A Parameter Tuning Method and System for a Polarization Beam Splitter Based on Micro-Nano Gratings

Abstract

The present invention relates to the field of micro-nano optical technologies, and particularly to a method and system for optimizing parameters of a polarization beam splitter based on a micro-nano grating. The test optical characteristic parameters of the polarization beam splitter to be evaluated are obtained, and it is determined whether parameter optimization of the polarization beam splitter to be evaluated is required according to the test optical characteristic parameters and the preset optical characteristic parameters; if parameter optimization of the polarization beam splitter to be evaluated is required, the optical characteristic parameters to be optimized of the polarization beam splitter that needs to be parameter-optimized are obtained; the singular structure characteristic parameters of the polarization beam splitter that needs to be parameter-optimized are determined according to the optical characteristic parameters to be optimized; and the singular structure characteristic parameters of the polarization beam splitter that needs to be parameter-optimized are optimized. Through the present invention, the efficiency of parameter optimization of the polarization beam splitter is effectively improved, the structural factors affecting the performance are accurately located, and finally the optical performance of the polarization beam splitter is ensured to meet the preset requirements.

Description

Technical Field

[0001] The present invention relates to the field of micro-nano optical technologies, and particularly to a method and system for optimizing the parameters of a polarization beam splitter based on a micro-nano grating. Background Art

[0002] With the development of the miniaturization and integration of optical devices, polarization beam splitters based on micro-nano gratings have been widely used in the fields of optical communication, optical sensing, biomedical imaging, etc. due to their advantages such as small size, light weight, and easy integration. However, the polarization beam splitting performance of micro-nano gratings is significantly affected by their structural parameters (such as grating period, depth, duty cycle, etc.), and small changes in these parameters will lead to significant differences in polarization beam splitting performance. Therefore, how to quickly and accurately optimize the structural parameters of micro-nano grating polarization beam splitters to meet the polarization beam splitting performance requirements in specific application scenarios has become a key problem to be solved urgently. Traditional methods for optimizing the parameters of polarization beam splitters mainly rely on numerical simulation methods such as finite element analysis and finite difference time domain, which require a large amount of computing resources and time, and the optimization process is complex and inefficient. In addition, these methods usually need to preset the target performance and approach the target performance by repeatedly adjusting the structural parameters, which is easy to fall into local optimal solutions and difficult to obtain global optimal solutions. In view of this, the present invention proposes a method and system for optimizing the parameters of a polarization beam splitter based on a micro-nano grating. Summary of the Invention

[0003] The present invention overcomes the deficiencies of the prior art and provides a method and system for optimizing the parameters of a polarization beam splitter based on a micro-nano grating.

[0004] The technical solution adopted by the present invention to achieve the above object is as follows:

[0005] The present invention discloses a method for optimizing the parameters of a polarization beam splitter based on a micro-nano grating, including the following steps:

[0006] Obtain the standard structural characteristic parameters corresponding to various types of polarization beam splitters, and construct a Huffman search tree model library according to the standard structural characteristic parameters corresponding to various types of polarization beam splitters;

[0007] Obtain the structural model diagram of the polarization beam splitter to be evaluated, and import the structural model diagram of the polarization beam splitter to be evaluated into the Huffman search tree model library for search and matching to obtain the preset optical characteristic parameters of the polarization beam splitter to be evaluated;

[0008] Obtain the test optical characteristic parameters of the polarization beam splitter to be evaluated, and determine whether it is necessary to optimize the parameters of the polarization beam splitter to be evaluated according to the test optical characteristic parameters and the preset optical characteristic parameters;

[0009] If it is necessary to optimize the parameters of the polarization beam splitter to be evaluated, obtain the optical characteristic parameters to be optimized of the polarization beam splitter to be optimized; determine the singular structure characteristic parameters of the polarization beam splitter to be optimized according to the optical characteristic parameters to be optimized;

[0010] Perform optimization processing on the singular structure characteristic parameters of the polarization beam splitter to be optimized, so that the optical performance of the polarization beam splitter to be optimized meets the preset requirements.

[0011] Preferably, obtain the standard structure characteristic parameters corresponding to various types of polarization beam splitters, and construct a Huffman search tree model library according to the standard structure characteristic parameters corresponding to various types of polarization beam splitters, specifically:

[0012] Obtain the standard structure characteristic parameters corresponding to various types of polarization beam splitters, and construct characteristic model diagrams corresponding to various types of polarization beam splitters according to the corresponding standard structure characteristic parameters; wherein, the standard structure characteristic parameters include grating shape, grating width, grating pitch and grating period;

[0013] And obtain the preset optical characteristic parameters corresponding to various types of polarization beam splitters; wherein, the preset optical characteristic parameters include polarization beam splitting efficiency, extinction ratio, bandwidth, incident angle range, polarization state purity, transmittance, reflectance, angular resolution and polarization beam splitting angle;

[0014] Construct a Huffman tree, and recursively split out corresponding numbers of subtrees for various types of polarization beam splitters in the Huffman tree;

[0015] Encode the preset optical characteristic parameters corresponding to various types of polarization beam splitters to assign a string of digital codes to the preset optical characteristic parameters corresponding to various types of polarization beam splitters;

[0016] Inscribe the digital codes corresponding to various types of polarization beam splitters on the corresponding subtrees, and store the characteristic model diagrams corresponding to various types of polarization beam splitters on the corresponding subtrees respectively to obtain a Huffman search tree model library.

[0017] Preferably, obtain the structure model diagram of the polarization beam splitter to be evaluated, and import the structure model diagram of the polarization beam splitter to be evaluated into the Huffman search tree model library for search and matching to obtain the preset optical characteristic parameters of the polarization beam splitter to be evaluated, specifically:

[0018] Obtain the laser point cloud data of the polarization beam splitter to be evaluated through a laser scanning device, and construct the structure model diagram of the polarization beam splitter to be evaluated according to the laser point cloud data;

[0019] Extract the feature model diagrams within each subtree from the Huffman search tree model library, introduce the bounding box algorithm, and calculate the intersection degree between the structural model diagram of the polarization beam splitter to be evaluated and the feature model diagrams within each subtree based on the bounding box algorithm;

[0020] Extract the highest intersection degree from the intersection degrees between the structural model diagram of the polarization beam splitter to be evaluated and the feature model diagrams within each subtree, and obtain the feature model diagram belonging to the highest intersection degree;

[0021] Identify the subtree corresponding to the feature model diagram belonging to the highest intersection degree, obtain the digital code of the identified subtree, and interpret the digital code of the identified subtree to obtain the preset optical characteristic parameters of the polarization beam splitter to be evaluated.

[0022] Preferably, obtain the test optical characteristic parameters of the polarization beam splitter to be evaluated, and determine whether parameter tuning is required for the polarization beam splitter to be evaluated according to the test optical characteristic parameters and the preset optical characteristic parameters. Specifically:

[0023] Perform polarization optical testing on the polarization beam splitter to be evaluated based on an optical testing device to obtain the test optical characteristic parameters of the polarization beam splitter to be evaluated;

[0024] Obtain the accuracy performance requirement information of the polarization beam splitter to be evaluated, and determine the parameter deviation value threshold for each optical characteristic parameter of the polarization beam splitter to be evaluated according to the accuracy performance requirement information;

[0025] Calculate the actual parameter difference between each test optical characteristic parameter and the preset optical characteristic parameter of the polarization beam splitter to be evaluated;

[0026] Determine whether there is a situation where one or more actual parameter differences in the polarization beam splitter to be evaluated are greater than the corresponding parameter deviation value threshold;

[0027] If there is a situation where one or more actual parameter differences in the polarization beam splitter to be evaluated are greater than the corresponding parameter deviation value threshold, then determine the polarization beam splitter to be evaluated as a polarization beam splitter that requires parameter tuning;

[0028] If there is no situation where the actual parameter difference in the polarization beam splitter to be evaluated is greater than the corresponding parameter deviation value threshold, then determine the polarization beam splitter to be evaluated as a polarization beam splitter that does not require parameter tuning;

[0029] Among them, the test optical characteristic parameters include polarization beam splitting efficiency, extinction ratio, bandwidth, incident angle range, polarization state purity, transmittance, reflectance, angular resolution, and polarization beam splitting angle.

[0030] Preferably, if parameter tuning is required for the polarization beam splitter to be evaluated, then obtain the optical characteristic parameters to be tuned of the polarization beam splitter that requires parameter tuning. Specifically:

[0031] If the polarization beam splitter to be evaluated is determined to be a polarization beam splitter that requires parameter tuning, the optical characteristic parameters corresponding to the actual parameter difference greater than the corresponding parameter deviation value threshold are calibrated as the optical characteristic parameters to be tuned, and the optical characteristic parameters to be tuned of the polarization beam splitter that requires parameter tuning are obtained.

[0032] Preferably, the singular structural characteristic parameters of the polarization beam splitter that requires parameter tuning are determined according to the optical characteristic parameters to be tuned, specifically:

[0033] Obtain the functional specification information of the polarization beam splitter that requires parameter tuning, and obtain the linear influence degree between each structural characteristic parameter and each optical characteristic parameter in the polarization beam splitter that requires parameter tuning according to the functional specification information;

[0034] Take each structural characteristic parameter as a quantitative node, and each optical characteristic parameter as a variable node; and determine the topological distance between each quantitative node and each variable node according to the linear influence degree between each structural characteristic parameter and each optical characteristic parameter; among them, the greater the linear influence degree of the structural characteristic parameter on the optical characteristic parameter, the smaller the corresponding topological distance;

[0035] Topologically connect each quantitative node and each variable node according to the linear distance between each quantitative node and each variable node, and obtain the topological structure diagram between each structural characteristic parameter and each optical characteristic parameter in the polarization beam splitter that requires parameter tuning;

[0036] Obtain the optical characteristic parameters to be tuned of the polarization beam splitter that requires parameter tuning, and retrieve the variable node corresponding to the optical characteristic parameter to be tuned in the topological structure diagram, and define it as the target variable node;

[0037] Obtain the topological distance between the target variable node and the quantitative node in the topological structure diagram, and mark the quantitative node corresponding to the topological distance not greater than the preset distance threshold as the target quantitative node;

[0038] Obtain the structural characteristic parameter corresponding to the target quantitative node, and obtain the singular structural characteristic parameters of the polarization beam splitter that requires parameter tuning.

[0039] Preferably, optimize the singular structural characteristic parameters of the polarization beam splitter that requires parameter tuning, specifically:

[0040] Obtain the structural model diagram of the polarization beam splitter that requires parameter tuning, and perform format transformation processing on the structural model diagram of the polarization beam splitter that requires parameter tuning to obtain the finite element analysis model of the polarization beam splitter that requires parameter tuning, and import the finite element analysis model into the finite element analysis software;

[0041] Determine the key variables of the finite element analysis model according to the singular structure characteristic parameters of the polarization beam splitter to be optimized, and set the excitation source parameters in the finite element analysis software;

[0042] Obtain the preset process range of the singular structure characteristic parameters, define the upper boundary condition and the lower boundary condition of the singular structure characteristic parameters according to the preset process range, and discretize a number of discrete parameter values of the singular structure characteristic parameters according to the upper boundary condition and the lower boundary condition;

[0043] Respectively replace the corresponding singular structure characteristic parameters in the finite element analysis model based on each discrete parameter value, and perform polarization optical simulation test processing on the finite element analysis model after replacing the parameters to obtain the simulated optical characteristic parameters of the finite element analysis model;

[0044] Calculate the Pearson correlation coefficient value between the simulated optical characteristic parameters and the preset optical characteristic parameters. If the Pearson correlation coefficient value is not greater than the preset value, perform the next discrete parameter value replacement test operation, and continuously iterate this process until the obtained Pearson correlation coefficient value is greater than the preset value;

[0045] When the obtained Pearson correlation coefficient value is greater than the preset value, use the discrete parameter value corresponding to the corresponding replacement test operation as the optimized parameter value of the singular structure characteristic parameters;

[0046] Perform optimization processing on the polarization beam splitter that needs to be optimized based on the optimized parameter value.

[0047] The present invention also discloses a polarization beam splitter parameter optimization system based on a micro-nano grating. The polarization beam splitter parameter optimization system includes a memory and a processor. A polarization beam splitter parameter optimization method program is stored in the memory. When the polarization beam splitter parameter optimization method program is executed by the processor, the steps of any one of the polarization beam splitter parameter optimization methods are implemented.

[0048] The present invention solves the technical defects existing in the background art, and the present invention has the following beneficial effects: obtaining the standard structural characteristic parameters corresponding to various types of polarization beam splitters, and constructing a Huffman search tree model library according to the standard structural characteristic parameters corresponding to various types of polarization beam splitters; obtaining the structural model diagram of the polarization beam splitter to be evaluated, and importing the structural model diagram of the polarization beam splitter to be evaluated into the Huffman search tree model library for search and matching to obtain the preset optical characteristic parameters of the polarization beam splitter to be evaluated; obtaining the test optical characteristic parameters of the polarization beam splitter to be evaluated, and judging whether it is necessary to optimize the parameters of the polarization beam splitter to be evaluated according to the test optical characteristic parameters and the preset optical characteristic parameters; if it is necessary to optimize the parameters of the polarization beam splitter to be evaluated, obtaining the optical characteristic parameters to be optimized of the polarization beam splitter that needs to be optimized for parameters; determining the singular structural characteristic parameters of the polarization beam splitter that needs to be optimized for parameters according to the optical characteristic parameters to be optimized; and performing optimization processing on the singular structural characteristic parameters of the polarization beam splitter that needs to be optimized for parameters so that the optical performance of the polarization beam splitter that needs to be optimized for parameters meets the preset requirements. Through the present invention, the efficiency of parameter optimization of the polarization beam splitter is effectively improved, the structural factors affecting the performance are accurately located, and finally the optical performance of the polarization beam splitter is ensured to meet the preset requirements. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained according to these drawings.

[0050] Figure 1 It is the overall method flowchart of the parameter optimization method for this polarization beam splitter;

[0051] Figure 2 It is the partial method flowchart of the parameter optimization method for this polarization beam splitter;

[0052] Figure 3 It is the system block diagram of the parameter optimization system for this polarization beam splitter. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0053] In order to be able to more clearly understand the above-mentioned objects, features and advantages of the present invention, the present invention will be further described in detail below with reference to the drawings and specific embodiments. It should be noted that, without conflict, the embodiments of the present application and the features in the embodiments can be combined with each other.

[0054] In the following description, many specific details are set forth in order to provide a thorough understanding of the present invention. However, the present invention may be practiced in other ways than those specifically described herein, and thus, the scope of protection of the present invention is not limited by the specific embodiments disclosed below.

[0055] The present invention discloses a method for optimizing the parameters of a polarization beam splitter based on a micro-nano grating. As Figure 1 shown, it includes the following steps:

[0056] S102: Obtain the standard structural characteristic parameters corresponding to various types of polarization beam splitters, and construct a Huffman search tree model library according to the standard structural characteristic parameters corresponding to various types of polarization beam splitters;

[0057] S104: Obtain the structural model diagram of the polarization beam splitter to be evaluated, import the structural model diagram of the polarization beam splitter to be evaluated into the Huffman search tree model library for search and matching, and obtain the preset optical characteristic parameters of the polarization beam splitter to be evaluated;

[0058] S106: Obtain the test optical characteristic parameters of the polarization beam splitter to be evaluated, and determine whether it is necessary to optimize the parameters of the polarization beam splitter to be evaluated according to the test optical characteristic parameters and the preset optical characteristic parameters;

[0059] S108: If it is necessary to optimize the parameters of the polarization beam splitter to be evaluated, obtain the optical characteristic parameters to be optimized of the polarization beam splitter that needs to be optimized; determine the singular structural characteristic parameters of the polarization beam splitter that needs to be optimized according to the optical characteristic parameters to be optimized;

[0060] S110: Optimize the singular structural characteristic parameters of the polarization beam splitter that needs to be optimized so that the optical performance of the polarization beam splitter that needs to be optimized meets the preset requirements.

[0061] It should be noted that, first, the standard structural characteristic parameters of various types of polarization beam splitters are obtained. The structural characteristic parameters here include key factors such as grating shape, grating width, grating pitch, and grating period. These parameters are the basic descriptions of the polarization beam splitter structure. By constructing a Huffman search tree model library with these standard structural characteristic parameters corresponding to different types of polarization beam splitters. The Huffman search tree has efficient search characteristics and can quickly locate and match relevant information. For the polarization beam splitter to be evaluated, its structural model diagram is obtained and imported into the constructed Huffman search tree model library for search and matching. In this way, the preset optical characteristic parameters of the polarization beam splitter to be evaluated can be obtained. These optical characteristic parameters include polarization beam splitting efficiency, extinction ratio, bandwidth, incident angle range, polarization state purity, transmittance, reflectivity, angular resolution, and polarization beam splitting angle, etc. This step is to preliminarily estimate the characteristics of the new polarization beam splitter using the existing model library information. Then, the test optical characteristic parameters of the polarization beam splitter to be evaluated are obtained and compared with the preset optical characteristic parameters obtained previously. Due to factors such as actual manufacturing and usage environments, the test optical characteristic parameters may be different from the preset ones. By comparing these two sets of parameters, it can be determined whether parameter tuning is required for the polarization beam splitter to be evaluated. If parameter tuning is required, the optical characteristic parameters to be tuned for the polarization beam splitter that needs parameter tuning are obtained. Then, based on these optical characteristic parameters to be tuned, the singular structural characteristic parameters of the polarization beam splitter that needs parameter tuning are determined. These singular structural characteristic parameters are the key structural factors that affect the substandard optical performance of the polarization beam splitter. Finally, these singular structural characteristic parameters are optimized, aiming to make the optical performance of the polarization beam splitter that needs parameter tuning meet the preset requirements, so as to ensure that the performance of the polarization beam splitter in actual applications meets the expectations.

[0062] In summary, the parameter tuning method for the polarization beam splitter based on micro-nano gratings can quickly obtain the preset optical characteristic parameters of the polarization beam splitter to be evaluated by constructing a Huffman search tree model library, judge whether to tune by comparing with the test optical characteristic parameters, further determine the singular structural characteristic parameters and optimize them, effectively improving the efficiency of parameter tuning of the polarization beam splitter, accurately locating the structural factors affecting the performance, and finally ensuring that the optical performance of the polarization beam splitter meets the preset requirements, providing an efficient and accurate solution for the design, manufacturing, and performance optimization of micro-nano grating polarization beam splitters.

[0063] Preferably, the standard structural characteristic parameters corresponding to various types of polarization beam splitters are obtained, and a Huffman search tree model library is constructed according to the standard structural characteristic parameters corresponding to various types of polarization beam splitters, as Figure 2 shown, specifically:

[0064] S202: Obtain the standard structural characteristic parameters corresponding to various types of polarization beam splitters, and construct the characteristic model diagrams corresponding to various types of polarization beam splitters according to the corresponding standard structural characteristic parameters; wherein, the standard structural characteristic parameters include grating shape, grating width, grating pitch, and grating period;

[0065] S204: And obtain the preset optical characteristic parameters corresponding to various types of polarization beam splitters; wherein, the preset optical characteristic parameters include polarization beam splitting efficiency, extinction ratio, bandwidth, incident angle range, polarization state purity, transmittance, reflectance, angular resolution, and polarization beam splitting angle;

[0066] S206: Construct a Huffman tree, and recursively split out the corresponding number of subtrees for various types of polarization beam splitters in the Huffman tree;

[0067] S208: Encode the preset optical characteristic parameters corresponding to various types of polarization beam splitters to assign a string of digital codes to the preset optical characteristic parameters corresponding to various types of polarization beam splitters;

[0068] S210: Inscribe the digital codes corresponding to various types of polarization beam splitters on the corresponding subtrees, and store the characteristic model diagrams corresponding to various types of polarization beam splitters on the corresponding subtrees respectively, to obtain a Huffman search tree model library.

[0069] It should be noted that the standard structural characteristic parameters of various types of polarization beam splitters are obtained, which cover aspects such as grating shape, grating width, grating pitch, and grating period. Based on these standard structural characteristic parameters, the characteristic model diagrams corresponding to various types of polarization beam splitters are constructed. This step is to represent the structural characteristics of the polarization beam splitter in the form of a visual or easily processed model diagram, preparing for the subsequent construction of the model library. At the same time, the preset optical characteristic parameters corresponding to various types of polarization beam splitters are obtained, which include polarization beam splitting efficiency, extinction ratio, bandwidth, incident angle range, polarization state purity, transmittance, reflectance, angular resolution, and polarization beam splitting angle, etc. These optical characteristic parameters are important indicators for measuring the performance of the polarization beam splitter and need to be associated with the structural characteristics when constructing the model library. Constructing a Huffman tree is one of the core steps in the entire construction of the model library. According to various types of polarization beam splitters, the corresponding number of subtrees are recursively split in the Huffman tree. The Huffman tree is a tree model with the shortest weighted path length. By splitting the subtrees in this way, different types of polarization beam splitters can be effectively classified and organized, making subsequent searches and matches more efficient. The preset optical characteristic parameters corresponding to various types of polarization beam splitters are encoded, and a string of digital codes is assigned to them. This encoding method can transform complex optical characteristic parameters into a form convenient for storage and identification in the Huffman tree structure, enabling the optical characteristics of each polarization beam splitter to be uniquely identified in a concise manner. Finally, the digital codes corresponding to various types of polarization beam splitters are engraved on the corresponding subtrees, and the characteristic model diagrams corresponding to various types of polarization beam splitters are respectively stored on the corresponding subtrees, thus obtaining the Huffman search tree model library. This model library integrates the structural characteristics, optical characteristics, and organizational structure of the Huffman tree of the polarization beam splitter, providing a comprehensive data basis for subsequent searches, matches, and evaluations of the polarization beam splitter.

[0070] The Huffman search tree model library constructed through the above series of steps organically combines the structural characteristics and optical characteristics of the polarization beam splitter and is organized in the structure of a Huffman tree. This model library can efficiently store and manage the relevant information of different types of polarization beam splitters, providing fast and accurate data support for subsequent operations such as evaluation, search, and match of the polarization beam splitter, helping to improve the work efficiency in the research and application of the polarization beam splitter. For example, it can play an important role in the design optimization, performance evaluation, and fault troubleshooting of the polarization beam splitter.

[0071] Preferably, the structural model diagram of the polarization beam splitter to be evaluated is obtained, and the structural model diagram of the polarization beam splitter to be evaluated is imported into the Huffman search tree model library for search and match to obtain the preset optical characteristic parameters of the polarization beam splitter to be evaluated, specifically:

[0072] Obtain the laser point cloud data of the polarization beam splitter to be evaluated through a laser scanning device, and construct a structural model diagram of the polarization beam splitter to be evaluated according to the laser point cloud data;

[0073] Extract the characteristic model diagrams within each subtree from the Huffman search tree model library, introduce the bounding box algorithm, and calculate the intersection degree between the structural model diagram of the polarization beam splitter to be evaluated and the characteristic model diagrams within each subtree based on the bounding box algorithm;

[0074] Extract the highest intersection degree from the intersection degrees between the structural model diagram of the polarization beam splitter to be evaluated and the characteristic model diagrams within each subtree, and obtain the characteristic model diagram belonging to the highest intersection degree;

[0075] Identify the subtree corresponding to the characteristic model diagram belonging to the highest intersection degree, obtain the digital code of the identified subtree, and interpret the digital code of the identified subtree to obtain the preset optical characteristic parameters of the polarization beam splitter to be evaluated.

[0076] It should be noted that the laser point cloud data of the polarization beam splitter to be evaluated is obtained by using a laser scanning device. The laser point cloud data is a three-dimensional data representation form, which contains the coordinate information of a large number of points on the object surface. According to these laser point cloud data, a structural model diagram of the polarization beam splitter to be evaluated is constructed. This structural model diagram can accurately reflect the geometric structural characteristics of the polarization beam splitter to be evaluated and is the basis for subsequent search and matching. In the Huffman search tree model library, the characteristic model diagrams within each subtree need to be extracted. Then, the bounding box algorithm is introduced. The bounding box algorithm is an algorithm used to quickly calculate the spatial relationship between two objects. Based on this algorithm, the intersection degree between the structural model diagram of the polarization beam splitter to be evaluated and the characteristic model diagrams within each subtree is calculated. The intersection degree can be understood as a quantitative index of the similarity or matching degree between two model diagrams. Among the calculated intersection degrees between the structural model diagram of the polarization beam splitter to be evaluated and the characteristic model diagrams within each subtree, the highest intersection degree is extracted. The characteristic model diagram corresponding to this highest intersection degree is the model diagram that is most similar to the polarization beam splitter to be evaluated in terms of structure. By obtaining this characteristic model diagram belonging to the highest intersection degree, the model that best matches the structure of the polarization beam splitter to be evaluated in the Huffman search tree model library is found. Identify the subtree corresponding to the characteristic model diagram belonging to the highest intersection degree, and then obtain the digital code of the identified subtree. Since the preset optical characteristic parameters corresponding to various types of polarization beam splitters were engraved in the form of digital codes on the subtrees when the model library was constructed before, interpreting the digital code of the identified subtree can obtain the preset optical characteristic parameters of the polarization beam splitter to be evaluated. This step realizes the mapping from the structural model diagram to the optical characteristic parameters, and finds the corresponding optical characteristic parameters through the structural similarity.

[0077] Through the above steps, based on the structural model diagram of the polarization beam splitter to be evaluated, the Huffman search tree model library can be utilized, and the intersection degree can be calculated by means of the bounding box algorithm to find the most matching feature model diagram, thereby obtaining the preset optical characteristic parameters of the polarization beam splitter to be evaluated. This method realizes the rapid and accurate derivation from the structural information of the polarization beam splitter to be evaluated to the optical characteristic parameters, provides an important basis for subsequent operations such as judging whether the polarization beam splitter needs parameter tuning, etc., and improves the efficiency of the performance evaluation and optimization process of the polarization beam splitter.

[0078] Preferably, obtain the test optical characteristic parameters of the polarization beam splitter to be evaluated, and judge whether parameter tuning is required for the polarization beam splitter to be evaluated according to the test optical characteristic parameters and the preset optical characteristic parameters. Specifically:

[0079] Perform polarization optical testing on the polarization beam splitter to be evaluated based on an optical testing device to obtain the test optical characteristic parameters of the polarization beam splitter to be evaluated;

[0080] Obtain the information on the accuracy performance requirements of the polarization beam splitter to be evaluated, and determine the threshold value of the parameter deviation of each optical characteristic parameter of the polarization beam splitter to be evaluated according to the accuracy performance requirements information;

[0081] Calculate the actual parameter difference between each test optical characteristic parameter and the preset optical characteristic parameter of the polarization beam splitter to be evaluated;

[0082] Judge whether there is a situation where one or more actual parameter differences in the polarization beam splitter to be evaluated are greater than the corresponding parameter deviation threshold value;

[0083] If there is a situation where one or more actual parameter differences in the polarization beam splitter to be evaluated are greater than the corresponding parameter deviation threshold value, then determine the polarization beam splitter to be evaluated as a polarization beam splitter that requires parameter tuning;

[0084] If there is no situation where the actual parameter difference in the polarization beam splitter to be evaluated is greater than the corresponding parameter deviation threshold value, then determine the polarization beam splitter to be evaluated as a polarization beam splitter that does not require parameter tuning;

[0085] Among them, the test optical characteristic parameters include polarization beam splitting efficiency, extinction ratio, bandwidth, incident angle range, polarization state purity, transmittance, reflectance, angular resolution, and polarization beam splitting angle.

[0086] It should be noted that, first, the polarization optical test is carried out on the polarization beam splitter to be evaluated based on the optical test equipment, so as to obtain the test optical characteristic parameters of the polarization beam splitter to be evaluated. These test optical characteristic parameters cover multiple aspects such as polarization beam splitting efficiency, extinction ratio, bandwidth, incident angle range, polarization state purity, transmittance, reflectance, angular resolution, and polarization beam splitting angle. These parameters comprehensively describe the performance of the polarization beam splitter in actual tests. Then, obtain the information on the precision performance requirements of the polarization beam splitter to be evaluated, and determine the threshold value of the parameter deviation of each optical characteristic parameter of the polarization beam splitter to be evaluated according to this information. This threshold value is set according to specific precision performance requirements, and it represents the allowable fluctuation range of each optical characteristic parameter when the application requirements are met. Calculate the actual parameter difference between each test optical characteristic parameter of the polarization beam splitter to be evaluated and the preset optical characteristic parameters. The preset optical characteristic parameters are the theoretically optical characteristic parameters obtained through model matching before, while the test optical characteristic parameters are obtained through actual measurement. By calculating the difference between the two, the deviation degree between the actual performance and the theoretical performance can be evaluated. Determine whether there is a situation where one or more actual parameter differences in the polarization beam splitter to be evaluated are greater than the corresponding parameter deviation threshold value. If such a situation exists, it means that the deviation degree between the actual performance and the theoretical performance exceeds the allowable range, then the polarization beam splitter to be evaluated is determined to be a polarization beam splitter that needs parameter tuning; if such a situation does not exist, that is, the deviation between the actual performance and the theoretical performance is within the allowable range, then the polarization beam splitter to be evaluated is determined to be a polarization beam splitter that does not need parameter tuning.

[0087] Through the above steps, it is possible to accurately determine whether the polarization beam splitter to be evaluated needs parameter tuning based on the test optical characteristic parameters and preset optical characteristic parameters of the polarization beam splitter to be evaluated, combined with the information on precision performance requirements. This method comprehensively considers the actual test results, theoretical expectations, and precision requirements, provides a scientific and reasonable determination mechanism for the performance evaluation of the polarization beam splitter, helps to ensure that the performance of the polarization beam splitter meets the expected requirements in actual applications, and improves the accuracy of quality control and performance optimization of the polarization beam splitter.

[0088] Preferably, if it is necessary to tune the parameters of the polarization beam splitter to be evaluated, obtain the optical characteristic parameters to be tuned of the polarization beam splitter that needs parameter tuning, specifically:

[0089] If the polarization beam splitter to be evaluated is determined to be a polarization beam splitter that needs parameter tuning, then calibrate the optical characteristic parameter corresponding to the actual parameter difference greater than the corresponding parameter deviation threshold value as the optical characteristic parameter to be tuned, and obtain the optical characteristic parameters to be tuned of the polarization beam splitter that needs parameter tuning.

[0090] Preferably, the singular structural characteristic parameters of the polarization beam splitter that needs to have its parameters optimized are determined according to the optical characteristic parameters to be optimized, specifically as follows:

[0091] Obtain the functional specification information of the polarization beam splitter that needs to have its parameters optimized, and obtain the linear influence degree of each structural characteristic parameter on each optical characteristic parameter in the polarization beam splitter that needs to have its parameters optimized according to the functional specification information;

[0092] Take each structural characteristic parameter as a quantitative node, and each optical characteristic parameter as a variable node; and determine the topological distance between each quantitative node and each variable node according to the linear influence degree of each structural characteristic parameter on each optical characteristic parameter; among them, the greater the linear influence degree of the structural characteristic parameter on the optical characteristic parameter, the smaller the corresponding topological distance;

[0093] Topologically connect each quantitative node and each variable node according to the linear distance between each quantitative node and each variable node, and obtain the topological structure diagram of the influence of each structural characteristic parameter on each optical characteristic parameter in the polarization beam splitter that needs to have its parameters optimized;

[0094] Obtain the optical characteristic parameters to be optimized of the polarization beam splitter that needs to have its parameters optimized, and retrieve the variable node corresponding to the optical characteristic parameter to be optimized in the topological structure diagram, which is defined as the target variable node;

[0095] Obtain the topological distance between the target variable node and the quantitative node in the topological structure diagram, and mark the quantitative node corresponding to the topological distance not greater than the preset distance threshold as the target quantitative node;

[0096] Obtain the structural characteristic parameter corresponding to the target quantitative node, and obtain the singular structural characteristic parameters of the polarization beam splitter that needs to have its parameters optimized.

[0097] It should be noted that when the polarization beam splitter to be evaluated is determined to need parameter optimization, the optical characteristic parameter corresponding to the actual parameter difference greater than the corresponding parameter deviation value threshold is calibrated as the optical characteristic parameter to be optimized. This is a direct and reasonable determination method, because those optical characteristic parameters with a large deviation between the actual value and the preset value are exactly the objects that need to be adjusted and optimized, thus clarifying the focus of the subsequent parameter optimization work.

[0098] First, obtain the functional specification information of the polarization beam splitter whose parameters need to be optimized, and from it, obtain the linear influence degree of each structural characteristic parameter on each optical characteristic parameter. This step is the basis for establishing the relationship between structural characteristics and optical characteristics, and the linear influence degree can reflect the influence law of the change of structural characteristics on optical characteristics. Take each structural characteristic parameter as a quantitative node and each optical characteristic parameter as a variable node, and determine the topological distance between each quantitative node and each variable node according to the linear influence degree (the greater the linear influence degree, the smaller the topological distance). Then, connect each quantitative node and each variable node topologically according to these topological distances, so as to obtain the topological structure diagram of the relationship between each structural characteristic parameter and each optical characteristic parameter in the polarization beam splitter whose parameters need to be optimized. This topological structure diagram visually shows the mutual relationship network between structural characteristic parameters and optical characteristic parameters. Obtain the optical characteristic parameters to be optimized of the polarization beam splitter whose parameters need to be optimized, retrieve the variable nodes corresponding to these optical characteristic parameters to be optimized in the topological structure diagram, and define them as target variable nodes. This step is to locate the nodes related to the optical characteristics to be optimized in the topological structure. Obtain the topological distance between the target variable node and the quantitative node in the topological structure diagram, and mark the quantitative nodes corresponding to the topological distance not greater than the preset distance threshold as target quantitative nodes. This step is to screen out the quantitative nodes closely related to the target variable node through the topological distance. Finally, obtain the structural characteristic parameters corresponding to the target quantitative nodes, and these structural characteristic parameters are the singular structural characteristic parameters of the polarization beam splitter whose parameters need to be optimized. Through such a series of steps, the structural characteristic parameters that have an important impact on the optical characteristics to be optimized are found from the relationship network between structural characteristics and optical characteristics.

[0099] Through the above steps, after determining that the parameters of the polarization beam splitter need to be optimized, the optical characteristic parameters to be optimized can be accurately found, and further, the singular structural characteristic parameters closely related to the optical characteristic parameters to be optimized can be determined by constructing a topological structure, analyzing topological relationships, etc. This method establishes a complete logical chain from the deviation of optical characteristic parameters to the optical characteristic parameters to be optimized, and then to the singular structural characteristic parameters, provides a precise basis for targeted parameter optimization of the polarization beam splitter, helps to improve the efficiency and accuracy of parameter optimization of the polarization beam splitter, and thus better meets its functional requirements.

[0100] Preferably, optimize the singular structural characteristic parameters of the polarization beam splitter whose parameters need to be optimized, specifically:

[0101] Obtain the structural model diagram of the polarization beam splitter whose parameters need to be optimized, perform format transformation processing on the structural model diagram of the polarization beam splitter whose parameters need to be optimized to obtain the finite element analysis model of the polarization beam splitter whose parameters need to be optimized, and import the finite element analysis model into the finite element analysis software;

[0102] Determine the key variables of the finite element analysis model according to the singular structure characteristic parameters of the polarization beam splitter to be optimized, and set the excitation source parameters in the finite element analysis software;

[0103] Obtain the preset process range of the singular structure characteristic parameters, define the upper boundary condition and the lower boundary condition of the singular structure characteristic parameters according to the preset process range, and discretize a number of discrete parameter values of the singular structure characteristic parameters according to the upper boundary condition and the lower boundary condition;

[0104] Replace the corresponding singular structure characteristic parameters in the finite element analysis model based on each discrete parameter value respectively, and perform polarization optical simulation test processing on the finite element analysis model after replacing the parameters to obtain the simulated optical characteristic parameters of the finite element analysis model;

[0105] Calculate the Pearson correlation coefficient value between the simulated optical characteristic parameters and the preset optical characteristic parameters. If the Pearson correlation coefficient value is not greater than the preset value, perform the next discrete parameter value replacement test operation, and continuously iterate this process until the obtained Pearson correlation coefficient value is greater than the preset value;

[0106] When the obtained Pearson correlation coefficient value is greater than the preset value, use the discrete parameter value corresponding to the corresponding replacement test operation as the optimized parameter value of the singular structure characteristic parameters;

[0107] Perform optimization processing on the polarization beam splitter that needs to be optimized based on the optimized parameter value.

[0108] It should be noted that the structural model diagram of the polarization beam splitter whose parameters need to be optimized is obtained, and it is processed by format transformation to obtain a finite element analysis model, and then imported into the finite element analysis software. This step is to convert the structural model of the polarization beam splitter into a form suitable for finite element analysis. The finite element analysis software can perform accurate numerical simulation calculations on the model, providing a calculation platform for subsequent parameter optimization. The key variables of the finite element analysis model are determined according to the singular structure characteristic parameters of the polarization beam splitter to be optimized. These key variables are the parts that need to be focused on and adjusted in the finite element analysis. At the same time, the excitation source parameters are set in the finite element analysis software. The excitation source parameters simulate the external excitation conditions in actual work, making the simulation closer to the real situation. The preset process range of the singular structure characteristic parameters is obtained, and the upper boundary condition and the lower boundary condition of the singular structure characteristic parameters are defined according to this range, and then several discrete parameter values are discretized within this range. The discretization process is to explore the value space of the singular structure characteristic parameters through a finite number of parameter values in order to find the optimal parameter value. The corresponding singular structure characteristic parameters in the finite element analysis model are replaced based on each discrete parameter value respectively, and then the polarization optical simulation test process is carried out on the finite element analysis model after parameter replacement to obtain the simulated optical characteristic parameters. Then, the Pearson correlation coefficient value between the simulated optical characteristic parameters and the preset optical characteristic parameters is calculated. The Pearson correlation coefficient is used to measure the linear correlation between two variables and is used here to evaluate the closeness between the simulation result and the preset requirements. If the Pearson correlation coefficient value is not greater than the preset value, it means that the simulation result does not meet the requirements, and then the next discrete parameter value replacement test operation is executed, and this process is iterated continuously until the obtained Pearson correlation coefficient value is greater than the preset value. This iterative process searches in the value space of the discrete parameter values to find the parameter values that meet the requirements. After the Pearson correlation coefficient value is greater than the preset value, the discrete parameter value corresponding to the corresponding replacement test operation is used as the optimized parameter value of the singular structure characteristic parameters. Finally, the polarization beam splitter whose parameters need to be optimized is optimized based on this optimized parameter value, so that the optical performance of the polarization beam splitter meets the preset requirements.

[0109] Through the above steps, the singular structure characteristic parameters of the polarization beam splitter whose parameters need to be optimized can be effectively optimized. From constructing the finite element analysis model to setting parameters, discrete value taking, simulation testing, iterative search, and finally determining the optimized parameter value and carrying out the optimization process, the whole process forms a complete parameter optimization process. This method can find the optimal structural parameter value that makes the optical performance of the polarization beam splitter meet the preset requirements through accurate numerical simulation and iterative search on the premise of meeting the preset process range, improving the accuracy and efficiency of the parameter optimization of the polarization beam splitter and ensuring the performance of the polarization beam splitter in actual applications.

[0110] The method for optimizing the parameters of this polarization beam splitter further includes the following steps:

[0111] Obtain the structural model diagram of the polarization beam splitter to be evaluated, perform feature extraction processing on the structural model diagram, and obtain the feature information of the defects in the polarization beam splitter to be evaluated; the defects include micro air holes, micro cracks, burrs, depressions, and protrusions; the feature information includes the defect positions and defect types;

[0112] Obtain the position coordinate information of each defect in the polarization beam splitter to be evaluated according to the feature information; introduce the Lagrange interpolation method, and regard each defect as a data point according to the position coordinate information of each defect in the polarization beam splitter to be evaluated;

[0113] Number the position coordinates of each defect in a preset order, and perform Lagrange interpolation operations on the abscissa and ordinate respectively in the position coordinate information of each defect;

[0114] Multiply the ordinate value corresponding to each abscissa point by the Lagrange basis function, and add all the product results to obtain the Lagrange interpolation result in the abscissa direction; similarly, perform the same operation on the ordinate to obtain the Lagrange interpolation result in the ordinate direction

[0115] Combine the Lagrange interpolation results in the abscissa and ordinate directions to construct the Lagrange interpolation curve of the defects in the polarization beam splitter to be evaluated;

[0116] Obtain the accuracy performance requirement information of the polarization beam splitter to be evaluated, and determine the limit defect concentration values of the positions of each sub-region in the polarization beam splitter to be evaluated according to the accuracy performance requirement information;

[0117] Fit the limit defect concentration values of the positions of each sub-region in the polarization beam splitter to be evaluated into the Lagrange interpolation curve according to the position coordinate relationship of the positions of each sub-region;

[0118] Compare the defect concentration value of each sub-region position on the fitted Lagrange interpolation curve with the limit defect concentration value of that sub-region. If the defect concentration value on the curve is greater than the limit defect concentration value, it means that the defect at that sub-region position does not meet the requirements, and mark it as the defective sub-region position;

[0119] Obtain the production equipment that has a production correlation with the defective sub-region position, and obtain the real-time production parameter information of the production equipment. Calculate the difference between the real-time production parameter information and the preset production parameter information to obtain the production parameter deviation value;

[0120] Adjust and optimize the real-time production parameter information corresponding to the production parameter deviation value greater than the preset deviation value threshold.

[0121] It should be noted that the structural model diagram of the polarization beam splitter to be evaluated is obtained, and the characteristic information of the defects (such as micro air holes, micro cracks, etc.) therein is obtained through feature extraction processing, including the defect positions and types. Based on the defect position information, their coordinates are obtained. Treating each defect as a data point, after numbering in a preset order, Lagrange interpolation operations are respectively performed on the horizontal and vertical coordinates. The interpolation results in the horizontal and vertical coordinate directions are combined to obtain the Lagrange interpolation curve of the defects. This step converts the discrete defect position information into a continuous curve representation, which helps to comprehensively analyze the distribution of defects in the polarization beam splitter. The accuracy performance requirement information of the polarization beam splitter to be evaluated is obtained, the limit defect concentration values at the positions of each sub-region are determined, and then they are fitted into the Lagrange interpolation curve according to the coordinate relationship of the sub-region positions. This makes the curve not only contain defect position information, but also incorporate the tolerance information of different regions for defect concentration, providing a basis for subsequent judgment of whether the defects meet the requirements. Comparing the defect concentration values of each sub-region on the fitted Lagrange interpolation curve with the limit defect concentration values, if the value on the curve is greater than the limit value, the defects in that sub-region do not meet the requirements and are marked as the positions of the defective sub-regions. This judgment is based on the curve constructed previously and the set limit value, and can accurately find the regions that do not meet the requirements. For the marked positions of the defective sub-regions, the production equipment associated with them in production is found, and the real-time production parameter information thereof is obtained. The difference between the real-time production parameter and the preset production parameter is calculated to obtain the production parameter deviation value. If the deviation value is greater than the preset deviation value threshold, the corresponding real-time production parameter is adjusted and optimized. This step establishes the connection between product defects and the production process, and improves product quality by adjusting production parameters. Through the steps of this method, the defect analysis of the polarization beam splitter to be evaluated can be comprehensively carried out, from defect feature extraction to constructing the interpolation curve, then to judging whether the defects meet the requirements, and finally tracing back to the production equipment and adjusting the parameters. It can not only accurately find the sub-regions that do not meet the requirements, but also solve problems from the production source, improving the production quality and product qualification rate of the polarization beam splitter.

[0122] The parameter tuning method for this polarization beam splitter further includes the following steps:

[0123] Obtain the standard texture feature images of the polarization beam splitter to be evaluated when working under various characteristic environmental conditions through the big data network;

[0124] Perform matrix transformation processing on each standard texture feature image to obtain the standard gray-level co-occurrence matrix corresponding to each standard texture feature image, and obtain the standard gray-level co-occurrence matrix of the polarization beam splitter to be evaluated when working under various characteristic environmental conditions;

[0125] Conduct robustness tests on the polarization beam splitter to be evaluated under various characteristic environmental conditions to obtain the actual texture feature images corresponding to the polarization beam splitter under various characteristic environmental conditions;

[0126] Perform matrix transformation processing on each actual texture feature image to obtain the actual gray-level co-occurrence matrix corresponding to the actual texture feature image, and obtain the actual gray-level co-occurrence matrix of the polarization beam splitter to be evaluated when working under various characteristic environmental conditions;

[0127] Calculate the structural similarity index between the actual gray-level co-occurrence matrix of the polarization beam splitter to be evaluated when working under various characteristic environmental conditions and the standard gray-level co-occurrence matrix respectively;

[0128] If the structural similarity index between the actual gray-level co-occurrence matrix of the polarization beam splitter to be evaluated when working under various characteristic environmental conditions and the standard gray-level co-occurrence matrix is greater than the preset index threshold, then determine the polarization beam splitter to be evaluated as a polarization beam splitter that does not require parameter tuning;

[0129] If the structural similarity index between the actual gray-level co-occurrence matrix of the polarization beam splitter to be evaluated when working under one or more characteristic environmental conditions and the standard gray-level co-occurrence matrix is not greater than the preset index threshold, then determine the polarization beam splitter to be evaluated as a polarization beam splitter that requires parameter tuning.

[0130] It should be noted that standard texture feature images of the polarization beam splitter to be evaluated under various characteristic environmental conditions are obtained using a big data network. These characteristic environmental conditions include different temperatures, humidities, light intensities, etc. Then, matrix transformation processing is performed on each standard texture feature image. Here, the matrix transformation is an operation that converts image data into a form that is more conducive to analysis. Through this processing, the standard gray-level co-occurrence matrix corresponding to each standard texture feature image is obtained. The gray-level co-occurrence matrix is a matrix used to describe the texture features of an image, and it can reflect the spatial distribution relationship of gray levels in the image. In this way, the standard gray-level co-occurrence matrices of the polarization beam splitter to be evaluated under various characteristic environmental conditions are obtained, and these matrices will be used as the benchmarks for subsequent judgments. A robustness test of the polarization beam splitter to be evaluated under various characteristic environmental conditions is carried out, and during the test, the actual texture feature images corresponding to the polarization beam splitter to be evaluated under various characteristic environmental conditions are obtained. Similar to obtaining the standard texture feature images, matrix transformation processing is performed on each actual texture feature image, so as to obtain the actual gray-level co-occurrence matrix corresponding to the actual texture feature image, and the actual gray-level co-occurrence matrices of the polarization beam splitter to be evaluated under various characteristic environmental conditions are obtained. This step is to obtain the texture feature data of the polarization beam splitter under the actual working environment. The structural similarity index between the actual gray-level co-occurrence matrix and the standard gray-level co-occurrence matrix of the polarization beam splitter to be evaluated under various characteristic environmental conditions is calculated respectively. The structural similarity index is an index that measures the similarity degree between two images (here, the texture features represented by the gray-level co-occurrence matrix). If the structural similarity index between the actual gray-level co-occurrence matrix and the standard gray-level co-occurrence matrix of the polarization beam splitter to be evaluated under various characteristic environmental conditions is greater than the preset index threshold, it indicates that under various environmental conditions, the actual texture features of the polarization beam splitter are very similar to the standard texture features, that is, its performance is relatively stable in different environments. Therefore, the polarization beam splitter to be evaluated is determined to be a polarization beam splitter that does not require parameter tuning. On the contrary, if the structural similarity index between the actual gray-level co-occurrence matrix and the standard gray-level co-occurrence matrix during the operation under one or more characteristic environmental conditions is not greater than the preset index threshold, this indicates that under these environmental conditions, the performance of the polarization beam splitter may deviate, and parameter tuning is required. Therefore, the polarization beam splitter to be evaluated is determined to be a polarization beam splitter that requires parameter tuning. By obtaining the standard and actual texture feature images of the polarization beam splitter to be evaluated under different characteristic environmental conditions, converting them into gray-level co-occurrence matrices and calculating the structural similarity index, the performance stability of the polarization beam splitter under different environments can be effectively evaluated, and it can be accurately judged whether the polarization beam splitter needs parameter tuning, providing a new approach based on texture feature analysis for the performance evaluation and optimization of the polarization beam splitter, which helps to improve the reliability and stability of the polarization beam splitter in complex environments.

[0131] The present invention also discloses a parameter tuning system for a polarization beam splitter based on micro-nano gratings, as Figure 3 shown. The parameter tuning system for the polarization beam splitter includes a memory 45 and a processor 55. A parameter tuning method program for the polarization beam splitter is stored in the memory 45. When the parameter tuning method program for the polarization beam splitter is executed by the processor 55, the steps of any one of the parameter tuning methods for the polarization beam splitter are implemented.

[0132] The above are only specific embodiments of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present invention can easily think of changes or substitutions, which should all be covered within the protection scope of the present invention.

Claims

1. A method for tuning parameters of a polarization beam splitter based on micro-nano grating, characterized in that: The following steps are involved: Obtaining standard structural characteristic parameters corresponding to various types of polarization beam splitters, and constructing a Huffman search tree model library according to the standard structural characteristic parameters corresponding to various types of polarization beam splitters; Obtaining a structural model diagram of the polarization beam splitter to be evaluated, importing the structural model diagram of the polarization beam splitter to be evaluated into a Huffman search tree model library for search and matching, and obtaining preset optical characteristic parameters of the polarization beam splitter to be evaluated; Obtaining test optical characteristic parameters of the polarization beam splitter to be evaluated, and determining whether parameter tuning of the polarization beam splitter to be evaluated is required according to the test optical characteristic parameters and preset optical characteristic parameters; If it is necessary to perform parameter tuning on the polarization beam splitter to be evaluated, the optical characteristic parameters to be tuned of the polarization beam splitter to be tuned are obtained; and the singular structural characteristic parameters of the polarization beam splitter to be tuned are determined according to the optical characteristic parameters to be tuned; Optimizing the singular structural characteristic parameters of the polarization beam splitter that needs parameter tuning, so that the optical performance of the polarization beam splitter that needs parameter tuning meets the preset requirements; Among them, standard structural characteristic parameters corresponding to various types of polarization beam splitters are obtained, and a Huffman search tree model library is constructed according to the standard structural characteristic parameters corresponding to various types of polarization beam splitters, specifically: Obtaining standard structural characteristic parameters corresponding to various types of polarization beam splitters, and constructing characteristic model diagrams corresponding to various types of polarization beam splitters according to the corresponding standard structural characteristic parameters; wherein the standard structural characteristic parameters include grating shape, grating width, grating spacing, and grating period; and obtaining preset optical characteristic parameters corresponding to various types of polarization beam splitters; wherein the preset optical characteristic parameters include polarization beam splitting efficiency, extinction ratio, bandwidth, incident angle range, polarization state purity, transmittance, reflectivity, angular resolution and polarization beam splitting angle; Constructing a Huffman tree, and recursively dividing various types of polarization beam splitters into corresponding numbers of subtrees in the Huffman tree; Encoding preset optical characteristic parameters corresponding to various types of polarization beam splitters to assign a string of digital codes to the preset optical characteristic parameters corresponding to various types of polarization beam splitters; The digital codes corresponding to various types of polarization beam splitters are engraved on the corresponding subtrees, and the characteristic model graphs corresponding to various types of polarization beam splitters are respectively stored on the corresponding subtrees to obtain a Huffman search tree model library.

2. The method for tuning parameters of a polarization beam splitter based on micro-nano grating according to claim 1, characterized in that: Obtain a structural model diagram of the polarization beam splitter to be evaluated, import the structural model diagram of the polarization beam splitter to be evaluated into the Huffman search tree model library for search and matching, and obtain preset optical characteristic parameters of the polarization beam splitter to be evaluated, specifically: Acquire laser point cloud data of the polarization beam splitter to be evaluated by a laser scanning device, and construct a structural model diagram of the polarization beam splitter to be evaluated according to the laser point cloud data; Extracting the characteristic model graph in each subtree from the Huffman search tree model library, introducing a bounding box algorithm, and calculating the degree of intersection between the structural model graph of the polarization beam splitter to be evaluated and the characteristic model graph in each subtree based on the bounding box algorithm; Extract the highest degree of intersection between the structural model graph of the polarization beam splitter to be evaluated and the characteristic model graphs in each subtree, and obtain the characteristic model graph to which the highest degree of intersection belongs; The subtree corresponding to the characteristic model graph to which the highest intersection degree belongs is identified, and the digital code of the identified subtree is obtained, and the digital code of the identified subtree is interpreted to obtain the preset optical characteristic parameters of the polarization beam splitter to be evaluated.

3. The method for tuning parameters of a polarization beam splitter based on micro-nano grating according to claim 1, characterized in that: Obtain the test optical characteristic parameters of the polarization beam splitter to be evaluated, and determine whether it is necessary to perform parameter tuning on the polarization beam splitter to be evaluated based on the test optical characteristic parameters and the preset optical characteristic parameters, specifically: Performing polarization optical testing on the polarization beam splitter to be evaluated based on an optical testing device to obtain test optical characteristic parameters of the polarization beam splitter to be evaluated; Acquiring precision performance requirement information of the polarization beam splitter to be evaluated, and determining parameter deviation value thresholds of various optical characteristic parameters of the polarization beam splitter to be evaluated according to the precision performance requirement information; Calculating actual parameter differences between various tested optical characteristic parameters of the polarization beam splitter to be evaluated and preset optical characteristic parameters; Determine whether one or more actual parameter differences in the polarization beam splitter to be evaluated are greater than a corresponding parameter deviation value threshold; If one or more actual parameter differences in the polarization beam splitter to be evaluated are greater than the corresponding parameter deviation threshold, the polarization beam splitter to be evaluated is determined to be a polarization beam splitter that needs parameter tuning; If there is no situation in the polarization beam splitter to be evaluated where the actual parameter difference is greater than the corresponding parameter deviation value threshold, the polarization beam splitter to be evaluated is determined as a polarization beam splitter that does not require parameter tuning; The tested optical characteristic parameters include polarization splitting efficiency, extinction ratio, bandwidth, incident angle range, polarization state purity, transmittance, reflectivity, angular resolution and polarization splitting angle.

4. The method for tuning parameters of a polarization beam splitter based on micro-nano grating according to claim 3, characterized in that: If it is necessary to perform parameter tuning on the polarization beam splitter to be evaluated, obtain the optical characteristic parameters to be tuned of the polarization beam splitter to be tuned, specifically: If the polarization beam splitter to be evaluated is determined to be a polarization beam splitter that requires parameter tuning, the optical characteristic parameters corresponding to the actual parameter difference values ​​that are greater than the corresponding parameter deviation value threshold are calibrated as the optical characteristic parameters to be tuned, thereby obtaining the optical characteristic parameters to be tuned of the polarization beam splitter that requires parameter tuning.

5. The method for tuning parameters of a polarization beam splitter based on micro-nano grating according to claim 1, characterized in that: The singular structural characteristic parameters of the polarization beam splitter that need to be parameter-tuned are determined according to the optical characteristic parameters to be tuned, specifically: Obtaining functional specification information of the polarization beam splitter that needs parameter tuning, and obtaining the degree of linear influence of each structural characteristic parameter on each optical characteristic parameter in the polarization beam splitter that needs parameter tuning according to the functional specification information; Each structural characteristic parameter is used as a quantitative node, and each optical characteristic parameter is used as a variable node; The topological distance between each quantitative node and each variable node is determined according to the linear influence degree between each structural characteristic parameter on each optical characteristic parameter; wherein, the greater the linear influence degree of the structural characteristic parameter on the optical characteristic parameter, the smaller the corresponding topological distance; Topologically connect each quantitative node with each variable node according to the linear distance between each quantitative node and each variable node, and obtain a topological structure diagram between each structural characteristic parameter and each optical characteristic parameter in the polarization beam splitter that needs parameter tuning; Obtaining optical characteristic parameters to be tuned of the polarization beam splitter that needs parameter tuning, and retrieving a variable node corresponding to the optical characteristic parameters to be tuned in the topological structure diagram, and defining the node as a target variable node; Obtaining the topological distance between the target variable node and the quantitative node in the topological structure diagram, and marking the quantitative node corresponding to the topological distance not greater than the preset distance threshold as the target quantitative node; The structural characteristic parameters corresponding to the target quantitative nodes are obtained, and the singular structural characteristic parameters of the polarization beam splitter that need to be parameter tuned are obtained.

6. The method for tuning parameters of a polarization beam splitter based on micro-nano grating according to claim 1, characterized in that: The singular structural characteristic parameters of the polarization beam splitter that needs parameter tuning are optimized, specifically: Obtaining a structural model diagram of a polarization beam splitter that needs parameter tuning, performing format conversion processing on the structural model diagram of the polarization beam splitter that needs parameter tuning, obtaining a finite element analysis model of the polarization beam splitter that needs parameter tuning, and importing the finite element analysis model into finite element analysis software; Determine key variables of the finite element analysis model according to the singular structural characteristic parameters of the polarization beam splitter to be tuned, and set the excitation source parameters in the finite element analysis software; Acquire a preset process range of the characteristic parameter of the singular structure, define an upper boundary condition and a lower boundary condition of the characteristic parameter of the singular structure according to the preset process range, and discretize a plurality of discrete parameter values ​​of the characteristic parameter of the singular structure according to the upper boundary condition and the lower boundary condition; Replacing corresponding singular structural characteristic parameters in the finite element analysis model based on respective discrete parameter values, and performing polarization optical simulation test processing on the finite element analysis model after the parameter replacement to obtain simulated optical characteristic parameters of the finite element analysis model; Calculating the Pearson correlation coefficient value between the simulated optical characteristic parameter and the preset optical characteristic parameter, and if the Pearson correlation coefficient value is not greater than the preset value, performing the next discrete parameter value replacement test operation, and continuously iterating this process until the obtained Pearson correlation coefficient value is greater than the preset value; When the obtained Pearson correlation coefficient value is greater than the preset value, the discrete parameter value corresponding to the corresponding replacement test operation is used as the tuning parameter value of the singular structure characteristic parameter; The polarization beam splitter that needs parameter tuning is tuned based on the tuning parameter value.

7. A polarization beam splitter parameter tuning system based on micro-nano grating, characterized in that: The polarization beam splitter parameter tuning system includes a memory and a processor, wherein the memory stores a polarization beam splitter parameter tuning method program, and when the polarization beam splitter parameter tuning method program is executed by the processor, the steps of the polarization beam splitter parameter tuning method as described in any one of claims 1 to 6 are implemented.

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