Brightness enhancement film center wavelength optimization method based on genetic algorithm and electronic equipment

Abstract

The invention relates to the technical field of optical thin films, in particular to a brightness enhancement film center wavelength optimization method based on a genetic algorithm and electronic equipment. The method comprises the steps that preset environment parameters are acquired, and optimization variable parameters are defined; based on the preset environment parameters and the optimization variable parameters, performing parameterized inverse design on the multilayer film model by adopting finite element simulation to obtain a target function; based on the objective function, adopting a genetic algorithm to execute iterative simulation on the multilayer film model, and performing optimization by utilizing a fitness function in an iteration process until an optimization objective is met or the number of iterations reaches a preset number of iterations; and obtaining an optimal solution after iteration is completed, wherein the optimal solution comprises a central wavelength combination and the number of film layers. According to the method and the device, accurate optimization parameters can be obtained, so that the reflectivity performance of the multi-layer film is optimal.

Description

Technical Field

[0001] The present application relates to the technical field of optical thin films, and in particular, to a method for optimizing the central wavelength of a brightness enhancement film based on a genetic algorithm and an electronic device. Background Art

[0002] Optical multilayer films are widely used in fields such as brightness enhancement films, filters, interferometers, antireflection coatings, etc. Their reflection and transmission properties usually depend on parameters such as film thickness, film material, and arrangement order. Among them, the central wavelength is a key parameter for adjusting the reflectivity of the multilayer film. A Dual Brightness Enhancement Film (DBEF) is a multilayer film that uses optical interference technology to improve the brightness of a display. Its main function is to use the technology of optical interference to recover the S wave that does not pass through the polarizer and convert it into a P wave, so that in addition to improving the brightness of the front of the display, the brightness of the viewing angle can also be increased.

[0003] Optimizing the central wavelength not only affects the thickness and stacking layers of the film, but also directly determines the optical properties of the film in the target band. In optical design, the setting of the central wavelength is a complex calculation problem, and with the increase in the number of layers or the expansion of the bandwidth, the optimization difficulty significantly increases.

[0004] Related technologies use high-precision optical testing equipment (such as synchrotron radiation light sources) to test the reflectivity of multilayer films, so as to find the optimized central parameters. However, this method relies on manual experiments and is prone to deviation, resulting in inaccurate experimental results. Summary of the Invention

[0005] In order to solve the problem that the accuracy of the central wavelength parameters determined by the existing technology to achieve the best reflectivity performance is not high, the present application provides a method for optimizing the central wavelength of a brightness enhancement film based on a genetic algorithm and an electronic device.

[0006] In a first aspect, the present application provides a method for optimizing the central wavelength of a brightness enhancement film based on a genetic algorithm, adopting the following technical solution: A method for optimizing the central wavelength of a brightness enhancement film based on a genetic algorithm, comprising: Obtaining preset environmental parameters and defining optimization variable parameters; Based on the preset environmental parameters and the optimization variable parameters, performing parametric inverse design on the multilayer film model by using finite element simulation to obtain an objective function; Based on the objective function, performing iterative simulation on the multilayer film model by using a genetic algorithm and optimizing it by using a fitness function during the iteration until the optimization target is met or the number of iterations reaches a preset number of iterations; Obtaining the optimal solution after the iteration is completed, where the optimal solution includes a central wavelength combination and the number of film layers.

[0007] By adopting the above technical solution, preset environmental parameters (such as multi-layer film structure parameters, wavelength range, target reflectivity, etc.) are obtained and optimization variable parameters (such as central wavelength combination and number of film layers) are defined. Parametric inverse design of the multi-layer film model is carried out by finite element simulation to construct an objective function. Based on the objective function, the genetic algorithm is used to perform iterative simulation on the multi-layer film model. The central wavelength combination is evaluated and optimized through the fitness function, gradually approaching the optimal solution. The finally obtained optimal solution includes the central wavelength combination and the number of film layers, ensuring that the reflectivity of the multi-layer film within the target wavelength range reaches the optimum. This application can efficiently and accurately optimize the multi-layer film design, meet complex optical performance requirements, and at the same time avoid falling into local optimal solutions through the global search ability of the genetic algorithm, improving the accuracy of parameter optimization.

[0008] In a preferred example, this application can be further configured as: the obtaining of the preset environmental parameters and the definition of the optimization variable parameters include: Obtain the preset environmental parameters, where the preset environmental parameters include: the structure parameters of the multi-layer film model, the wavelength range parameters, and the target reflectivity; Define a preset number of central wavelengths as the central wavelength combination, and use the central wavelength combination and the number of film layers as the optimization variable parameters; Among them, the multi-layer film model is composed of alternately stacked high-refractive-index films and low-refractive-index films. The number of groups of the multi-layer film model is equal to the preset number, and the sum of the number of high-refractive-index films and low-refractive-index films included in each group is the number of film layers.

[0009] By adopting the above technical solution, obtaining the preset environmental parameters and defining the optimization variable parameters provide clear input conditions for subsequent optimization design. The multi-layer film model is composed of alternately stacked high-refractive-index films and low-refractive-index films, and the number of groups of the multi-layer film model is consistent with the preset number of central wavelengths, ensuring the structural rationality and optimizability of the model. Through this step, the complex optical thin film design problem can be transformed into a computable optimization problem, laying a foundation for subsequent finite element simulation and genetic algorithm optimization, so as to achieve efficient and accurate multi-layer film design and meet the target reflectivity requirements.

[0010] In a preferred example, this application can be further configured as: the structure parameters of the multi-layer film model include: the refractive index of the high-refractive-index film, the refractive index of the low-refractive-index film, the substrate refractive index, and the total film thickness range; the wavelength range parameters include: the traversal wavelength range and the central wavelength range; Based on the preset environmental parameters and the optimization variable parameters, parametric inverse design of the multi-layer film model is carried out by finite element simulation to obtain an objective function, including: Apply sequential constraints to the central wavelength combination; Construct the total system transfer matrix of the multilayer film model based on the central wavelength combination; Obtain the air refractive index, and calculate the reflectivity based on the total system transfer matrix, the air refractive index, and the substrate refractive index; construct an objective function, and the expression of the objective function is: F = (λ c , n1, n2, n air , n sub , λ, H, L, tR, λ s , λ e ) where F is the objective function, λ c is the central wavelength combination, n1 is the refractive index of the high-refractive-index film, n2 is the refractive index of the low-refractive-index film, n air is the air refractive index, n sub is the substrate refractive index, λ is the traversed wavelength range, H is the total thickness of the film layer, L is the number of film layers, tR is the target reflectivity, λ s is the upper bound of the central wavelength range, and λ e is the lower bound of the central wavelength range.

[0011] By adopting the above technical solutions, the structural parameters and wavelength range parameters of the multilayer film model are clarified. Based on these parameters, parametric inverse design of the multilayer film model is carried out by finite element simulation. By imposing the sequential constraint of the central wavelength combination, constructing the total system transfer matrix and calculating the reflectivity, an objective function is finally constructed. The objective function comprehensively considers factors such as the central wavelength combination, material refractive index, air refractive index, substrate refractive index, traversed wavelength range, total thickness of the film layer, number of film layers, and target reflectivity, and can comprehensively reflect the optical performance of the multilayer film.

[0012] In a preferred example of the present application, it can be further configured that: the method further includes: Impose central wavelength constraints and total film thickness constraints on the objective function; wherein, the central wavelength constraint is that the central wavelength combination in the objective function is within the central wavelength range; the total film thickness constraint is that the total film thickness in the objective function is within the total film thickness range, and the total film thickness is determined based on the central wavelength combination, the refractive index of the high-refractive-index film, the refractive index of the low-refractive-index film, and the number of film layers.

[0013] By adopting the above technical solutions, central wavelength constraints and total film thickness constraints are imposed on the objective function to ensure that the central wavelength combination and the total film thickness are always within the preset reasonable ranges during the optimization process. The central wavelength constraint ensures that the central wavelength combination is arranged in an orderly manner within the target wavelength range, avoiding the generation of invalid solutions; the total film thickness constraint ensures that the total thickness of the multilayer film meets the physical limitations of actual manufacturing and application.

[0014] In a preferred example, the present application can be further configured as follows: The total system transfer matrix for constructing the multi-layer film model based on the central wavelength combination includes: For each group in the multi-layer film model, calculate the first thickness of the high-refractive-index film and the second thickness of the low-refractive-index film in the group based on the central wavelength combination; construct the first transfer matrix of the group based on the first thickness, construct the second transfer matrix of the group based on the second thickness, and construct the transfer matrix of the group based on the first transfer matrix, the second transfer matrix, and the number of film layers; Calculate the product of the transfer matrices of the preset number of groups to obtain the total system transfer matrix of the multi-layer film model.

[0015] By adopting the above technical solution, calculate the thicknesses of the high-refractive-index film and the low-refractive-index film in each group based on the central wavelength combination, and construct their transfer matrices respectively. Finally, obtain the total transfer matrix of each layer of film through matrix multiplication, and then multiply the transfer matrices of all film layers to obtain the total system transfer matrix of the multi-layer film model. This process can accurately describe the propagation behavior of light in the multi-layer film, and efficiently calculate the reflectivity and transmittance in combination with the transfer matrix method, providing an accurate mathematical model for the optimization design.

[0016] In a preferred example, the present application can be further configured as follows: The calculation of the reflectivity based on the total system transfer matrix, the air refractive index, and the substrate refractive index includes: Substitute the total system transfer matrix, the air refractive index, and the substrate refractive index into the reflection coefficient formula to obtain the reflection coefficient. The reflection coefficient formula is: where r is the reflection coefficient, T is the total system transfer matrix, n air is the air refractive index, and n sub is the substrate refractive index; Substitute the reflection coefficient into the reflectivity formula to obtain the reflectivity. The reflectivity formula is: R = |r| 2 where R is the reflectivity and r is the reflection coefficient.

[0017] By adopting the above technical solution, based on the transfer matrix algorithm, it can accurately describe the propagation behavior of light in the multi-layer film, and efficiently calculate the reflectivity through strict mathematical formulas, providing a high-precision reflectivity calculation method and a reliable quantitative basis for the optical performance evaluation and optimization design of the multi-layer film. In a preferred example, the present application can be further configured as follows: Based on the objective function, a genetic algorithm is used to perform iterative simulation on the multilayer film model, and an adaptation function is used for optimization during the iteration until the optimization objective is met or the number of iterations reaches a preset number of iterations, including: Randomly generate an initial population based on the objective function; Using the adaptation function, calculate the adaptation value of each individual in the initial population based on the objective function value corresponding to each individual in the initial population, and the adaptation value is the mean of the reflectivities corresponding to the central wavelength range; Perform iterative optimization operations until the adaptation value obtained in the current iteration round meets the optimization objective or the number of iterations reaches the preset number of iterations; Among them, the iterative optimization operation includes: taking the population obtained in the previous iteration round as the parent population, performing selection operation, crossover operation and mutation operation on the parent population based on the adaptation value of the parent population to obtain a new population and an updated objective function value, and calculating the adaptation value of each individual obtained in the current iteration round based on the updated objective function value; determining a penalty factor, and updating the adaptation value of each individual obtained in the current iteration round based on the penalty factor.

[0018] By adopting the above technical solution, the global search ability of the genetic algorithm is utilized to efficiently explore the complex design space, avoid falling into local optimal solutions, and at the same time ensure that the optimization result meets the target reflectivity requirements and physical constraints through the adaptation function and the penalty factor, and finally achieve high-precision optimization of the multilayer film design.

[0019] In a preferred example, the present application can be further configured as follows: The determining the penalty factor and updating the adaptation value of each individual obtained in the current iteration round based on the penalty factor includes: For each individual obtained in the current iteration round, calculate the difference between the adaptation value of the individual and the target reflectivity; calculate the product of the difference and the adjustment coefficient as the penalty factor, and adjust the adaptation value of the individual based on the penalty factor.

[0020] By adopting the above technical solution, by introducing a penalty mechanism, the optimization process is effectively guided to move in the direction of meeting the target reflectivity, avoiding the generation of solutions that do not meet the requirements by the algorithm, and at the same time improving the optimization efficiency and the quality of the solutions.

[0021] In a preferred example, the present application can be further configured as follows: The method further includes: After the iteration is completed, determine the reflectivity of the traversed wavelength range corresponding to the optimal solution, and draw a reflectivity curve based on the reflectivity of the traversed wavelength range; Select the reflectivity corresponding to the central wavelength range from the reflectivities of the traversed wavelength range, and calculate the mean reflectivity and the standard deviation of the reflectivity based on the reflectivity corresponding to the central wavelength range, so as to verify the iteration result through the mean reflectivity and the standard deviation of the reflectivity.

[0022] By adopting the above technical solution, the quality of the optimization result is comprehensively evaluated by using the reflectivity curve and the quantization indexes (mean value and standard deviation), so as to ensure that the reflectivity of the multilayer film within the target wavelength range reaches the optimum and is evenly distributed.

[0023] In a second aspect, the present application provides an electronic device, adopting the following technical solution: One or more processors; A memory; At least one application program, wherein at least one application program is stored in the memory and is configured to be executed by at least one processor, and the at least one application program is configured to: execute the method for optimizing the central wavelength of the brightness enhancement film based on the genetic algorithm as described in any item of the first aspect.

[0024] In a third aspect, the present application provides a computer-readable storage medium, adopting the following technical solution: A computer-readable storage medium, on which a computer program is stored. When the computer program is executed on a computer, the computer is made to execute the method for optimizing the central wavelength of the brightness enhancement film based on the genetic algorithm as described in any item of the first aspect.

[0025] In a fourth aspect, the present application provides a computer program product, adopting the following technical solution: A computer program product includes a computer program. When the computer program is executed by a processor, the method for optimizing the central wavelength of the brightness enhancement film based on the genetic algorithm as described in any item of the first aspect is implemented.

[0026] In summary, the present application includes the following beneficial technical effects: The present application obtains preset environmental parameters (such as multilayer film structure parameters, wavelength range, target reflectivity, etc.) and defines optimization variable parameters (such as central wavelength combination and number of film layers), performs parametric inverse design on the multilayer film model by using finite element simulation, and constructs an objective function; based on the objective function, uses the genetic algorithm to perform iterative simulation on the multilayer film model, evaluates and optimizes the central wavelength combination through the fitness function, and gradually approaches the optimal solution; the finally obtained optimal solution includes the central wavelength combination and the number of film layers, ensuring that the reflectivity of the multilayer film within the target wavelength range reaches the optimum; the present application can efficiently and accurately optimize the design of the multilayer film, meet complex optical performance requirements, and at the same time avoid falling into local optimal solutions through the global search ability of the genetic algorithm, improving the accuracy of parameter optimization. Description of the Drawings

[0027] Figure 1 It is a schematic flow chart of an optimization method for the central wavelength of a brightness enhancement film based on a genetic algorithm provided by an embodiment of the present application; Figure 2 It is a schematic structural diagram of a multilayer film provided by an embodiment of the present application; Figure 3 It is a schematic flow chart of an optimization method for the central wavelength of a brightness enhancement film based on a genetic algorithm provided by another embodiment of the present application; Figure 4 It is a convergence graph of the iterative process of the genetic algorithm provided by an embodiment of the present application; Figure 5 It is a schematic diagram of a reflectivity curve provided by an embodiment of the present application; Figure 6 It is a schematic structural diagram of an electronic device provided by an embodiment of the present application. Detailed implementation manners

[0028] The following will further describe the present application in detail Figure 1 - appended Figure 6 with reference to the appended drawings.

[0029] This specific embodiment is only an interpretation of the present application and does not limit the present application. After reading this specification, those skilled in the art can make modifications to this embodiment without creative contributions as needed, but as long as it is within the scope of the claims of the present application, it is protected by the patent law.

[0030] To make the objectives, technical solutions, and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are some, but not all, of the embodiments of the present application. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present application without making creative efforts fall within the scope of protection of the present application.

[0031] In addition, the term "and / or" in this article is only a description of the association relationship of associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist simultaneously, and B exists alone. In addition, the character " / " in this article generally represents an "or" relationship between the associated objects before and after, unless otherwise specified.

[0032] It should be noted that in the optional embodiments of the present application, for relevant data such as object information, when the embodiments in the present application are applied to specific products or technologies, object permission or consent needs to be obtained, and the collection, use, and processing of relevant data need to comply with relevant laws, regulations, and standards of relevant countries and regions. That is to say, if the embodiments in the present application involve data related to an object, it needs to be obtained under the authorization and consent of the object, the authorization and consent of relevant departments, and in compliance with relevant laws, regulations, and standards of the country and region. In the embodiments, if personal information is involved, the acquisition of all personal information needs to obtain the consent of the individual. If sensitive information is involved, the separate consent of the information subject needs to be obtained, and the embodiments also need to be implemented under the authorization and consent of the object.

[0033] The method for optimizing the central wavelength of a brightness enhancement film based on a genetic algorithm provided by the embodiments of the present application can be widely applied to fields such as the design of brightness enhancement films for liquid crystal displays, the design of optical filters, and the optimization of antireflection coatings, and has practical production application value.

[0034] The embodiments of the present application provide a method for optimizing the central wavelength of a brightness enhancement film based on a genetic algorithm, as Figure 1 shown. In the method provided in the embodiments of the present application, it is executed by an electronic device, and the electronic device can be a server or a terminal device. Among them, the server can be an independent physical server, a server cluster or a distributed system composed of multiple physical servers, or a cloud server providing cloud computing services. The terminal device can be a smart phone, a tablet computer, a notebook computer, a desktop computer, etc., but is not limited thereto. The terminal device and the server can be directly or indirectly connected through wired or wireless communication methods, and the embodiments of the present application do not make limitations here.

[0035] A dual brightness enhancement film (DBEF) is a multi-layer film. When the structural parameters of the multi-layer film are known, the reflectivity of the multi-layer film can be calculated, which is a forward problem. On the contrary, the present application adopts an inverse design method for the structural parameters of a birefringent multi-layer film based on a genetic algorithm. When the target refractive index of the multi-layer film is known, the structural parameters of the multi-layer film (central wavelength combination and number of film layers) are inversely designed. Based on the central wavelength combination and the number of film layers obtained by the optimal design, and combined with the refractive indices of the high-refractive-index film and the low-refractive-index film, the total thickness of the film layer can be calculated.

[0036] The method for optimizing the central wavelength of a brightness enhancement film based on a genetic algorithm provided by the present application includes steps S101 - S104, where: S101. Obtain preset environmental parameters and define optimization variable parameters.

[0037] Specifically, the preset environmental parameters are preset and include: the structural parameters of the multi-layer film model, the wavelength range parameters, and the target reflectivity.

[0038] See Figure 2 , which shows a schematic structural diagram of a multilayer film. The multilayer film is composed of a plurality of high-refractive-index films and low-refractive-index films that are periodically stacked alternately on a substrate material. The number of groups of the multi-film layer structure is a preset number N, that is Figure 2 Groups 1 - N in [[ ]]. Each group is composed of a stacked high-refractive-index film and a low-refractive-index film. The sum of the number of high-refractive-index films and low-refractive-index films in each group is the number of film layers. The model parameters of the multilayer film include: the refractive index of the high-refractive-index film, the refractive index of the low-refractive-index film, the substrate refractive index, and the total film thickness range. The refractive index of the high-refractive-index film, the refractive index of the low-refractive-index film, and the substrate refractive index are determined by the material properties of the multilayer film. The total film thickness range indicates that the finally designed total film thickness needs to be restricted within the total film thickness range. Optionally, the central value of the total film thickness range can be set to 30μm, and the preset number N is 9.

[0039] The wavelength range parameters include: the traversal wavelength range and the central wavelength range. According to the wavelength range of visible light, the traversal wavelength range can be determined as 300nm - 1000nm. The central wavelength range is set based on the actual application requirements of the brightness enhancement film. The central wavelength range is used as the target wavelength range. The purpose of the solution is to optimize the reflectivity of the multilayer film within the target wavelength range. Optionally, the central wavelength range is 400nm - 850nm.

[0040] The traversal wavelength and the simulation process are key steps in optimizing the design of the multilayer film. By simulating each wavelength value within the set traversal wavelength range, including: setting a wavelength step such as 1nm, starting from the lower bound of 300nm of the traversal wavelength range, and sequentially increasing the wavelength values (301nm, 302nm,..., 1000nm), and performing simulation calculations for each wavelength value. The simulation process calculates the reflectivity of the multilayer film at each wavelength value through the transfer matrix algorithm, and iteratively optimizes the central wavelength combination and the number of film layers through the genetic iteration algorithm, so that the reflectivity of the multilayer film within the target wavelength range (central wavelength range) reaches the optimum and meets the target reflectivity. Finally, the optimal central wavelength combination and the number of film layers are output as the optimal solution. The target reflectivity is the optimal value that the reflectivity of the multilayer film can reach by optimizing the central wavelength, and the target reflectivity can be set to 0.98.

[0041] The optimization variable parameters are the parameters that are dynamically optimized during the iteration process. The finally output optimal solution includes the central wavelength combination and the number of film layers that can make the reflectivity of the multilayer film reach the optimum. Among them, the central wavelength combination includes N preset central wavelengths.

[0042] S102. Based on the preset environmental parameters and the optimization variable parameters, perform parametric inverse design on the multilayer film model using finite element simulation to obtain the objective function.

[0043] Specifically, this solution uses a genetic algorithm to find N central wavelengths, which are used to calculate the film thickness. A sequential constraint is imposed on the combination of central wavelengths. The sequential constraint means that the N central wavelengths are arranged in ascending order, aiming to simulate the actual film arrangement in numerical simulation, that is, the film thickness is arranged in ascending order. The sequential constraint can be expressed as: where, λ c1 to λ cN represents a combination of central wavelengths arranged in ascending order. The upper and lower bounds of the central wavelengths are the upper and lower bounds of the central wavelength range. Setting matrix A and matrix B satisfies the sequential constraint. The element δ in matrix B is an infinitesimal value.

[0044] Furthermore, the refractive index of air is obtained. Based on the refractive index of air, the refractive index of the high-refractive-index film, the refractive index of the low-refractive-index film, the refractive index of the substrate, and the combination of central wavelengths, the total reflectivity of the multilayer film is calculated.

[0045] Construct an objective function. The expression of the objective function is: F = (λ c , n1, n2, n air , n sub , λ, H, L, tR, λ s , λ e ) where, F is the objective function, λ c is the combination of central wavelengths, n1 is the refractive index of the high-refractive-index film, n2 is the refractive index of the low-refractive-index film, n air is the refractive index of air, n sub is the refractive index of the substrate, λ is the traversed wavelength range, H is the total film thickness, L is the number of film layers, tR is the target reflectivity, λ s is the upper bound of the central wavelength range, and λ e is the lower bound of the central wavelength range.

[0046] S103. Based on the objective function, use a genetic algorithm to perform iterative simulation on the multilayer film model, and use the fitness function for optimization during the iteration until the optimization goal is met or the number of iterations reaches the preset number of iterations.

[0047] Specifically, based on the genetic algorithm, an initial population is generated, and the combination of central wavelengths and the number of film layers are gradually optimized through iterative optimization operations. During this process, the value of the objective function also changes dynamically. The iterative optimization operations include selection operations, crossover operations, and mutation operations. Through the optical transfer matrix theory, use the fitness function to evaluate whether the reflectivity of the multilayer film meets the target reflectivity and adjust the optimization direction.

[0048] S104. Obtain the optimal solution after the iteration is completed. The optimal solution includes the combination of central wavelengths and the number of film layers.

[0049] After the iteration is completed, a reflectivity curve is plotted based on the optimal solution, and the mean reflectivity and the standard deviation of the reflectivity are calculated to verify the iteration results through the mean reflectivity and the standard deviation of the reflectivity.

[0050] See Figure 3 , which shows a schematic flow chart of a method for optimizing the central wavelength of a brightness enhancement film based on a genetic algorithm provided in another embodiment of the present application. Given preset environmental parameters and optimization variable parameters, including refractive index parameters, total film thickness, traversal wavelength range, central wavelength range, target reflectivity requirements, etc. A target function is constructed based on the transfer matrix. Set constraints and iteration conditions, including upper and lower bounds of the central wavelength, population size, maximum number of iterations, and use the genetic algorithm for iterative optimization. The iterative optimization process includes population selection, crossover, and mutation. The fitness function is used to evaluate whether the obtained population meets the target reflectivity requirements. If it meets, the optimal solution is output, and the optimal solution includes the central wavelength combination and the number of film layers, and the reflectivity is calculated and the reflectivity curve is plotted; if it does not meet, judge the magnitude of the deviation, assign a penalty value according to the deviation, and then perform the next round of iteration until the optimization goal is met or the maximum number of iterations is reached. After the iteration is completed, a Pareto (Pareto optimal) solution set is obtained.

[0051] In the embodiment of the present application, by obtaining preset environmental parameters (such as multi-layer film structure parameters, wavelength range, target reflectivity, etc.) and defining optimization variable parameters (such as central wavelength combination and number of film layers), a parametric inverse design of the multi-layer film model is carried out by finite element simulation to construct a target function; based on the target function, the genetic algorithm is used to perform iterative simulation on the multi-layer film model, and the central wavelength combination is evaluated and optimized through the fitness function, gradually approaching the optimal solution; the finally obtained optimal solution includes the central wavelength combination and the number of film layers, ensuring that the reflectivity of the multi-layer film within the target wavelength range reaches the optimum; the present application can efficiently and accurately optimize the multi-layer film design, meet complex optical performance requirements, and at the same time avoid falling into local optimal solutions through the global search ability of the genetic algorithm, improving the accuracy of parameter optimization.

[0052] A possible implementation manner of the embodiment of the present application is to obtain preset environmental parameters and define optimization variable parameters, including: Obtain preset environmental parameters, and the preset environmental parameters include: structural parameters of the multi-layer film model, wavelength range parameters, and target reflectivity; Define a preset number of central wavelengths as the central wavelength combination, and use the central wavelength combination and the number of film layers as optimization variable parameters; wherein, the multi-layer film model is composed of alternately stacked high-refractive-index films and low-refractive-index films, the number of groups of the multi-layer film model is equal to the preset number, and the sum of the number of high-refractive-index films and low-refractive-index films included in each group is the number of film layers.

[0053] In the embodiments of the present application, by obtaining preset environmental parameters and defining optimization variable parameters, clear input conditions are provided for subsequent optimization design; the multi-layer film model is composed of alternating stacks of high-refractive-index films and low-refractive-index films, and the number of groups of the multi-layer film model is consistent with the number of preset central wavelengths, ensuring the structural rationality and optimizability of the model; through this step, complex optical thin film design problems can be transformed into computable optimization problems, laying a foundation for subsequent finite element simulation and genetic algorithm optimization, so as to achieve efficient and accurate multi-layer film design and meet the target reflectivity requirements.

[0054] In a possible implementation manner of the embodiments of the present application, the structural parameters of the multi-layer film model include: the refractive index of the high-refractive-index film, the refractive index of the low-refractive-index film, the substrate refractive index, and the total film thickness range; the wavelength range parameters include: the traversal wavelength range and the central wavelength range; Based on the preset environmental parameters and optimization variable parameters, parametric inverse design of the multi-layer film model is carried out by finite element simulation to obtain the objective function, including: Applying sequential constraints to the central wavelength combination; Based on the central wavelength combination, constructing the total system transmission matrix of the multi-layer film model; Obtaining the air refractive index, and calculating the reflectivity based on the total system transmission matrix, the air refractive index, and the substrate refractive index; Constructing the objective function, and the expression of the objective function is: F=(λ c ,n1,n2,n air ,n sub ,λ,H,L,tR,λ s ,λ e ) Wherein, F is the objective function, λ c is the central wavelength combination, n1 is the refractive index of the high-refractive-index film, n2 is the refractive index of the low-refractive-index film, n air is the air refractive index, n sub is the substrate refractive index, λ is the traversal wavelength range, H is the total film thickness, L is the number of film layers, tR is the target reflectivity, λ s is the upper bound of the central wavelength range, λ e is the lower bound of the central wavelength range.

[0055] The purpose of this embodiment is to calculate the total reflectivity of the multi-layer film through the combination of the refractive index of the high-refractive-index film, the refractive index of the low-refractive-index film, the total film thickness, and the central wavelength parameters, and meet the target reflectivity requirements.

[0056] Specifically, constructing the total transmission matrix of the multilayer film model based on the central wavelength combination includes: for each group in the multilayer film model, calculating the first transmission matrix corresponding to the high-refractive-index film and the second transmission matrix of the low-refractive-index film in the group based on the corresponding central wavelength, constructing the transmission matrix of the group based on the first transmission matrix, the second transmission matrix, and the number of film layers. Furthermore, taking the product of the transmission matrices of all groups in the multilayer film model as the total system transmission matrix of the multilayer film model.

[0057] Furthermore, substituting the total system transmission matrix, the refractive index of air, and the refractive index of the substrate into the preset reflection coefficient formula to obtain the reflection coefficient. Then substituting the reflection coefficient into the preset reflectivity formula to obtain the reflectivity of the multilayer film.

[0058] In the embodiment of the present application, by clarifying the structural parameters and wavelength range parameters of the multilayer film model, based on these parameters, parametric inverse design of the multilayer film model is carried out by finite element simulation. By imposing the sequential constraint of the central wavelength combination, constructing the total system transmission matrix, and calculating the reflectivity, an objective function is finally constructed. The objective function comprehensively considers factors such as the central wavelength combination, material refractive index, air refractive index, substrate refractive index, traversed wavelength range, total film thickness, number of film layers, and target reflectivity, and can comprehensively reflect the optical performance of the multilayer film.

[0059] A possible implementation manner of the embodiment of the present application is that the method further includes: Imposing a central wavelength constraint and a total film thickness constraint on the objective function; Among them, the central wavelength constraint is that the central wavelength combination in the objective function is within the central wavelength range; the total film thickness constraint is that the total film thickness in the objective function is within the total film thickness range, and the total film thickness is determined based on the central wavelength combination, the refractive index of the high-refractive-index film, the refractive index of the low-refractive-index film, and the number of film layers.

[0060] In the embodiment of the present application, by imposing a central wavelength constraint and a total film thickness constraint on the objective function, it is ensured that the central wavelength combination and the total film thickness are always within the preset reasonable ranges during the optimization process. The central wavelength constraint ensures that the central wavelength combination is arranged in an orderly manner within the target wavelength range, avoiding the generation of invalid solutions; the total film thickness constraint ensures that the total thickness of the multilayer film meets the physical limitations of actual manufacturing and application.

[0061] A possible implementation manner of the embodiment of the present application is that, based on the central wavelength combination, constructing the total system transmission matrix of the multilayer film model includes: For each group in the multilayer film model, calculating the first thickness of the high-refractive-index film and the second thickness of the low-refractive-index film in the group based on the central wavelength combination; constructing the first transmission matrix of the group based on the first thickness, constructing the second transmission matrix of the group based on the second thickness, and constructing the transmission matrix of the group based on the first transmission matrix, the second transmission matrix, and the number of film layers; Calculate the product of the transfer matrices of a preset number of groups to obtain the total system transfer matrix of the multilayer film model.

[0062] In this embodiment, any group in the multilayer film model is represented as i, and the expression for the first transfer matrix of this group of film layers is determined as: where φ i1 is the phase thickness, λ is the currently traversed wavelength, n1 is the refractive index of the high-refractive-index film, λ ci is the central wavelength corresponding to group i, j is the imaginary unit, and j 2 = -1.

[0063] The expression for the second transfer matrix of group i is: where φ i2 is the phase thickness, λ is the currently traversed wavelength, n2 is the refractive index of the low-refractive-index film, λ ci is the central wavelength corresponding to group i, j is the imaginary unit, and j 2 = -1.

[0064] The transfer matrix of group i is where L in the formula is the number of film layers.

[0065] The expression for the total system transfer matrix of the multilayer film model is:

[0066] In the embodiment of the present application, based on the combination of central wavelengths, the thicknesses of the high-refractive-index film and the low-refractive-index film in each group are calculated, and their transfer matrices are respectively constructed. Finally, the total transfer matrix of each layer of film is obtained through matrix multiplication, and then the transfer matrices of all film layers are multiplied to obtain the total system transfer matrix of the multilayer film model. This process can accurately describe the propagation behavior of light in the multilayer film, and combined with the transfer matrix method, the reflectivity and transmittance can be efficiently calculated, providing an accurate mathematical model for the optimization design.

[0067] A possible implementation manner of the embodiment of the present application calculates the reflectivity based on the total system transfer matrix, the air refractive index, and the substrate refractive index, including: Substitute the total system transfer matrix, the air refractive index, and the substrate refractive index into the reflection coefficient formula to obtain the reflection coefficient. The reflection coefficient formula is: where r is the reflection coefficient, T is the total system transfer matrix, n air is the air refractive index, and n sub is the substrate refractive index; Substitute the reflection coefficient into the reflectivity formula to obtain the reflectivity. The reflectivity formula is as follows: R = |r| 2 where R is the reflectivity and r is the reflection coefficient.

[0068] Based on the transfer matrix algorithm, the embodiments of the present application can accurately describe the propagation behavior of light in a multilayer film, and efficiently calculate the reflectivity through strict mathematical formulas, providing a high-precision reflectivity calculation method, which provides a reliable quantitative basis for the optical performance evaluation and optimal design of the multilayer film. A possible implementation manner of the embodiments of the present application is to perform iterative simulation on the multilayer film model based on the objective function using the genetic algorithm, and optimize it using the fitness function during the iteration until the optimization goal is met or the number of iterations reaches the preset number of iterations, including: Randomly generate an initial population based on the objective function; Using the fitness function, calculate the fitness value of each individual in the initial population based on the objective function value corresponding to each individual. The fitness value is the mean of the reflectivities corresponding to the central wavelength range; Perform iterative optimization operations until the fitness value obtained in the current iteration round meets the optimization goal or the number of iterations reaches the preset number of iterations; Among them, the iterative optimization operation includes: taking the population obtained in the previous iteration round as the parent population, performing selection operation, crossover operation, and mutation operation on the parent population based on the fitness value of the parent population to obtain a new population and the updated objective function value, and calculating the fitness value of each individual obtained in the current iteration round based on the updated objective function value; determining the penalty factor and updating the fitness value of each individual obtained in the current iteration round based on the penalty factor.

[0069] Specifically, based on the objective function with the order constraint, central wavelength constraint, and total film thickness constraint of the central wavelength combination set, randomly generate an initial population to ensure that the possible solution space can be covered. Each individual in the initial population represents a set of central wavelength combinations and the number of film layers. For example, each individual can be represented as a vector, x = [λ1, λ2,..., λ N , L], where λ1, λ2,..., λ N is the central wavelength combination and L is the number of film layers. Substitute the central wavelength combination and the number of film layers of each individual into the objective function to obtain the objective function value of the individual.

[0070] For each individual in the initial population, calculate its reflectivity within a traversed wavelength range using the transfer matrix algorithm. The process includes: constructing the transfer matrix of each film layer in the multilayer film, calculating the total system transfer matrix based on the transfer matrix of each film layer, and calculating the reflectivity at each wavelength within the traversed wavelength range using the total system transfer matrix. Calculate the average value of the reflectivity within the central wavelength range, and define the obtained average value of the reflectivity as the fitness value of this individual. Each individual in the obtained initial population corresponds to an objective function value and a fitness value.

[0071] Furthermore, perform iterative optimization operations and repeat the iterative optimization operations until the fitness value obtained in a certain iteration round meets the optimization objective or the number of iterations reaches the preset number of iterations. The optimization objective includes that the fitness value of the optimal solution is the highest and exceeds the target reflectivity by 0.98, and the maximum number of iterations can be set to a number between 200 and 300 according to actual requirements.

[0072] The iterative optimization operations include: selection operation, select better individuals as parents according to the fitness value. Common selection methods include roulette wheel selection, tournament selection, etc. The probability of an individual with a higher fitness value being selected is greater; crossover selection, perform crossover operations on the selected parents to generate new offspring. For example, partially exchange the central wavelength combinations of two parents, or exchange the number of film layers of two parents to form a new central wavelength combination and / or film layer distribution; mutation operation, perform random mutations on the offspring to increase population diversity. For example, randomly adjust the value of a certain central wavelength or the number of film layers to explore a new design space. Finally, update the population, replace the parents with the newly generated offspring to form a new generation of population.

[0073] For each individual in the new generation of population, recalculate its fitness value using the optical transfer matrix algorithm based on the updated objective function value, and update the fitness value using the penalty factor. Compare whether the new fitness value meets the optimization objective and adjust the optimization direction.

[0074] See Figure 4 , which shows the convergence graph of the genetic algorithm iteration process. The abscissa represents the iteration round, and the ordinate represents the fitness value obtained in the intermediate iteration rounds.

[0075] The embodiments of this application utilize the global search ability of the genetic algorithm to efficiently explore complex design spaces, avoid falling into local optimal solutions, and at the same time ensure that the optimization results meet the target reflectivity requirements and physical constraints through the fitness function and the penalty factor, ultimately achieving high-precision optimization of multilayer film design.

[0076] A possible implementation manner of the embodiments of this application, determining the penalty factor and updating the fitness value of each individual obtained in the current iteration round based on the penalty factor includes: For each individual obtained in the current iteration round, calculate the difference between the fitness value of the individual and the target reflectivity; calculate the product of the difference and the adjustment coefficient as the penalty factor, and adjust the fitness value of the individual based on the penalty factor.

[0077] In this embodiment, the penalty factor is used to penalize individuals that do not meet the requirements of the target reflectivity, thereby reducing their fitness values. The calculation of the penalty factor is based on the difference between the fitness value of the individual and the target reflectivity.

[0078] For each individual, calculate the difference between its fitness value and the target reflectivity. When the obtained difference is less than 0, it means that the fitness value of the individual does not meet the requirements of the target reflectivity.

[0079] In a possible case, a technician can determine a positive number as the adjustment factor according to experience, calculate the product of the difference and the adjustment coefficient as the penalty factor, and the obtained penalty factor is negative. Then calculate the sum of the penalty factor and the fitness value of the individual as the adjusted fitness value. For example, the fitness value of a certain individual is 0.95, which does not meet the requirement of being greater than the target reflectivity of 0.98. Calculate the difference between the fitness value and the target reflectivity as -0.03, set the adjustment coefficient to 10, calculate the product of the difference and the adjustment coefficient as -0.3 as the penalty factor, and take the sum of the penalty factor and the fitness value, 0.65, as the adjusted fitness value.

[0080] In another possible case, a technician can determine a negative number as the adjustment coefficient according to experience, calculate the product of the difference and the adjustment coefficient as the penalty factor, and the obtained penalty factor ranges from 0 to 1. Then calculate the product of the penalty factor and the fitness value of the individual as the adjusted fitness value.

[0081] The embodiment of the present application effectively guides the optimization process towards meeting the target reflectivity by introducing a penalty mechanism, avoids the algorithm from generating solutions that do not meet the requirements, and improves the optimization efficiency and the quality of the solutions at the same time.

[0082] A possible implementation manner of the embodiment of the present application, the method further includes: After the iteration is completed, determine the reflectivity of the traversed wavelength range corresponding to the optimal solution, and draw a reflectivity curve based on the reflectivity of the traversed wavelength range; Select the reflectivity corresponding to the central wavelength range from the reflectivity of the traversed wavelength range, and calculate the reflectivity mean and the reflectivity standard deviation based on the reflectivity corresponding to the central wavelength range, so as to verify the iteration result through the reflectivity mean and the reflectivity standard deviation.

[0083] In this embodiment, the optimal solution includes a central wavelength combination (N central wavelengths) and the number of film layers L. Within the traversed wavelength range, the reflectance of the multilayer film is calculated for each wavelength value to obtain a reflectance data set. Using the reflectance data set as the plotting data, a reflectance curve is plotted using a plotting tool (such as Matplotlib, Excel, etc.). As Figure 5 shown, the horizontal axis of the reflectance curve is the wavelength value, and the vertical axis is the reflectance. It is also possible to calculate the total thickness of the film layers corresponding to the optimal solution in combination with the refractive indices of the high-refractive-index film and the low-refractive-index film according to the optimal solution, and use the obtained total thickness of the film layers as an evaluation index.

[0084] Select the reflectance within the central wavelength range, and calculate the mean reflectance and the standard deviation of the reflectance. If the mean reflectance is greater than the target reflectance of 0.98, it indicates that the optimization result meets the design requirements. It is also possible to preset the requirements for the standard deviation of the reflectance, and compare the calculated standard deviation of the reflectance with the preset requirements for the standard deviation of the reflectance, which can reflect whether the reflectance is evenly distributed within the central wavelength range and whether the smoothness of the reflectance curve meets the requirements.

[0085] The embodiment of the present application comprehensively evaluates the quality of the optimization result using the reflectance curve and quantization indexes (mean and standard deviation) to ensure that the reflectance of the multilayer film reaches the optimum and is evenly distributed within the target wavelength range.

[0086] The genetic algorithm is an intelligent optimization algorithm that simulates the biological evolution process, and its characteristic is that it can quickly search for the global optimal solution in the complex nonlinear optimization space of multiple layers and multiple parameters. Therefore, using the genetic algorithm to optimize the design of optical multilayer films can achieve high efficiency within the target wavelength range. In addition, other intelligent optimization algorithms such as simulated annealing and particle swarm optimization can also be used to replace the genetic algorithm.

[0087] The method for optimizing the central wavelength of the brightness enhancement film based on the genetic algorithm provided by the present application is also applicable to other wavelength ranges and different reflectance requirements, and is not only applicable to the dual brightness enhancement film (DBEF) structure, but also can be applied to the design of other types of optical thin films such as band-pass filters and antireflection films.

[0088] The embodiment of the present application uses the genetic algorithm to avoid the local minimum problem of the traditional method and has the global search ability in high-dimensional and multi-constraint problems; it can flexibly adjust the target reflectance and wavelength range and support multi-objective thin film design; by introducing constraint conditions through penalty terms, it can automatically meet requirements such as film thickness sorting and total thickness limitation; the method can be extended to the design of other types of multilayer films such as band-pass filters and antireflection films.

[0089] In the embodiment of the present application, an electronic device is provided, such as Figure 6 shown, Figure 6The illustrated electronic device 600 includes: a processor 601 and a memory 603. Among them, the processor 601 and the memory 603 are connected, such as through a bus 602. Optionally, the electronic device 600 may further include a transceiver 604. It should be noted that in practical applications, the transceiver 604 is not limited to one, and the structure of the electronic device 600 does not constitute a limitation to the embodiments of the present application.

[0090] The processor 601 may be a CPU (Central Processing Unit), a general-purpose processor, a DSP (Digital Signal Processor), an ASIC (Application Specific Integrated Circuit), an FPGA (Field Programmable Gate Array), or other programmable logic devices, transistor logic devices, hardware components, or any combination thereof. It can implement or execute various exemplary logical blocks, modules, and circuits described in connection with the disclosure of the present application. The processor 601 may also be a combination that implements computing functions, such as a combination including one or more microprocessors, a combination of a DSP and a microprocessor, etc.

[0091] The bus 602 may include a path for transmitting information between the above components. The bus 602 may be a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus, etc. The bus 602 may be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 6 only a thick line is used to represent it in the figure, but it does not mean that there is only one bus or one type of bus.

[0092] The memory 603 can be a ROM (Read Only Memory), or other types of static storage devices that can store static information and instructions, a RAM (Random Access Memory), or other types of dynamic storage devices that can store information and instructions. It can also be an EEPROM (Electrically Erasable Programmable Read Only Memory), a CD-ROM (Compact Disc Read Only Memory), or other optical disc storage, optical disc storage (including compact discs, laser discs, optical discs, digital versatile discs, Blu-ray discs, etc.), magnetic disk storage media, or other magnetic storage devices, or any other medium that can be used to carry or store the desired program code in the form of instructions or data structures and can be accessed by a computer, but is not limited thereto.

[0093] The memory 603 is used to store the application program code for implementing the solution of this application, and is controlled by the processor 601 for execution. The processor 601 is used to execute the application program code stored in the memory 603 to implement the content shown in the foregoing embodiments of the method for optimizing the central wavelength of a brightness enhancement film based on a genetic algorithm.

[0094] Figure 6 The illustrated electronic device is merely an example and should not impose any limitations on the functions and usage scope of the embodiments of this application.

[0095] The embodiments of this application provide a computer-readable storage medium on which a computer program is stored. When it runs on a computer, it enables the computer to execute the content shown in the foregoing embodiments of the method for optimizing the central wavelength of a brightness enhancement film based on a genetic algorithm.

[0096] It should be understood that although the steps in the flowchart of the accompanying drawings are shown in sequence according to the indication of the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless there is a clear indication in this article, the execution of these steps has no strict order limitation, and they can be executed in other orders. Moreover, at least a part of the steps in the flowchart of the accompanying drawings may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily executed at the same time, but can be executed at different times, and their execution order is not necessarily sequential, but can be executed alternately or in turn with at least a part of other steps or sub-steps or stages of other steps.

[0097] An embodiment of the present application provides a computer program product, including a computer program, which when executed by a processor, implements the content shown in the foregoing embodiment of the method for optimizing the central wavelength of a brightness enhancement film based on a genetic algorithm.

[0098] The above are only partial embodiments of the present application. It should be noted that for those of ordinary skill in the art, without departing from the principle of the present application, several improvements and refinements can be made, and these improvements and refinements should also be regarded as the protection scope of the present application.

Claims

1. A method for optimizing the central wavelength of a brightness enhancement film based on a genetic algorithm, characterized in that, It includes: Obtain preset environmental parameters and define optimization variable parameters; Based on the preset environmental parameters and the optimization variable parameters, perform parametric inverse design on the multi-layer film model using finite element simulation to obtain an objective function; Based on the objective function, use a genetic algorithm to perform iterative simulation on the multi-layer film model, and optimize it using a fitness function during the iteration until the optimization goal is met or the number of iterations reaches a preset number of iterations; Obtain the optimal solution after the iteration is completed, and the optimal solution includes a central wavelength combination and the number of film layers.

2. The method for optimizing the central wavelength of a brightness enhancement film based on a genetic algorithm according to claim 1, wherein The obtaining of the preset environmental parameters and the definition of the optimization variable parameters includes: Obtain the preset environmental parameters, and the preset environmental parameters include: the structural parameters of the multi-layer film model, the wavelength range parameters, and the target reflectivity; Define a preset number of central wavelengths as the central wavelength combination, and use the central wavelength combination and the number of film layers as the optimization variable parameters; Wherein, the multi-layer film model is composed of alternating stacks of high-refractive-index films and low-refractive-index films, the number of groups of the multi-layer film model is equal to the preset number, and the sum of the number of high-refractive-index films and low-refractive-index films included in each group is the number of film layers.

3. The method for optimizing the central wavelength of a brightness enhancement film based on a genetic algorithm according to claim 2, wherein The structural parameters of the multi-layer film model include: the refractive index of the high-refractive-index film, the refractive index of the low-refractive-index film, the substrate refractive index, and the total film thickness range; the wavelength range parameters include: the traversal wavelength range and the central wavelength range; Based on the preset environmental parameters and the optimization variable parameters, perform parametric inverse design on the multi-layer film model using finite element simulation to obtain an objective function, including: Apply a sequential constraint to the central wavelength combination; Based on the central wavelength combination, construct the total system transfer matrix of the multi-layer film model; Obtain the air refractive index, calculate the reflectivity based on the total system transfer matrix, the air refractive index, and the substrate refractive index; construct an objective function, and the expression of the objective function is: F = (λ c , n1, n2, n air , n sub , λ, H, L, tR, λ s , λ e ) where F is the objective function, λ c is the central wavelength combination, n1 is the refractive index of the high refractive index film, n2 is the refractive index of the low refractive index film, n air is the refractive index of air, n sub is the refractive index of the substrate, λ is the traversed wavelength range, H is the total thickness of the film layer, L is the number of film layers, tR is the target reflectivity, λ s is the upper bound of the central wavelength range, λ e is the lower bound of the central wavelength range.

4. The method for optimizing the central wavelength of a brightness enhancement film based on a genetic algorithm according to claim 1, wherein The method further includes: Apply a central wavelength constraint and a total film thickness constraint to the objective function; Wherein, the central wavelength constraint is that the central wavelength combination in the objective function is within the central wavelength range; the total film thickness constraint is that the total film thickness in the objective function is within the total film thickness range, and the total film thickness is determined based on the central wavelength combination, the refractive index of the high-refractive-index film, the refractive index of the low-refractive-index film, and the number of film layers.

5. The method for optimizing the central wavelength of a brightness enhancement film based on a genetic algorithm according to claim 3, characterized in that The constructing of the total system transfer matrix of the multi-layer film model based on the central wavelength combination includes: For each group in the multi-layer film model, calculate the first thickness of the high-refractive-index film and the second thickness of the low-refractive-index film in the group based on the central wavelength combination; construct the first transfer matrix of the group based on the first thickness, construct the second transfer matrix of the group based on the second thickness, and construct the transfer matrix of the group based on the first transfer matrix, the second transfer matrix, and the number of film layers; Calculate the product of the transfer matrices of the preset number of groups to obtain the total system transfer matrix of the multi-layer film model.

6. The method for optimizing the central wavelength of a brightness enhancement film based on a genetic algorithm according to claim 3, wherein The calculating of the reflectivity based on the total system transfer matrix, the air refractive index, and the substrate refractive index includes: Substitute the total system transmission matrix, the air refractive index, and the substrate refractive index into the reflection coefficient formula to obtain the reflection coefficient. The reflection coefficient formula is as follows: where r is the reflection coefficient, T is the total system transmission matrix, n air is the refractive index of air, and n sub is the refractive index of the substrate; Substitute the reflection coefficient into the reflectance formula to obtain the reflectance. The reflectance formula is as follows: R＝|r| 2 where R is the reflectance and r is the reflection coefficient.

7. The method for optimizing the central wavelength of a brightness enhancement film based on a genetic algorithm according to claim 1, characterized in that The iterative simulation of the multilayer film model based on the objective function using the genetic algorithm and the optimization using the fitness function during the iteration process until the optimization objective is met or the number of iterations reaches the preset number of iterations includes: Randomly generate an initial population based on the objective function; Using the fitness function, calculate the fitness value of each individual in the initial population based on the objective function value corresponding to each individual in the initial population. The fitness value is the mean of the reflectances corresponding to the central wavelength range; Perform iterative optimization operations until the fitness value obtained in the current iteration round meets the optimization objective or the number of iterations reaches the preset number of iterations; wherein the iterative optimization operation includes: taking the population obtained in the previous iteration round as the parental population, performing selection operation, crossover operation, and mutation operation on the parental population based on the fitness value of the parental population to obtain a new population and an updated objective function value, and calculating the fitness value of each individual obtained in the current iteration round based on the updated objective function value; determining a penalty factor, and updating the fitness value of each individual obtained in the current iteration round based on the penalty factor.

8. The method for optimizing the central wavelength of a brightness enhancement film based on a genetic algorithm according to claim 7, wherein The determining the penalty factor and updating the fitness value of each individual obtained in the current iteration round based on the penalty factor includes: For each individual obtained in the current iteration round, calculate the difference between the fitness value of the individual and the target reflectance; calculate the product of the difference and the adjustment coefficient as the penalty factor, and adjust the fitness value of the individual based on the penalty factor.

9. The method for optimizing the central wavelength of a brightness enhancement film based on a genetic algorithm according to claim 1, wherein The method further includes: After the iteration is completed, determine the reflectance of the traversed wavelength range corresponding to the optimal solution, and draw a reflectance curve based on the reflectance of the traversed wavelength range; Select the reflectances corresponding to the central wavelength range from the reflectances of the traversed wavelength range, and calculate the mean reflectance and the standard deviation of the reflectance based on the reflectances corresponding to the central wavelength range, so as to verify the iteration result through the mean reflectance and the standard deviation of the reflectance.

10. An electronic device, characterized in that, including: At least one processor; A memory; At least one application program, wherein at least one application program is stored in the memory and is configured to be executed by at least one processor. The at least one application program is configured to: execute the method for optimizing the central wavelength of the brightness enhancement film based on the genetic algorithm according to any one of claims 1-9.

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