Optical system polarization aberration optimization design method based on multi-group film system cooperative optimization strategy

Abstract

The application discloses a method for optimizing a polarization aberration of an optical system based on a multi-group film system cooperative optimization strategy, belongs to the technical field of optical system aberration optimization, and aims at solving the problems in the prior art.The method comprises the following steps: 1, a three-dimensional polarization light tracing algorithm is used to analyze polarization information on a light path and to calculate a polarization aberration function of the optical system; 2, an evaluation function of the polarization aberration of the optical system is constructed, and the optimization effect of the polarization aberration is evaluated according to the evaluation function value; 3, an optimization mechanism of the polarization aberration of the optical system is constructed based on a genetic algorithm and a particle swarm algorithm, thickness parameters of a film system are selected as optimization variables, and each group of feasible solutions is regarded as a new individual; 4, a cooperative optimization strategy is established based on the optimization mechanism of step 3, and the film system structure on multiple optical surfaces is cooperatively optimized; 5, the new individual is evaluated according to constraint conditions and the evaluation function, and the best film system structure is selected; and 6, after the evaluation of the new individual is completed, a termination condition is judged, and the polarization aberration and the transmittance value of the optical system are calculated.

Description

Technical Field

[0001] This invention belongs to the field of optical system aberration optimization technology, specifically relating to an optimization design method for polarization aberration of optical systems, particularly an optimization design method for polarization aberration of optical systems based on a multi-set film system collaborative optimization strategy. Background Technology

[0002] Optical thin films, as important optical components in modern optical systems, are commonly used to control the transmission of light energy at optical interfaces. When light is incident on the surface of an optical thin film at a certain angle, the transmission / reflection coefficients of s-polarized and p-polarized light are different. This leads to differences in the phase and amplitude of the two orthogonally polarized components of the beam, resulting in polarization effects. The polarization effects generated by optical thin films directly affect the wavefront aberration of the optical system and introduce chromatic aberration. In some complex optical systems with high numerical apertures (such as photolithography projection lenses), polarization aberration induced by optical thin films has become one of the main factors affecting the system's imaging quality. Furthermore, for polarization remote sensing systems, polarization aberration induced by the film system directly affects the measurement accuracy of polarization signals.

[0003] In 2015, Shang Hongbo of the Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, published an article entitled "The Influence and Improvement of Polarization Aberration Induced by Film Systems on the Design of Projection Lithography Objectives" in the journal *Acta Optica Sinica*. Taking a projection lithography objective system with a numerical aperture of 0.75 as an example, this paper proposed a method of combining film systems to correct polarization aberration induced by film systems. First, the incident angle range of the beam on each optical surface in the optical system is analyzed. Then, the polarization characteristics of the film system are set as the optimization target. Film systems with high transmittance and low polarization characteristics are designed for surfaces in different incident ranges to minimize the polarization characteristics of the coating interface, thereby reducing the polarization aberration of the entire optical system. Although this method can reduce the polarization aberration of the optical system to a certain extent, it still has certain limitations. For example, in optical systems with a large number of coated optical surfaces, the residual polarization effect of low polarization characteristic films can still accumulate to produce significant polarization aberrations; in high-performance optical systems with polarization elements such as birefringent materials, diffraction gratings, and holograms, polarization aberrations mainly originate from the polarization elements, rather than the optical thin film. Under these conditions, the design of low polarization characteristic films cannot effectively improve the polarization aberration of optical systems. Summary of the Invention

[0004] To address the issues of image quality degradation and system performance decline caused by residual polarization aberration in existing optical systems, this invention proposes an optimized design method to further reduce or eliminate polarization aberration in optical systems. This method compensates for residual polarization aberration throughout the system by simultaneously optimizing the polarization characteristics of films at multiple interfaces. This invention enables the optimized design of polarization aberration and transmittance in optical systems.

[0005] The technical solution of this invention to solve the technical problem is:

[0006] A polarization aberration optimization design method for optical systems based on a multi-film system collaborative optimization strategy, comprising the following steps:

[0007] Step 1: Analyze the polarization information on the light path using a three-dimensional polarization tracing algorithm, and calculate the polarization aberration function of the optical system;

[0008] Step 2: Construct an evaluation function for polarization aberration of the optical system, and evaluate the optimization effect of polarization aberration based on the evaluation function value;

[0009] Step 3: Based on the genetic algorithm and particle swarm optimization algorithm, construct an optimization mechanism for the polarization aberration of the optical system, select the thickness parameter of the film system as the optimization variable, and treat each feasible solution as a new individual;

[0010] Step 4: Based on the optimization mechanism in Step 3, establish a collaborative optimization strategy to achieve joint optimization of the film structure on multiple optical surfaces;

[0011] Determine the incident angle range of each coated surface, select a suitable film system in the optical system as the optimization variable, first use step 3 to optimize each film system separately to ensure that each solution produces a high transmittance, then sort them according to the transmittance, and merge the solution sets of all film systems to generate a new individual, and then use step 3 to continue optimization.

[0012] Step 5: Evaluate the new individual based on the constraints and evaluation function, and select the optimal membrane structure;

[0013] The thickness parameters of the film system in the optical system are updated based on the new individuals, and the constraints are determined. For individuals that do not meet the constraints, the optimization process in step 3 continues. For new individuals that meet the constraints, the three-dimensional polarization tracing process in step 1 is executed. Then, the polarization aberration evaluation function is calculated according to step 2. Individuals are sorted according to the magnitude of the evaluation function value, and the individual with the smallest evaluation function value is recorded.

[0014] Step 6: After completing the evaluation of the new individual, determine whether the program termination condition has been met. If the termination condition has not been met, continue to optimize the film thickness parameters of the optical system and execute the process of steps 1 to 5. If the termination condition has been met, select the film structure with the smallest evaluation function value during the optimization process and calculate the polarization aberration and transmittance values ​​of the optical system.

[0015] Step 1 specifically involves: first, inputting the structural parameters of the optical system, including the radius of curvature of the interfaces, the distance between the interfaces, the refractive index of the materials, the pupil diameter, the field of view, and the wavelength; then, using a three-dimensional polarization tracing algorithm to calculate the polarization aberration function of the entire optical system.

[0016]

[0017] J q Let be the three-dimensional polarization tracing matrix for the light ray on the q-th interface. This matrix is ​​expressed as:

[0018]

[0019] Among them O out,q and O in,q Let and represent the local coordinate systems of the incident ray and the exit ray, respectively. Both matrices are real-valued unitary matrices, used to achieve the transformation between the local and global coordinate systems; α s,q and α p,q is the transmission coefficient or reflection coefficient of s-polarized and p-polarized light at each of the q-th interfaces. When the interface is uncoated, the transmission coefficient and reflection coefficient are calculated using Fresnel's formula; when the interface is coated with a multilayer dielectric film, the transmission coefficient and reflection coefficient are obtained from the characteristic matrix of the film system, the expression of which is:

[0020]

[0021] Where δ m Let δ be the phase thickness of the m-th film. m =2π / λn m d m θ m n m For refractive index, d m Let θ be the film thickness. m η is the angle of incidence; m Let η be the effective admittance of the m-th film dielectric, where the effective admittance of p-polarized light is η. m,p =n m / cosθ m The effective admittance of s-polarized light is η m,s =n m cosθ m η sub The effective admittance of the membrane substrate is given; the transmission coefficient t and reflection coefficient r of the membrane system are calculated according to equation (3):

[0022]

[0023]

[0024] Where η0 is the optical admittance of the incident medium.

[0025] Step 2 specifically involves: extracting the physical parameters contained in the polarization aberration function in equation (1) using singular value decomposition.

[0026]

[0027] Where U and V are both unitary matrices, and S is a diagonal real matrix. Represents the conjugate transpose of a matrix; The Hermitian matrix represents the bidirectional attenuation component. The matrix is ​​unitary and represents the phase retardation component. A polarization aberration evaluation function for the optical system is constructed using the biaxial attenuation D and phase retardation R in the polarization aberration function.

[0028]

[0029] Where m and n are the light sampling points along the x and y axes on the exit pupil plane, respectively, and ΔD and ΔR are tolerance values. and It is the target value, y d and y r This is the weight value. Meanwhile, formula (7) satisfies the following constraints:

[0030]

[0031] t is the target transmittance, and t is the transmittance value of the sampled light.

[0032] The optimization steps for step 3 are as follows:

[0033] Step 1: Determine the initial structure of the film system on each surface of the optical system, set the thickness parameters of these film systems as optimization variables, and then randomly generate 2N sets of initial solutions, each set of solutions is called an individual;

[0034] Step 2: Optimize the first N individuals using a genetic algorithm, including crossover and mutation operations. Crossover involves exchanging some data between individuals to change variables. The method is to select an intersection point in the array of any two individuals and then exchange some data. Its expression is:

[0035] x i+1 =αx i +(1-α)x j (9)

[0036] x j+1 =(1-α)x i +αx j (10)

[0037] Where x i and x j There are two random individuals, x i+1 and x j+1These are two new individuals generated after the crossover operation, where α is the position of the crossover point. Then, a mutation operation is performed on these new individuals. This operation simulates a gene mutation process to update the individuals. The mutation operation is performed on the k-th variable in the random individual, as shown in the following expression:

[0038] x i+1 (k)=β(x max -x min )+x min (11)

[0039] Where x min ~x max β represents the range of film thickness variables, where β is a parameter between 0 and 1.

[0040] Step 3: Optimize the remaining N individuals using the Particle Swarm Optimization (PSO) algorithm. This algorithm approaches the global optimum based on the optimal solution in the population and the search experience of each individual. The search range of variables is controlled by the flight speed. The calculation process is as follows:

[0041] v i+1 =w·v i +c1·rand·(p id -x i )+c2·rand·(p pd -x i (12)

[0042] x i+1 =x i +v i+1 (13)

[0043] Where v is the velocity vector, c1 and c2 are learning factors, and rand is a random variable between 0 and 1; based on steps 2 and 3, a total of 2N new individuals are generated.

[0044] The beneficial effects of this invention are as follows: This invention adds optical thin film parameters as optimization variables for polarization aberration of the optical system, employs an intelligent optimization algorithm as the optimization mechanism, and combines a collaborative optimization strategy, a three-dimensional polarization tracing algorithm, and a polarization aberration function to construct a mathematical model for optimizing the polarization aberration of the optical system. This model achieves the balance of polarization aberration of the entire optical system by changing the polarization characteristics of the film system on different optical surfaces, thereby achieving the purpose of correcting residual polarization aberration. Simultaneously, it can also optimize the transmittance of the optical system and has the advantages of high optimization efficiency and speed. Attached Figure Description

[0045] Figure 1 This is a flowchart of the optical system polarization aberration optimization method based on a multi-film system collaborative optimization strategy of the present invention;

[0046] Figure 2An optical system diagram provided as an example of the present invention;

[0047] Figure 3 This refers to the zero field-of-view exit pupil transmittance described in this invention;

[0048] Figure 4 This refers to the bidirectional attenuation aberration at the zero field of view exit pupil as described in this invention;

[0049] Figure 5 This refers to the zero-field-of-view exit pupil phase retardation aberration described in this invention;

[0050] Figure 6 This refers to the transmittance at the exit pupil of the edge field of view as described in this invention;

[0051] Figure 7 This refers to the bidirectional attenuation aberration at the exit pupil of the edge field of view as described in this invention;

[0052] Figure 8 This refers to the phase delay aberration at the exit pupil of the edge field of view as described in this invention.

[0053] Specific methods

[0054] The present invention will now be described in further detail with reference to the accompanying drawings.

[0055] A method for optimizing polarization aberrations in optical systems based on a multi-film collaborative optimization strategy, comprising the following steps:

[0056] Step 1: Analyze the polarization information along the light path using a three-dimensional polarization tracing algorithm;

[0057] First, the structural parameters of the optical system are input, including the radius of curvature of the interfaces, the distance between the interfaces, the refractive index of the materials, the pupil diameter, the field of view, and the wavelength. Then, a three-dimensional polarization tracing algorithm is used to calculate the polarization aberration function of the entire optical system.

[0058]

[0059] J q Let be the three-dimensional polarization tracing matrix for the light ray on the q-th interface. This matrix is ​​expressed as:

[0060]

[0061] Among them O out,q and O in,q Let and represent the local coordinate systems of the incident ray and the exit ray, respectively. Both matrices are real-valued unitary matrices, used to achieve the transformation between the local and global coordinate systems; α s,q and α p,qis the transmission coefficient or reflection coefficient of s-polarized and p-polarized light at each of the q-th interfaces. When the interface is uncoated, the transmission coefficient and reflection coefficient are calculated using Fresnel's formula; when the interface is coated with a multilayer dielectric film, the transmission coefficient and reflection coefficient are obtained from the characteristic matrix of the film system, the expression of which is:

[0062]

[0063] Where δ m Let δ be the phase thickness of the m-th film. m =2π / λn m d m θ m n m For refractive index, d m Let θ be the film thickness. m η is the angle of incidence; m Let η be the effective admittance of the m-th film dielectric, where the effective admittance of p-polarized light is η. m,p =n m / cosθ m The effective admittance of s-polarized light is η m,s =n m cosθ m η sub The effective admittance of the membrane substrate is given; the transmission coefficient t and reflection coefficient r of the membrane system are calculated according to equation (3):

[0064]

[0065]

[0066] Where η0 is the optical admittance of the incident medium;

[0067] Step 2: Construct an evaluation function for the polarization aberration of the optical system;

[0068] The physical parameters contained in the polarization aberration function in equation (1) are extracted using the singular value decomposition method:

[0069]

[0070] Where U and V are both unitary matrices, and S is a diagonal real matrix. Represents the conjugate transpose of a matrix; The Hermitian matrix represents the bidirectional attenuation component. The matrix is ​​unitary and represents the phase retardation component. A polarization aberration evaluation function for the optical system is constructed using the biaxial attenuation D and phase retardation R in the polarization aberration function.

[0071]

[0072] Where m and n are the light sampling points along the x and y axes on the exit pupil plane, respectively, and ΔD and ΔR are tolerance values. and It is the target value, y d and y r This is the weight value. Meanwhile, formula (7) satisfies the following constraints:

[0073]

[0074] t is the transmittance of the target light, and t is the transmittance value of the sampled light.

[0075] Step 3: Construct an optimization mechanism for the polarization aberration of the optical system based on genetic algorithm and particle swarm optimization algorithm. The optimization steps are as follows:

[0076] Step 1: Determine the initial structure of the film system on each surface of the optical system, set the thickness parameters of these film systems as optimization variables, and then randomly generate 2N sets of initial solutions, each set of solutions is called an individual;

[0077] Step 2: Optimize the first N individuals using a genetic algorithm, including crossover and mutation operations. Crossover involves exchanging some data between individuals to change variables. The method is to select an intersection point in the array of any two individuals and then exchange some data. Its expression is:

[0078] x i+1 =αx i +(1-α)x j (9)

[0079] x j+1 =(1-α)x i +αx j (10)

[0080] Where x i and x j There are two random individuals, x i+1 and x j+1 These are two new individuals generated after the crossover operation, where α is the position of the crossover point. Then, a mutation operation is performed on these new individuals. This operation simulates a gene mutation process to update the individuals. The mutation operation is performed on the k-th variable in the random individual, as shown in the following expression:

[0081] x i+1 (k)=β(x max -x min )+x min (11)

[0082] Where x min ~x max β represents the range of film thickness variables, where β is a parameter between 0 and 1.

[0083] Step 3: Optimize the remaining N individuals using the Particle Swarm Optimization (PSO) algorithm. This algorithm approaches the global optimum based on the optimal solution in the population and the search experience of each individual. The search range of variables is controlled by the flight speed. The calculation process is as follows:

[0084] v i+1 =w·v i +c1·rand·(p id -x i )+c2·rand·(p pd -x i (12)

[0085] x i+1 =x i +v i+1 (13)

[0086] Where v is the velocity vector, c1 and c2 are learning factors, and rand is a random variable between 0 and 1; based on steps 2 and 3, a total of 2N new individuals are generated;

[0087] Step 4: Simultaneous optimization of optical system transmittance and polarization aberration is achieved using a collaborative optimization strategy. First, the film system to be optimized is determined. Then, the film system on the selected surface is optimized to ensure that each solution has high transmittance. The solution sets are sorted, and then the solution sets of all film systems are merged. The new population is then optimized using Step 3.

[0088] Step 5: Evaluate the new individual based on the constraints and evaluation function, and select the optimal membrane structure;

[0089] The thickness parameters of the film system in the optical system are updated based on the new individuals, and the constraints are determined. For individuals that do not meet the constraints, the optimization process in step 3 continues. For new individuals that meet the constraints, the three-dimensional polarization tracing process in step 1 is executed. Then, the polarization aberration evaluation function is calculated based on the three-dimensional polarization tracing results. Individuals are sorted according to the magnitude of the evaluation function value, and the individual with the smallest evaluation function value is recorded.

[0090] Step 6: After completing the evaluation of the new individual, determine whether the program termination condition has been met. If the termination condition has not been met, continue to optimize the film thickness parameters of the optical system and execute the process of steps 1 to 5. If the termination condition has been met, select the film structure with the smallest evaluation function value during the optimization process and calculate the polarization aberration and transmittance values ​​of the optical system. Example:

[0091] This invention is applicable to the optimization of polarization aberrations in all coated optical systems. To describe the specific implementation process, a double Gaussian system is used as an optimization example. The steps for optimizing polarization aberrations in other optical systems are the same.

[0092] This invention designs suitable film systems for different surfaces and utilizes the structural differences between the coating interfaces to achieve a balance of polarization effects, thereby correcting polarization aberrations in the entire optical system. By combining collaborative optimization measurement, an optical system polarization aberration evaluation function, and a three-dimensional polarization tracing algorithm, a complete polarization aberration optimization model is constructed. The entire polarization aberration optimization process for the optical system is as follows: Figure 1 As shown. This invention is based on a designed double Gaussian lens, which is used as an optimization example for polarization aberration design. The lens parameters are as follows:

[0093]

[0094]

[0095] The system has a working focal length of 35mm, a marginal half-field of view of 20°, an F-number of 2, and operates in the visible wavelength range. Taking this optical system as an example, the optimization steps for polarization aberration are detailed below:

[0096] Step 1: Calculation of polarization information along the light path

[0097] like Figure 1 The left side shows the analysis process of the polarization characteristics of the optical system. First, the structural parameters of the optical system, as well as initial parameters such as object point, wavelength, and field of view, are input, and light is sampled on the entrance pupil surface with a sampling point count of 9×9. Then, a three-dimensional polarization tracing algorithm is used to calculate the polarization transformation matrix of each ray when it passes through each interface, and the incident angle, propagation vector, and direction vectors of s-ray and p-ray intersecting each surface during the tracing process are recorded. These parameters can quickly construct the polarization tracing matrix in equation (2). These data are saved and can be called at any time during the film system optimization process, thereby quickly evaluating the polarization evaluation function.

[0098] Step 2: Optical system polarization aberration optimization process

[0099] like Figure 1 The right side shows the optimization process for film system updates and polarization aberrations in an optical system. First, the film variables that need to be optimized in the optical system are determined. Since... Figure 2In the optical system shown, the 4th and 7th surfaces are double-cemented surfaces, and the polarization effect introduced by the film system does not need to be considered. Here, the film stack structure on other surfaces is selected as the optimization variable. Alternatively, the film stack structure on some surfaces can be selected as the optimization variable, depending on the actual situation. Then, the film system on each surface is initialized, and all the film layers on the coated surfaces are designed as a 4-layer structure: A|HLHL|G, where A represents air, G represents the substrate glass, and H and L represent high and low refractive index materials, respectively. This structure has strong polarization adjustment capability and fewer optimization variables. H is Ti3O5 with a refractive index of 2.358, and L is SiO2 with a refractive index of 1.453. The thicknesses of H and L are set as optimization variables. The film stack thickness parameters on all surfaces are combined into a string, d1d2…d 39 d 40 The array contains 40 variables, each representing an individual variable. An initial optimization population of 200 individuals is randomly generated. The fitness values ​​of these individuals are sorted using the polarization evaluation function in equation (4). The top 100 individuals with the highest fitness values ​​are called elite individuals and are evolved using a genetic algorithm. The other 100 individuals are evolved using a particle swarm optimization algorithm to enhance the search capability of the entire solution space. The film thickness parameters in the optical system are updated based on the data of the optimized individuals. The transmittance of light passing through the coating interface is calculated using the incident angle data of ray tracing. The transmittance of all surfaces on the light path is cascaded to obtain the transmittance value of the entire system after the film system is updated. It is determined whether the average transmittance value on the pupil surface meets the design requirements. Here, the transmittance constraint is 95%. For individuals that do not meet the transmittance condition, the optimization calculation continues. For individuals that meet the transmittance requirement, according to equation (2), the transmission coefficient of the film system and the polarization tracing vector are used to form a new polarization tracing matrix. The polarization aberration function values ​​under different film system conditions are calculated, and the film structure under the minimum evaluation function condition is recorded. After 1000 cycles, the structure with the smallest polarization aberration was selected. The film thickness parameters on each coated surface are as follows.

[0100]

[0101]

[0102] The transmittance and polarization aberration at the exit pupil of the optimized optical system are as follows: Figures 3-8 As shown, the average pupil value of the optimized result is compared with that of the optical system coated with a low polarization antireflection film. Figure 3 The average transmittance in the zero field of view reached 99.2%, an increase of 4.6%. Figure 4 The average value of the mid-axis attenuation aberration was 0.0048, a decrease of 10.5%. Figure 5The average phase retardation aberration was 0.34°, a decrease of 20.3%; Figure 5 The average transmittance of the mid-edge field of view was 97.6%, an increase of 3.5%. Figure 7 The average value of the mid-axis attenuation aberration was 0.018, a decrease of 3.2%. Figure 8 The average median phase retardation aberration was 2.3°, a decrease of 15.6%. These data indicate that optimizing the film thickness parameters of each optical surface, whether at zero or edge field of view, balances the polarization characteristics between the films and enhances the transmittance of the optical system.

Claims

1. A polarization aberration optimization method for optical systems based on a multi-film collaborative optimization strategy, characterized by: The method includes the following steps: Step 1: Analyze the polarization information on the light path using a three-dimensional polarization tracing algorithm, and calculate the polarization aberration function of the optical system; Step 2: Construct an evaluation function for polarization aberration of the optical system, and evaluate the optimization effect of polarization aberration based on the evaluation function value; Step 3: Based on the genetic algorithm and particle swarm optimization algorithm, construct an optimization mechanism for the polarization aberration of the optical system, select the thickness parameter of the film system in the optical system as the optimization variable, and treat each feasible solution as a new individual; Step 4: Based on the optimization mechanism in Step 3, establish a collaborative optimization strategy to achieve joint optimization of the film structure on multiple optical surfaces; Determine the incident angle range for each coated surface, select a suitable film system in the optical system as the optimization variable, first optimize each film system separately using step 3 to ensure that each solution produces a high transmittance, then sort them according to the transmittance, merge the solution sets of all film systems, and then continue to optimize using step 3. Step 5: Evaluate the new individual based on the constraints and evaluation function, and select the optimal membrane structure; The thickness parameters of the film system in the optical system are updated based on the new individuals, and the constraints are determined. For individuals that do not meet the constraints, the optimization process in step 3 continues. For new individuals that meet the constraints, the three-dimensional polarization tracing process in step 1 is executed. Then, the polarization aberration evaluation function is calculated according to step 2. Individuals are sorted according to the magnitude of the evaluation function value, and the individual with the smallest evaluation function value is recorded. Step 6: After completing the evaluation of the new individual, determine whether the program termination condition has been met. If the termination condition has not been met, continue to optimize the film thickness parameters of the optical system and execute the process of steps 1 to 4. If the termination condition has been met, select the film structure with the smallest evaluation function value during the optimization process and calculate the polarization aberration and transmittance values ​​of the optical system.

2. The optical system polarization aberration optimization method based on a multi-film system collaborative optimization strategy according to claim 1, characterized in that, Step 1 specifically involves: first, inputting the structural parameters of the optical system, including the radius of curvature of the interfaces, the distance between the interfaces, the refractive index of the materials, the pupil diameter, the field of view, and the wavelength; then, using a three-dimensional polarization tracing algorithm to calculate the polarization aberration function of the entire optical system. J q Let be the three-dimensional polarization tracing matrix for the light ray on the q-th interface. This matrix is ​​expressed as: Among them O out,q and O in,q Let and represent the local coordinate systems of the incident ray and the exit ray, respectively. Both matrices are real-valued unitary matrices, used to achieve the transformation between the local and global coordinate systems; α s,q and α p,q is the transmission coefficient or reflection coefficient of s-polarized and p-polarized light at each of the q-th interfaces. When the interface is uncoated, the transmission coefficient and reflection coefficient are calculated using Fresnel's formula; when the interface is coated with a multilayer dielectric film, the transmission coefficient and reflection coefficient are obtained from the characteristic matrix of the film system, the expression of which is: Where δ m Let δ be the phase thickness of the m-th film. m =2π / λn m d m θ m n m For refractive index, d m Let θ be the film thickness. m η is the angle of incidence; m Let η be the effective admittance of the m-th film dielectric, where the effective admittance of p-polarized light is η. m,p =n m / cosθ m The effective admittance of s-polarized light is η m,s =n m cosθ m η sub The effective admittance of the membrane substrate is given; the transmission coefficient t and reflection coefficient r of the membrane system are calculated according to equation (3): Where η0 is the optical admittance of the incident medium.

3. The optical system polarization aberration optimization method based on a multi-film system collaborative optimization strategy according to claim 1, characterized in that, Step 2 specifically involves: extracting the physical parameters contained in the polarization aberration function in equation (1) using singular value decomposition. Where U and V are both unitary matrices, and S is a diagonal real matrix. Represents the conjugate transpose of a matrix; The Hermitian matrix represents the bidirectional attenuation component. The unitary matrix represents the phase retardation component; the polarization aberration evaluation function of the optical system is constructed using the biaxial attenuation D and phase retardation R in the polarization aberration function: Where m and n are the light sampling points along the x and y axes on the exit pupil plane, respectively, and ΔD and ΔR are tolerance values. and It is the target value, y d and y r These are the weight values; meanwhile, formula (7) satisfies the following constraints: t is the target transmittance, and t is the transmittance value of the sampled light.

4. The optical system polarization aberration optimization method based on a multi-film system collaborative optimization strategy according to claim 1, characterized in that, The optimization steps for step 3 are as follows: Step 1: Determine the initial structure of the film system on each surface of the optical system, set the thickness parameters of these film systems as optimization variables, and then randomly generate 2N sets of initial solutions, each set of solutions is called an individual; Step 2: Optimize the first N individuals using a genetic algorithm, including crossover and mutation operations. Crossover involves exchanging some data between individuals to change variables. The method is to select an intersection point in the array of any two individuals and then exchange some data. Its expression is: x i+1 =αx i +(1-a)x j (9) x j+1 =(1-a)x i +αx j (10) Where x i and x j There are two random individuals, x i+1 and x j+1 These are two new individuals generated after the crossover operation, where α is the position of the crossover point. Then, a mutation operation is performed on these new individuals. This operation simulates a gene mutation process to update the individuals. The mutation operation is performed on the k-th variable in the random individual, as shown in the following expression: x i+1 (k)=β(x max -x min )+x min (11) Where x min ~x max β represents the range of film thickness variables, where β is a parameter between 0 and 1. Step 3: Optimize the remaining N individuals using the Particle Swarm Optimization (PSO) algorithm. This algorithm approaches the global optimum based on the optimal solution in the population and the search experience of each individual. The search range of variables is controlled by the flight speed. The calculation process is as follows: v i+1 =w·v i +c1·rand·(p id -x i )+c2·rand·(p pd -x i ) (12) x i+1 =x i +v i+1 (13) Where v is the velocity vector, c1 and c2 are learning factors, and rand is a random variable between 0 and 1; based on steps 2 and 3, a total of 2N new individuals are generated.

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