Chirp grating design method and device, equipment and storage medium

Abstract

The invention discloses a chirp grating design method and device, equipment and a storage medium, and relates to the technical field of optical fibers, and the disclosed chirp grating design method comprises the steps: analyzing a preset target spectrum function to obtain a target grating structure parameter; layering the initial grating into a plurality of sub-grating regions with uniform periods based on the target grating structure parameters, and establishing a layered transmission matrix model; determining a refractive index modulation depth range of each sub-gate region according to the layered transmission matrix model; and performing iterative optimization on grating structure parameters of the initial grating according to the layered transmission matrix model, the target spectral function, the refractive index modulation depth range and a preset error function to obtain a target grating. The performance of the chirp grating can be optimized.

Description

Technical Field

[0001] The present application relates to the field of optical fiber technology, and in particular to a chirped grating design method, device, equipment and storage medium. Background Art

[0002] In the field of optical fiber technology, chirped grating is a key optical component, and its performance directly affects the output quality and stability of fiber lasers.

[0003] Existing chirped grating design algorithms have problems such as slow convergence, distortion of the designed spectrum, and burrs in the designed spectrum. These problems are particularly prominent when the target spectrum bandwidth is large or the grating structure parameters are complex, seriously restricting the high-performance application of chirped gratings in fiber lasers.

[0004] In summary, how to optimize the performance of chirped gratings has become a technical problem that needs to be urgently solved in this field. Summary of the Invention

[0005] The main purpose of this application is to provide a chirped grating design method, device, equipment and storage medium, aiming to optimize the performance of the chirped grating.

[0006] To achieve the above objectives, the present application proposes a chirped grating design method, which includes:

[0007] Analyze the preset target spectrum function to obtain the target grating structure parameters;

[0008] Based on the target grating structure parameters, the initial grating is layered into a plurality of sub-grating regions with uniform periods, and a layered transmission matrix model is established;

[0009] Determining the refractive index modulation depth range of each of the sub-gate regions according to the layered transmission matrix model;

[0010] According to the layered transfer matrix model, the target spectral function, the refractive index modulation depth range and a preset error function, the grating structure parameters of the initial grating are iteratively optimized to obtain a target grating.

[0011] In one embodiment, the step of iteratively optimizing the grating structure parameters of the initial grating according to the layered transfer matrix model, the target spectral function, the refractive index modulation depth range, and a preset error function to obtain the target grating includes:

[0012] Generating a plurality of groups of candidate solutions for refractive index modulation depth distribution of the initial grating within the refractive index modulation depth range based on a preset Gaussian distribution function;

[0013] Inputting each group of candidate solutions of the refractive index modulation depth distribution into the layered transmission matrix model to obtain each real reflection spectrum;

[0014] Calculating the error value between each of the true reflection spectra and the target spectrum function using a preset error function, and selecting candidate solutions with error values ​​lower than a preset threshold from each group of refractive index modulation depth distribution candidate solutions as seed samples;

[0015] The Gaussian distribution function is dynamically updated to iteratively generate new seed samples, and when the new seed samples meet a preset convergence condition, a target grating corresponding to the new seed samples is obtained.

[0016] In one embodiment, the step of dynamically updating the Gaussian distribution function to iteratively generate new seed samples includes:

[0017] Smoothing and updating the mean and the root mean square value in the Gaussian distribution function according to the statistical characteristics of the seed sample;

[0018] Based on the updated Gaussian distribution function, the step of generating multiple groups of candidate solutions for refractive index modulation depth distribution of the initial grating within the refractive index modulation depth range and subsequent steps are performed to iteratively generate new seed samples.

[0019] In one embodiment, when the new seed sample satisfies a preset convergence condition, the step of obtaining a target grating corresponding to the new seed sample includes:

[0020] When the minimum error value of the new seed sample decreases below a preset amplitude threshold;

[0021] and / or,

[0022] If the fluctuations of the mean and root mean square value of the seed samples of multiple consecutive iterations tend to be stable, the iterative optimization is terminated and the target grating corresponding to the new seed sample is output.

[0023] In one embodiment, the error function includes a configurable wavelength weight factor to assign a preset weight ratio to a specified wavelength band in the target spectral function to enhance the optimization priority of key spectral regions.

[0024] In one embodiment, the step of layering the initial grating into a plurality of sub-grating regions with uniform periods based on the target grating structure parameters comprises:

[0025] Determining the number of layers of the initial grating based on the target grating structure parameters;

[0026] The initial grating is divided into a plurality of sub-grating regions with uniform periods according to the number of layers.

[0027] In one embodiment, the step of establishing a layered transmission matrix model includes:

[0028] Calculating an initial coupling coefficient based on the target grating structure parameters;

[0029] A layered transmission matrix model is established based on the initial coupling coefficient, wherein the initial coupling coefficient serves as an input reference for the refractive index modulation depth distribution in the layered transmission matrix model.

[0030] In addition, to achieve the above-mentioned purpose, the present application also proposes a chirped grating design device, which includes:

[0031] Function parsing module, used to parse the preset target spectral function to obtain target grating structure parameters;

[0032] A model building module, configured to layer the initial grating into a plurality of sub-grating regions with uniform periods based on the target grating structure parameters, and to establish a layered transmission matrix model;

[0033] a range determination module, configured to determine a refractive index modulation depth range of each of the sub-gate regions according to the layered transmission matrix model;

[0034] The grating optimization module is used to iteratively optimize the grating structure parameters of the initial grating according to the layered transfer matrix model, the target spectral function, the refractive index modulation depth range and a preset error function to obtain a target grating.

[0035] In addition, to achieve the above-mentioned purpose, the present application also proposes an electronic device, which includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the computer program is configured to implement the steps of the chirped grating design method as described above.

[0036] In addition, to achieve the above-mentioned purpose, the present application also proposes a storage medium, which is a computer-readable storage medium. A computer program is stored on the storage medium. When the computer program is executed by a processor, the steps of the chirped grating design method as described above are implemented.

[0037] The present application proposes a chirped grating design method, which analyzes a preset target spectral function to obtain target grating structure parameters; based on the target grating structure parameters, the initial grating is layered into multiple sub-grating regions with uniform period, and a layered transmission matrix model is established; the refractive index modulation depth range of each sub-grating region is determined according to the layered transmission matrix model; and the grating structure parameters of the initial grating are iteratively optimized according to the layered transmission matrix model, the target spectral function, the refractive index modulation depth range and the preset error function to obtain the target grating.

[0038] In summary, this application obtains the target grating structure parameters by analyzing the target spectral function, and then constructs a layered transmission matrix model in layers to determine the refractive index modulation depth range of the sub-grating area, and performs iterative optimization based on multiple factors. It can effectively avoid the problem of slow convergence speed of existing algorithms and improve design efficiency; at the same time, through precise control and optimization of various parameters, it can reduce the distortion and deformation of the design spectrum and the appearance of burrs in the design spectrum, thereby improving the design accuracy and performance stability of the chirped grating. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0040] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0041] Figure 1 A schematic diagram of a flow chart of the first embodiment of the chirped grating design method of the present application;

[0042] Figure 2 A schematic diagram of a triangular grating spectrum provided in Example 2 of the chirped grating design method of this application;

[0043] Figure 3 A schematic diagram of a Gaussian grating spectrum provided in Example 2 of the chirped grating design method of this application;

[0044] Figure 4 Schematic diagram of the module structure of the chirped grating design device according to an embodiment of the present application;

[0045] Figure 5 Schematic diagram of the device structure of the hardware operating environment involved in the chirped grating design method in the embodiment of the present application.

[0046] The purpose, features and advantages of this application will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0047] It should be understood that the specific embodiments described herein are merely used to explain the technical solutions of the present application and are not intended to limit the present application.

[0048] In the field of optical fiber technology, chirped grating is a key optical component, and its performance directly affects the output quality and stability of fiber lasers.

[0049] Existing chirped grating design algorithms have problems such as slow convergence, distortion of the designed spectrum, and burrs in the designed spectrum. These problems are particularly prominent when the target spectrum bandwidth is large or the grating structure parameters are complex, seriously restricting the high-performance application of chirped gratings in fiber lasers.

[0050] In summary, how to optimize the performance of chirped gratings has become a technical problem that needs to be urgently solved in this field.

[0051] An embodiment of the present application provides a solution, which analyzes a preset target spectral function to obtain target grating structure parameters; based on the target grating structure parameters, the initial grating is layered into multiple sub-grating regions with uniform period, and a layered transmission matrix model is established; the refractive index modulation depth range of each sub-grating region is determined according to the layered transmission matrix model; and the grating structure parameters of the initial grating are iteratively optimized according to the layered transmission matrix model, the target spectral function, the refractive index modulation depth range, and a preset error function to obtain the target grating.

[0052] In summary, the embodiment of the present application obtains the target grating structure parameters by analyzing the target spectral function, and then constructs a layered transmission matrix model in layers to determine the refractive index modulation depth range of the sub-grating area, and performs iterative optimization based on multiple factors. It can effectively avoid the problem of slow convergence speed of existing algorithms and improve design efficiency; at the same time, through precise control and optimization of various parameters, it can reduce the distortion and deformation of the design spectrum and the appearance of burrs in the design spectrum, thereby improving the design accuracy and performance stability of the chirped grating.

[0053] It should be noted that the execution subject of this embodiment can be an electronic device with data processing, network communication and program execution functions, or an electronic device capable of implementing the above functions, etc. The following uses an electronic device as an example to illustrate this embodiment and the following embodiments.

[0054] Based on this, the embodiment of the present application provides a chirped grating design method, referring to Figure 1 , Figure 1 Schematic diagram of the flow chart of the first embodiment of the chirped grating design method of the present application.

[0055] In this embodiment, the chirped grating design method includes steps S10 to S40:

[0056] Step S10, analyzing a preset target spectrum function to obtain target grating structure parameters;

[0057] Input the preset target spectral function R target (z,λ j ), the target spectrum function describes the reflection spectrum characteristics required by the chirped grating, where z represents the position coordinate of the grating along the fiber axis, λ jThe target grating structure parameters are extracted by analyzing the target spectral function. The parameters may include but are not limited to the grating length, effective refractive index, central period, and chirp rate.

[0058] Step S20, based on the target grating structure parameters, the initial grating is layered into a plurality of sub-grating regions with uniform periods, and a layered transmission matrix model is established;

[0059] Based on the acquired target grating structure parameters, the initial grating is divided axially into multiple sub-regions with uniform periodicity. Each sub-region has the same physical length to ensure layer uniformity. A transmission matrix model is then established for each sub-region, describing the propagation characteristics of light within that sub-region. By calculating the transmission matrix for each sub-region and multiplying them together, the total transmission matrix model for the entire grating is obtained, which is used for subsequent spectral calculations and optimization.

[0060] In a feasible embodiment, the step of "slicing the initial grating into a plurality of sub-grating regions with uniform periods based on the target grating structure parameters" in step S20 includes steps S201 to S202:

[0061] Step S201, determining the number of layers of the initial grating based on the target grating structure parameters;

[0062] The complexity and precision requirements of the grating are analyzed based on the extracted target grating structure parameters, wherein the target grating structure parameters include but are not limited to the grating length L, the effective refractive index n eff The chirp rate α, central period Λ, and grating length L determine the basic range of the stratification. The number of stratifications N is selected appropriately based on the required accuracy of the target spectrum. A greater number of stratifications results in higher computational accuracy but also increases computational complexity.

[0063] Determine the number of layers N based on the grid length L and accuracy requirements: Wherein, ΔL is the length of each sub-gate region, which is usually set to a smaller value according to the accuracy requirement.

[0064] Step S202: The initial grating is divided into a plurality of sub-grating regions with uniform periods according to the number of layers.

[0065] Based on the determined number of slices N, the initial grating is evenly divided into N sub-regions along the axial direction. For each sub-region, the local period Λ(z) is calculated based on the chirp rate α and the central period Λ. The relationship between the local period and position z is: Λ(z) = Λ + αz, where z is the position coordinate of the sub-region and the value of z ranges from the starting end (z = 0) to the end end (z = L) of the grating.

[0066] In a feasible embodiment, the step of "establishing a layered transmission matrix model" in step S20 may include steps S203 to S204:

[0067] Step S203, calculating the initial coupling coefficient based on the target grating structure parameters;

[0068] It should be noted that the target grating structure parameters include: grating length L, effective refractive index n eff , chirp rate α and central period Λ, where the grating length L represents the total length of the grating, the central period Λ represents the central period of the grating, which is associated with the central wavelength of the target spectrum, and the effective refractive index n eff represents the effective refractive index of light in the grating, and the chirp rate α represents the parameter of the grating period that varies with position.

[0069] Initial coupling coefficient It is an important parameter that describes the light coupling strength in the grating. The calculation formula for the initial coupling coefficient based on the target grating structure parameters is as follows:

[0070]

[0071] Where σ(z) represents the local detuning amount, λ represents the wavelength of the incident light, and n eff represents the refractive index, and Λ represents the central period.

[0072] Step S204 : establishing a layered transmission matrix model based on the initial coupling coefficient, wherein the initial coupling coefficient serves as an input reference for the refractive index modulation depth distribution in the layered transmission matrix model.

[0073] A transmission matrix model is established for each sub-grid region to describe the propagation characteristics of light in the sub-grid region. The transmission matrix of the i-th layer can be expressed as:

[0074] For each sub-gate area, calculate each element of the transfer matrix. The specific formula is as follows:

[0075]

[0076] Where Δn e represents the refractive index modulation function, S i represents the wavelength-dependent propagation constant, l i represents the length of the i-th layer grating, σ represents the local detuning amount, K represents the coupling coefficient, λ represents the wavelength of the incident light, and v represents the grating visibility.

[0077] Multiply the transfer matrices of all sub-grating areas in sequence to obtain the total transfer matrix model of the entire grating A0=F1F2…F i …F N-1 F N AN , the total transmission matrix model is used to describe the propagation characteristics of light in the complete grating, and then used to calculate the reflection spectrum.

[0078] Initial coupling coefficient As the input benchmark of the refractive index modulation depth distribution in the layered transfer matrix model, in the subsequent optimization process, the refractive index modulation depth distribution will be adjusted based on this initial value to better match the target spectral function.

[0079] Step S30, determining the refractive index modulation depth range of each sub-gate region according to the layered transmission matrix model;

[0080] According to the physical properties and actual processing capabilities of the fiber Bragg grating, a reasonable range of the refractive index modulation depth is determined. This refractive index modulation depth range serves as the boundary condition for the grating optimization design to ensure the physical feasibility of the refractive index modulation depth during the optimization process. That is, the maximum and minimum values ​​of the refractive index modulation depth are set as the boundary conditions for the optimization design to prevent the calculation results from deviating from the actual physical conditions.

[0081] Step S40 , iteratively optimizing the grating structure parameters of the initial grating according to the layered transfer matrix model, the target spectral function, the refractive index modulation depth range, and the preset error function to obtain the target grating.

[0082] Based on the target spectral function, the layered transfer matrix model and the refractive index modulation depth range, the grating optimization algorithm is initialized. Through an iterative process, the refractive index modulation depth distribution of each sub-grating area is dynamically adjusted to minimize the error between the calculated actual spectrum and the target spectrum. During the iterative process, the preset error function can be used to evaluate the optimization effect, and the optimization strategy can be updated according to the optimization results. When the optimization process meets the preset convergence conditions, the iteration is terminated and the optimized grating structure parameters are output to obtain a chirped grating design that meets the target spectral requirements.

[0083] In this way, in the embodiment of the present application, the target grating structure parameters are obtained by analyzing the target spectral function, and then a layered transmission matrix model is constructed in layers to determine the refractive index modulation depth range of the sub-grating area, and iterative optimization is performed based on multiple factors. This can effectively avoid the problem of slow convergence speed of existing algorithms and improve design efficiency. At the same time, through precise control and optimization of various parameters, the distortion and deformation of the design spectrum and the appearance of burrs in the design spectrum can be reduced, thereby improving the design accuracy and performance stability of the chirped grating.

[0084] Based on the first embodiment of the present application, in the second embodiment of the present application, the same or similar contents as those in the above-mentioned embodiment 1 can be referred to the above introduction and will not be repeated hereafter. On this basis, step S40 can include steps S401 to S404:

[0085] Step S401, generating multiple groups of candidate solutions for refractive index modulation depth distribution of the initial grating within the refractive index modulation depth range based on a preset Gaussian distribution function;

[0086] A Gaussian distribution function is preset, and its mean and root mean square root (RMS) are determined based on the initial refractive index modulation depth range. For example, the initial mean value can be set to the median of the refractive index modulation depth range, and the RMS value can be set based on the width of the range.

[0087] A Gaussian distribution function is used to randomly generate multiple groups of refractive index modulation depth distribution candidate solutions {nk(z)}, each group of candidate solutions contains the refractive index modulation depth distribution functions n(z) of N sub-grating regions.

[0088] For example, generate K groups of candidate solutions, each group of candidate solutions nk(z) is expressed as:

[0089] nk(z)=μ+δ·ξk(z);

[0090] Where μ represents the mean, δ represents the root mean square value, and ξk(z) is a randomly generated value from a standard normal distribution, ensuring that nk(z) is within the refractive index modulation depth range. At the same time, it is ensured that the refractive index modulation depth values ​​in each set of candidate solutions nk(z) generated are within the preset physical range.

[0091] Step S402, inputting each group of refractive index modulation depth distribution candidate solutions into a layered transmission matrix model to obtain each real reflection spectrum;

[0092] For each set of candidate solutions for the refractive index modulation depth distribution nk(z), the transfer matrix F of each sub-gate region is calculated according to the layered transfer matrix model. i Extract the reflection spectrum R(z,λ) from the total transmission matrix A0 j ). The reflectance spectrum can be calculated using the following formula:

[0093]

[0094] in, and is the corresponding element in the total transmission matrix A0.

[0095] Step S403, calculating the error between each true reflection spectrum and the target spectrum function using a preset error function, and selecting candidate solutions with error values ​​lower than a preset threshold from each group of refractive index modulation depth distribution candidate solutions as seed samples;

[0096] In a feasible embodiment, the error function includes a configurable wavelength weight factor to assign a preset weight ratio to a specified wavelength band in the target spectral function to enhance the optimization priority of the key spectral region.

[0097] In this embodiment, the calculated reflection spectrum is evaluated using a preset error function F(z)

[0098]

[0099] Among them, R(z,λ j ) represents the spectrum after actual chirped grating optimization, R target (z,λ j ) represents the target spectral function, w(λ j ) represents the weight function, which can increase the weight ratio within the spectral window.

[0100] Based on this error function, the corresponding error value of each group of candidate solutions nk(z) is calculated to screen out samples with error values ​​lower than the preset error threshold from all candidate solutions as seed samples for subsequent optimization. At the same time, the current minimum error value and its corresponding refractive index modulation depth distribution are recorded.

[0101] Step S404 : dynamically updating the Gaussian distribution function to iteratively generate new seed samples, and obtaining the target grating corresponding to the new seed samples when the new seed samples meet a preset convergence condition.

[0102] According to the statistical characteristics of the screened seed samples (such as mean and root mean square deviation), the parameters of the Gaussian distribution function are dynamically updated. The update formula can be expressed as:

[0103] δ t+1 =βδ t +(1-β)δ t-1 ;

[0104] μ t+1 =γμ t +(1-γ)μ t-1 ;

[0105] Among them, δ t+1 and μ t+1 are the root mean square value and mean of the updated seed sample, respectively; β and γ are smoothing factors.

[0106] The updated Gaussian distribution function is used to generate new candidate solutions for the refractive index modulation depth distribution. The new candidate solutions are substituted into the layered transmission matrix model to calculate the new reflection spectrum. Then, the error function is used to evaluate the difference between the new reflection spectrum and the target spectrum, and new seed samples are screened out. When the new seed samples meet the preset convergence conditions, the iterative optimization is terminated and the final optimized refractive index modulation depth distribution function, i.e., the target grating, is output.

[0107] In a feasible embodiment, the step of “dynamically updating the Gaussian distribution function to iteratively generate new seed samples” in step S404 may include steps S4041 to S4042:

[0108] Step S4041, smoothing and updating the mean and root mean square value in the Gaussian distribution function according to the statistical characteristics of the seed sample;

[0109] From the currently screened seed samples, their mean and root mean square values ​​are calculated. These data reflect the central tendency and discrete degree of the refractive index modulation depth distribution during the current optimization process. Smoothing factors β and γ are used to update the mean and root mean square values ​​of the Gaussian distribution function. The role of the smoothing factor is to prevent drastic fluctuations during the update process and ensure the stability of the optimization process.

[0110] Step S4042 , based on the updated Gaussian distribution function, execute the step of generating multiple groups of candidate solutions for the refractive index modulation depth distribution of the initial grating within the refractive index modulation depth range and subsequent steps to iteratively generate new seed samples.

[0111] The updated Gaussian distribution function is used to randomly generate multiple sets of new index modulation depth distribution candidate solutions. Each set of candidate solutions contains the index modulation depth values ​​of N sub-grating regions, ensuring that these values ​​are within the index modulation depth range. The new candidate solutions are then substituted into the layered transfer matrix model to calculate the corresponding reflection spectra.

[0112] The specific steps of the reflection spectrum include: calculating the transmission matrix for each sub-gate area according to the new refractive index modulation depth distribution; multiplying the transmission matrices of all sub-gate areas to obtain the total transmission matrix; and extracting the reflection spectrum from the total transmission matrix.

[0113] Then, the preset error function is used to evaluate the difference between the new reflection spectrum and the target spectrum function, and a new error value is calculated. Samples with error values ​​lower than the preset error threshold are screened out from the new candidate solutions as new seed samples. At the same time, the current minimum error value and its corresponding refractive index modulation depth distribution are recorded.

[0114] In a feasible embodiment, the step of “obtaining a target grating corresponding to the new seed sample when the new seed sample satisfies a preset convergence condition” in step S404 may include step S4043:

[0115] Step S4043: when the minimum error value of the new seed sample decreases below a preset threshold;

[0116] and / or,

[0117] If the mean and RMS fluctuations of the seed samples after multiple rounds of iterations tend to be stable, the iterative optimization is terminated and the target grating corresponding to the new seed sample is output.

[0118] Compare the difference between the current minimum error value and the minimum error value of the previous iteration. If the difference is lower than the preset amplitude threshold, it is considered that the decrease in the error value is small enough and the optimization process is close to convergence.

[0119] At the same time, the fluctuation of the mean and root mean square value of the seed samples in multiple consecutive iterations is checked. If these statistical characteristics tend to be stable in multiple consecutive iterations, that is, the change is less than the preset threshold, the optimization process is considered to have converged.

[0120] When any of the above convergence conditions is met, the iterative optimization process is terminated and the final optimized refractive index modulation depth distribution function is output, which is the target grating. The refractive index modulation depth distribution function describes the refractive index modulation depth of each sub-grating area in the grating and can achieve a reflection spectrum that is highly matched with the target spectral function.

[0121] In a feasible implementation scenario, the target spectral function can be a Gaussian spectral function or a triangular spectral function. For example, taking the triangular spectral function as an example, the current setting of the gate length is 10 mm, the chirp rate is 1 nm / cm, and the central wavelength is set to 1550 nm. The spectral curve optimized by the traditional algorithm is as follows: Figure 2 As shown by the blue line in FIG, the spectrum curve optimized by the chirped grating design provided by this embodiment is as follows: Figure 2 As shown by the red line in the figure, the horizontal axis represents the wavelength and the vertical axis represents the intensity of the output spectrum. Compared with the traditional design, this embodiment solves the burr problem of the spectrum, making the side mode suppression ratio better. Taking the Gaussian shape spectrum function as an example, the spectrum curve optimized by the traditional algorithm is shown as follows Figure 3 As shown by the blue line in FIG, the spectrum curve optimized by the chirped grating design provided by this embodiment is as follows: Figure 3 As shown by the red line in the figure, the horizontal axis represents the wavelength and the vertical axis represents the intensity of the output spectrum. Compared with the traditional design, the jitter problem caused by the chirp rate in the flat area of ​​the grating in this embodiment is solved, which improves the design accuracy and performance stability of the chirped grating.

[0122] It should be noted that the above examples are only used to understand the present application and do not constitute a limitation on the chirped grating design method of the present application. More simple transformations based on this technical concept are all within the scope of protection of the present application.

[0123] The present application also provides a chirped grating design device, please refer to Figure 4 , the chirped grating design device includes:

[0124] Function analysis module 10, used for analyzing a preset target spectrum function to obtain target grating structure parameters;

[0125] A model building module 20 is used to layer the initial grating into a plurality of sub-grating regions with uniform periods based on target grating structure parameters, and to establish a layered transmission matrix model;

[0126] A range determination module 30 is configured to determine a refractive index modulation depth range of each sub-grating region according to a layered transmission matrix model;

[0127] The grating optimization module 40 is used to iteratively optimize the grating structure parameters of the initial grating according to the layered transmission matrix model, the target spectral function, the refractive index modulation depth range and the preset error function to obtain the target grating.

[0128] Optionally, the grating optimization module 40 is further configured to:

[0129] Generate multiple sets of refractive index modulation depth distribution candidate solutions of the initial grating within the refractive index modulation depth range based on a preset Gaussian distribution function;

[0130] Input each set of refractive index modulation depth distribution candidate solutions into the layered transmission matrix model to obtain each true reflection spectrum;

[0131] The error value between each true reflection spectrum and the target spectrum function is calculated using a preset error function, and candidate solutions with error values ​​lower than a preset threshold are selected from each group of refractive index modulation depth distribution candidate solutions as seed samples;

[0132] The Gaussian distribution function is dynamically updated to iteratively generate new seed samples, and when the new seed samples meet the preset convergence conditions, the target grating corresponding to the new seed samples is obtained.

[0133] Optionally, the grating optimization module 40 is further configured to:

[0134] According to the statistical characteristics of the seed sample, the mean and root mean square value in the Gaussian distribution function are smoothly updated;

[0135] Based on the updated Gaussian distribution function, the steps of generating multiple sets of candidate solutions for the refractive index modulation depth distribution of the initial grating within the refractive index modulation depth range and subsequent steps are performed to iteratively generate new seed samples.

[0136] Optionally, the grating optimization module 40 is further configured to:

[0137] When the minimum error value of the new seed sample decreases below the preset amplitude threshold;

[0138] and / or,

[0139] If the mean and RMS fluctuations of the seed samples after multiple rounds of iterations tend to be stable, the iterative optimization is terminated and the target grating corresponding to the new seed sample is output.

[0140] Optionally, the error function includes a configurable wavelength weight factor to assign a preset weight ratio to a specified wavelength band in the target spectral function to strengthen the optimization priority of key spectral regions.

[0141] Optionally, the model building module 20 is further configured to:

[0142] Determining the number of layers of the initial grating based on target grating structure parameters;

[0143] The initial grating is divided into a plurality of sub-grating regions with uniform periods according to the number of layers.

[0144] Optionally, the model building module 20 is further configured to:

[0145] Calculating the initial coupling coefficient based on the target grating structure parameters;

[0146] A layered transmission matrix model is established based on the initial coupling coefficient, wherein the initial coupling coefficient serves as an input reference for the refractive index modulation depth distribution in the layered transmission matrix model.

[0147] The chirped grating design device provided in the embodiments of the present application utilizes the chirped grating design method of the aforementioned embodiments to optimize the performance of the chirped grating. Compared to the prior art, the chirped grating design device provided in the embodiments of the present application achieves the same beneficial effects as the chirped grating design method provided in the aforementioned embodiments. Other technical features of the chirped grating design device are the same as those disclosed in the chirped grating design method of the aforementioned embodiments and are not further described here.

[0148] An embodiment of the present application provides an electronic device, comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the chirped grating design method in the above-mentioned embodiment 1.

[0149] Reference below Figure 5 , which shows a schematic diagram of the structure of an electronic device suitable for implementing the embodiments of the present application. The electronic devices in the embodiments of the present application may include, but are not limited to, mobile terminals such as laptop computers, digital broadcast receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Descriptions), PMPs (Portable Media Players), in-vehicle terminals (such as in-vehicle navigation terminals), and fixed terminals such as digital TVs and desktop computers. Figure 5The electronic device shown is merely an example and should not limit the functions and scope of use of the embodiments of the present application.

[0150] like Figure 5 As shown, the electronic device may include a processing device 1001 (e.g., a central processing unit, a graphics processing unit, etc.), which can perform various appropriate actions and processes based on programs stored in a read-only memory 1002 or programs loaded from a storage device 1003 into a random access memory 1004. The random access memory 1004 also stores various programs and data required for the operation of the electronic device. The processing device 1001, the read-only memory 1002, and the random access memory 1004 are connected to each other via a bus 1005. An input / output interface 1006 is also connected to the bus. Typically, the following systems may be connected to the input / output interface 1006: an input device 1007 including, for example, a touch screen, a touchpad, a keyboard, a mouse, an image sensor, a microphone, an accelerometer, a gyroscope, etc.; an output device 1008 including, for example, a liquid crystal display (LCD), a speaker, a vibrator, etc.; a storage device 1003 including, for example, a magnetic tape or a hard disk; and a communication device 1009. The communication device 1009 may allow the electronic device to communicate with other devices wirelessly or wired to exchange data. Although the figures show electronic devices with various systems, it should be understood that it is not required to implement or have all the systems shown. More or fewer systems may be implemented or have instead.

[0151] In particular, according to the embodiments disclosed in the present application, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, the embodiments disclosed in the present application include a computer program product comprising a computer program carried on a computer-readable medium, the computer program comprising program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network via a communication device, or installed from a storage device 1003, or installed from a read-only memory 1002. When the computer program is executed by the processing device 1001, the above-mentioned functions defined in the method of the embodiment disclosed in the present application are performed.

[0152] The electronic device provided in the embodiments of the present application utilizes the chirped grating design method of the above-described embodiments to optimize the performance of the chirped grating. Compared to the prior art, the electronic device provided in the embodiments of the present application achieves the same beneficial effects as the chirped grating design method of the above-described embodiments. Other technical features of the electronic device are the same as those disclosed in the chirped grating design method of the above-described embodiments and are not further described here.

[0153] It should be understood that the various parts disclosed in the embodiments of the present application can be implemented using hardware, software, firmware, or a combination thereof. In the description of the above embodiments, specific features, structures, materials, or characteristics can be combined in any appropriate manner in any one or more embodiments or examples.

[0154] The above are only specific embodiments of the present application, but the scope of protection of this application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this application should be included in the scope of protection of this application. Therefore, the scope of protection of this application should be based on the scope of protection of the claims.

[0155] An embodiment of the present application provides a computer-readable storage medium having computer-readable program instructions (ie, a computer program) stored thereon, wherein the computer-readable program instructions are used to execute the chirped grating design method in the above embodiment.

[0156] The computer-readable storage medium provided in the embodiments of the present application may be, for example, a USB flash drive, but is not limited to electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems or devices, or any combination thereof. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In this embodiment, the computer-readable storage medium may be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system or device. The program code contained on the computer-readable storage medium may be transmitted using any appropriate medium, including but not limited to: wires, optical cables, RF (Radio Frequency), etc., or any suitable combination thereof.

[0157] The computer-readable storage medium may be included in the electronic device, or may exist independently without being incorporated into the electronic device.

[0158] The computer-readable storage medium carries one or more programs. When the one or more programs are executed by an electronic device, the electronic device: analyzes a preset target spectral function to obtain target grating structure parameters; based on the target grating structure parameters, the initial grating is layered into multiple sub-grating regions with uniform periods, and a layered transmission matrix model is established; the refractive index modulation depth range of each sub-grating region is determined according to the layered transmission matrix model; and the grating structure parameters of the initial grating are iteratively optimized according to the layered transmission matrix model, the target spectral function, the refractive index modulation depth range, and a preset error function to obtain the target grating.

[0159] The computer program code for performing the operations of the embodiments of the present application can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, C++, and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving a remote computer, the remote computer can be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or can be connected to an external computer (for example, using an Internet service provider to connect via the Internet).

[0160] The flow charts and block diagrams in the accompanying drawings illustrate the possible architecture, functions and operations of the systems, methods and computer program products according to various embodiments of the present application. In this regard, each box in the flow chart or block diagram can represent a module, program segment or a part of code, and the module, program segment or a part of code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order than that marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flow chart, and the combination of the boxes in the block diagram and / or flow chart can be implemented by a dedicated hardware-based system that performs the specified function or operation, or can be implemented by a combination of dedicated hardware and computer instructions.

[0161] The modules described in the embodiments of the present application may be implemented in software or hardware, wherein the name of a module does not necessarily limit the unit itself.

[0162] The computer-readable storage medium provided in the embodiments of this application is a computer-readable storage medium that stores computer-readable program instructions (i.e., a computer program) for executing the chirped grating design method described above, thereby optimizing the performance of the chirped grating. Compared to the prior art, the computer-readable storage medium provided in the embodiments of this application has the same beneficial effects as the chirped grating design method provided in the embodiments described above, and therefore is not further elaborated here.

[0163] The above are only some embodiments of the present application and are not intended to limit the patent scope of the present application. All equivalent structural transformations made using the contents of the present application specification and drawings under the technical concept of the present application, or direct / indirect application in other related technical fields are included in the patent protection scope of the present application.

Claims

1. A chirped grating design method, characterized in that: The chirped grating design method comprises: Analyze the preset target spectrum function to obtain the target grating structure parameters; Based on the target grating structure parameters, the initial grating is layered into a plurality of sub-grating regions with uniform periods, and a layered transmission matrix model is established; Determining the refractive index modulation depth range of each of the sub-gate regions according to the layered transmission matrix model; According to the layered transfer matrix model, the target spectral function, the refractive index modulation depth range and a preset error function, the grating structure parameters of the initial grating are iteratively optimized to obtain a target grating.

2. The chirped grating design method according to claim 1, wherein: The step of iteratively optimizing the grating structure parameters of the initial grating according to the layered transfer matrix model, the target spectral function, the refractive index modulation depth range, and a preset error function to obtain a target grating comprises: Generating a plurality of groups of candidate solutions for refractive index modulation depth distribution of the initial grating within the refractive index modulation depth range based on a preset Gaussian distribution function; Inputting each group of candidate solutions of the refractive index modulation depth distribution into the layered transmission matrix model to obtain each real reflection spectrum; Calculating the error value between each of the true reflection spectra and the target spectrum function using a preset error function, and selecting candidate solutions with error values ​​lower than a preset threshold from each group of refractive index modulation depth distribution candidate solutions as seed samples; The Gaussian distribution function is dynamically updated to iteratively generate new seed samples, and when the new seed samples meet a preset convergence condition, a target grating corresponding to the new seed samples is obtained.

3. The chirped grating design method according to claim 2, wherein: The step of dynamically updating the Gaussian distribution function to iteratively generate new seed samples includes: Smoothing and updating the mean and the root mean square value in the Gaussian distribution function according to the statistical characteristics of the seed sample; Based on the updated Gaussian distribution function, the step of generating multiple groups of candidate solutions for refractive index modulation depth distribution of the initial grating within the refractive index modulation depth range and subsequent steps are performed to iteratively generate new seed samples.

4. The chirped grating design method according to claim 2, wherein: The step of obtaining a target grating corresponding to the new seed sample when the new seed sample meets a preset convergence condition includes: When the minimum error value of the new seed sample decreases below a preset amplitude threshold; and / or, If the fluctuations of the mean and root mean square value of the seed samples of multiple consecutive iterations tend to be stable, the iterative optimization is terminated and the target grating corresponding to the new seed sample is output.

5. The chirped grating design method according to claim 2, wherein: The error function includes a configurable wavelength weight factor to assign a preset weight ratio to a specified wavelength band in the target spectral function to strengthen the optimization priority of key spectral regions.

6. The chirped grating design method according to claim 1, wherein: The step of layering the initial grating into a plurality of sub-grating regions with uniform periods based on the target grating structure parameters comprises: Determining the number of layers of the initial grating based on the target grating structure parameters; The initial grating is divided into a plurality of sub-grating regions with uniform periods according to the number of layers.

7. The chirped grating design method according to claim 1, wherein: The step of establishing a layered transmission matrix model includes: Calculating an initial coupling coefficient based on the target grating structure parameters; A layered transmission matrix model is established based on the initial coupling coefficient, wherein the initial coupling coefficient serves as an input reference for the refractive index modulation depth distribution in the layered transmission matrix model.

8. A chirped grating design device, characterized in that: The chirped grating design device comprises: Function parsing module, used to parse the preset target spectral function to obtain target grating structure parameters; A model building module, configured to layer the initial grating into a plurality of sub-grating regions with uniform periods based on the target grating structure parameters, and to establish a layered transmission matrix model; a range determination module, configured to determine a refractive index modulation depth range of each of the sub-gate regions according to the layered transmission matrix model; The grating optimization module is used to iteratively optimize the grating structure parameters of the initial grating according to the layered transfer matrix model, the target spectral function, the refractive index modulation depth range and a preset error function to obtain a target grating.

9. An electronic device, characterized in that: The electronic device comprises: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the computer program is configured to implement the steps of the chirped grating design method according to any one of claims 1 to 7.

10. A storage medium, characterized in that: The storage medium is a computer-readable storage medium, and a computer program is stored on the storage medium. When the computer program is executed by a processor, the steps of the chirped grating design method according to any one of claims 1 to 7 are implemented.

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