" the second feature can mean that the first feature is in direct contact with the second feature, or that the first feature is in indirect contact with the second feature through an intermediate medium. Furthermore, "above," "over," and "on top" of the second feature can mean that the first feature is directly above or diagonally above the second feature, or simply that the first feature is at a higher horizontal level than the second feature. "Below," "below," and "under" the second feature can mean that the first feature is directly below or diagonally below the second feature, or simply that the first feature is at a lower horizontal level than the second feature.

[0071] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., refer to specific features, structures, materials, or characteristics described in connection with that embodiment or example, which are included in at least one embodiment or example of the present invention. In this specification, the illustrative expressions of the above terms should not be construed as necessarily referring to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0072] Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of the present invention.

Claims

1. A measuring device based on PCF-SPR refractive index sensing, characterized in that, include: An optical emitting component, wherein the optical emitting component is used to emit an optical signal and adjust the polarization state of the optical signal; The sensing body is connected to the optical emission component. The sensing body is a photonic crystal fiber structure and has a double-layer air hole. An Au / TiO2 composite thin film layer is deposited in its groove channel. The Au / TiO2 composite thin film layer interacts with the analyte to excite the surface plasmon resonance effect to modulate the optical signal. An optical receiving component is connected to the sensing body and is used to receive a modulated optical signal in order to obtain a real-time loss spectrum based on the modulated optical signal. The prediction module is connected to the optical receiving component. The prediction module is trained using a training dataset and constructs a functional relationship between refractive index and loss spectrum based on the trained prediction model. The refractive index of the analyte is obtained based on the real-time loss spectrum and the functional relationship between refractive index and loss spectrum. The training dataset includes the light source wavelength, the refractive index of the analyte, the thickness of the titanium dioxide film and its higher powers, and the corresponding loss spectrum.

2. The measuring device based on PCF-SPR refractive index sensing as described in claim 1, characterized in that, The optical emission assembly includes a light source and a polarization controller. The light source is connected to the polarization controller. The light source is used to emit optical signals, and the polarization controller is used to adjust the polarization state of the incident light.

3. The measuring device based on PCF-SPR refractive index sensing as described in claim 1, characterized in that, The Au / TiO2 composite thin film layer includes a metal layer and a dielectric layer.

4. The measuring device based on PCF-SPR refractive index sensing as described in claim 3, characterized in that, The metal layer is a gold film with a thickness of 35 nm. The dielectric layer includes a titanium dioxide film and a background material silicon dioxide. The thickness of the titanium dioxide film is adjusted in the range of 50-65 nm. The dispersion relation of the background material silicon dioxide can be expressed by the Sellmeier equation.

5. The measuring device based on PCF-SPR refractive index sensing as described in claim 1, characterized in that, The optical receiving component includes a single-mode optical fiber and a spectrum analyzer. The single-mode optical fiber is used to transmit the optical signal modulated by the sensing subject, and the spectrum analyzer is used to collect the loss spectrum.

6. The measuring device based on PCF-SPR refractive index sensing as described in claim 2, characterized in that, The prediction module is an ANN model, which includes an input layer, three hidden layers and an output layer. The input layer includes 12 neurons, each hidden layer includes 50 neurons, and the output layer includes 2 neurons. The activation function is the ReLU function.

7. A measurement method based on PCF-SPR refractive index sensing, characterized in that, A measurement device based on PCF-SPR refractive index sensing as described in any one of claims 1-6, comprising the following steps: The analyte is injected into the analyte-filled region of the sensor body to obtain the light source wavelength, analyte refractive index, titanium dioxide film thickness and its higher powers and the corresponding loss spectrum. The wavelength of the light source, the refractive index of the analyte, the thickness of the titanium dioxide film and its higher powers and the corresponding loss spectrum are input into the constructed ANN model for training to obtain a trained prediction model, and a functional relationship between the refractive index and the loss spectrum is constructed based on the trained prediction model. The analyte to be tested is injected into the analyte-filling area of ​​the sensor body, and the real-time loss spectrum is obtained by a spectrometer. The refractive index of the analyte is obtained based on the real-time loss spectrum and the functional relationship between the refractive index and the loss spectrum.

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