Multi-point out-of-focus lens design optimization method for ametropia and lens thereof

Abstract

The invention discloses a multi-point out-of-focus lens design optimization method for refractive error and a lens thereof, and relates to the technical field of optical design, and the method comprises the steps: carrying out the multi-refractive simulation optical test of the structure distribution characteristics of a to-be-optimized multi-point out-of-focus lens, obtaining the out-of-focus filling rate, aberration and astigmatism correction effect, then carrying out the partition aggregation, and obtaining the out-of-focus filling rate of the to-be-optimized multi-point out-of-focus lens. Obtaining a deviation area and a deviation characteristic of each diopter; constructing an optimization parameter library; performing compensation target analysis according to the deviation area and the deviation characteristic of each diopter to obtain an optical target parameter; and performing matching search in the optimization parameter library, determining a compensation parameter strategy, and performing structural design optimization on the structural distribution characteristics of the multi-point out-of-focus lens to be optimized. According to the invention, the technical problems of over-correction or insufficiency caused by defocus design unification in the prior art, serious stray light at the connection position of the edge of the multi-point micro lens and the circle of the lens and insufficient processing and assembling precision are solved, and the technical effect of improving the correction precision and visual stability of the lens is achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of optical design, and in particular to a design optimization method for a multi-point defocus lens for ametropia and a lens thereof. Background Art

[0002] With the continuous increase in the number of myopic people and the growing demand for vision correction, refractive error correction technology has been widely used in the field of ophthalmic lens design. However, with the increase in user personalized needs and the deepening of myopia prevention and control research, traditional lens design methods have gradually shown technical limitations. For example, the traditional uniform defocus design cannot meet the needs of users with different refractive powers, resulting in unsatisfactory correction effects; the stray light phenomenon at the edge of the lens is obvious, which reduces the overall optical performance; the lack of a high-precision marking system during the processing and assembly process can easily cause angle deviation and affect wearing stability. Summary of the invention

[0003] The present application provides a multi-point defocus lens design optimization method for refractive errors and its lens, which are used to solve the technical problems of the prior art that the unified defocus design leads to over-correction or under-correction, serious stray light at the edge of the lens, and insufficient processing and assembly accuracy.

[0004] In view of the above problems, the present application provides a multi-point defocus lens design optimization method for refractive error and a lens thereof.

[0005] In a first aspect of the present application, a method for designing and optimizing a multi-point through-focus lens for ametropia is provided, the method comprising: Analyze the structural distribution characteristics of the multi-point defocus lens to be optimized, perform multi-refractive simulated optical tests on the structural distribution characteristics of the multi-point defocus lens to be optimized, and obtain the defocus filling rate, aberration, and astigmatism correction effects; perform partition aggregation according to the defocus filling rate, aberration, and astigmatism correction effects to obtain the deviation area and deviation characteristics of each diopter; construct an optimization parameter library, which includes microlens array parameters, point array structure parameters and their corresponding optical optimization relationships; perform compensation target analysis according to the deviation area and deviation characteristics of each diopter to obtain optical target parameters; use the optical target parameters as constraints to perform matching search in the optimization parameter library to determine the compensation parameter strategy; and perform structural design optimization on the structural distribution characteristics of the multi-point defocus lens to be optimized based on the compensation parameter strategy.

[0006] The second aspect of the present application provides a lens, which is obtained by any of the multi-point defocus lens design optimization methods for refractive error described in the first aspect, and has a multi-point defocus lens optimization design structure obtained by any of the multi-point defocus lens design optimization methods for refractive error described in the first aspect.

[0007] One or more technical solutions provided in this application have at least the following technical effects or advantages: The present application analyzes the structural distribution characteristics of a multi-point defocus lens to be optimized, performs a multi-refractive simulated optical test on the structural distribution characteristics of the multi-point defocus lens to be optimized, and obtains defocus filling rate, aberration, and astigmatism correction effects; performs partition aggregation according to the defocus filling rate, aberration, and astigmatism correction effects to obtain deviation areas and deviation characteristics of each diopter; constructs an optimization parameter library, which includes microlens array parameters, point array structure parameters and their corresponding optical optimization relationships; performs compensation target analysis according to the deviation areas and deviation characteristics of each diopter to obtain optical target parameters; uses the optical target parameters as constraints to perform a matching search in the optimization parameter library to determine a compensation parameter strategy; and performs structural design optimization on the structural distribution characteristics of the multi-point defocus lens to be optimized based on the compensation parameter strategy. The present invention solves the technical problems in the prior art that the unified defocus design leads to over-correction or under-correction, serious stray light at the edge of the lens, and insufficient processing and assembly precision. Through nonlinear defocus distribution, optical simulation test and nano-lithography process, the structural characteristics of multi-point defocus lenses are analyzed, and optical performance data are obtained. Combined with the optimization parameter library, partition aggregation and compensation target analysis are performed, the best parameter strategy is matched, the microlens layout and angle are optimized, and the edge stray light bands are reduced, so as to achieve the technical effect of improving the correction precision of the lens and the visual stability. BRIEF DESCRIPTION OF THE DRAWINGS

[0008] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0009] Figure 1 A schematic diagram of a flow chart of a multi-point defocus lens design optimization method for ametropia provided in an embodiment of the present application; Figure 2 A schematic diagram of a flow chart for analyzing the structural distribution characteristics of a multi-point defocus lens to be optimized in a method for designing and optimizing a multi-point defocus lens for refractive error provided in an embodiment of the present application. DETAILED DESCRIPTION

[0010] The present application provides a multi-point defocus lens design optimization method for refractive errors and its lens, which are used to solve the technical problems of over-correction or under-correction caused by the unified defocus design in the prior art, serious stray light at the edge of the lens, and insufficient processing and assembly precision. Through nonlinear defocus distribution, optical simulation testing and nano-lithography technology, the structural characteristics of the multi-point defocus lens are analyzed, and optical performance data are obtained. Combined with the optimization parameter library, partition aggregation and compensation target analysis are performed, the optimal parameter strategy is matched, the microlens layout and angle are optimized, and the edge stray light bands are reduced, so as to achieve the technical effect of improving the correction accuracy of the lens and visual stability.

[0011] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application.

[0012] It should be noted that any variations of the terms "include" and "have" are intended to cover non-exclusive inclusions. For example, a process, method, system, product or server that includes a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or modules that are not explicitly listed or inherent to these processes, methods, products or devices.

[0013] Embodiment 1, as Figure 1 As shown, the present application provides a method for designing and optimizing a multi-point through-focus lens for refractive error, the method comprising: Step S100: analyzing the structural distribution characteristics of the multi-point defocus lens to be optimized, performing a multi-refractive simulated optical test on the structural distribution characteristics of the multi-point defocus lens to be optimized, and obtaining the defocus filling rate, aberration, and astigmatism correction effects.

[0014] In the embodiment of the present application, the key parameters of each microlens are first extracted, including the radius of curvature, arrangement spacing and distribution density, and the edge astigmatism characteristics of the microlens adjacent to the lens surface are recorded. By locating and integrating these parameters, a structural distribution characteristic model of the lens is established. The structure of the lens is not limited to microlenses or microcylindrical lenses, and its defocus range is +2.00D to +5.50D, and the diameter of the central optical zone of the lens is between 6 and 9mm.

[0015] Based on this model, an optical test of multi-refractive simulation is carried out. First, the user's refractive clustering characteristics, such as the diopter range, axial length and pupil diameter, are obtained, and an optical simulation model is constructed in combination with the lens structure characteristics. During the test, the refractive correction effect of the lens is simulated by adjusting the diopter in the simulation model; the propagation characteristics of light in the eyeball are analyzed by combining ray tracing technology; and the restriction of the pupil on the incidence of light is simulated by the aperture size, so as to comprehensively evaluate the optical performance of the lens.

[0016] Finally, based on the optical simulation results, the defocus filling rate, aberration and astigmatism correction effects are analyzed and obtained.

[0017] Further, such as Figure 2 As shown, in the method provided in the embodiment of the application, the analysis of the structural distribution characteristics of the multi-point defocus lens to be optimized also includes: Extract key parameters of each microlens, including curvature radius, arrangement spacing, and distribution density; extract edge astigmatism band parameters at the position where the microlens is adjacent to the lens surface; and locate and integrate the curvature radius, arrangement spacing, distribution density, and edge astigmatism band parameters according to the distribution positioning of the microlens to obtain the structural distribution characteristics of the multi-point defocus lens to be optimized.

[0018] In the embodiment of the present application, analyzing the structural distribution characteristics of the multi-point defocus lens to be optimized requires combining nanolithography technology with microlens technology, which is gradually achieved through the following steps to ultimately obtain the structural distribution characteristics of the lens.

[0019] First, the lens is scanned by an interferometer and a 3D surface profiler to extract the key parameters of the microlens, including the radius of curvature, arrangement spacing, and distribution density. The radius of curvature is used to reflect the optical focusing ability of the microlens, the arrangement spacing determines the distribution regularity and uniformity of the microlens, and the distribution density indicates the coverage of the microlens in the optical area.

[0020] Then, the parameters of the edge astigmatism band at the position where the microlens and the lens surface are adjacent are extracted through ray tracing technology and polarization interference experiments. Ray tracing technology records the distribution area of ​​the astigmatism band by simulating the propagation path of light at the edge of the microlens; polarization interference experiments further measure the width and intensity of the astigmatism band and quantify the impact of optical discontinuity on the overall performance of the lens. This step obtains the width, light intensity characteristics and distribution law of the astigmatism band, providing an important basis for subsequent optimization.

[0021] Finally, according to the actual distribution of microlenses, the curvature radius, arrangement spacing, distribution density and edge astigmatism band parameters are integrated according to the microlens position. The specific operation includes aligning the key parameters of the microlenses point by point, and marking the edge astigmatism band characteristics of the corresponding positions to ensure the consistency and integrity of these parameters in space. Through this integration method, the structural distribution characteristics of the multi-point defocus lens to be optimized are obtained.

[0022] Through the above steps, the structural distribution characteristics of the multi-point defocus lens to be optimized are finally obtained, that is, the key optical parameters of the microlens and the detailed characteristics of the edge astigmatism band.

[0023] Furthermore, in the method provided in the embodiment of the application, a multi-refractive simulated optical test is performed on the structural distribution characteristics of the multi-point defocus lens to be optimized to obtain the defocus filling rate, aberration, and astigmatism correction effect, and further includes: Obtain user refractive clustering characteristics, including refractive power range, axial length, and pupil diameter; construct an optical simulation model based on the structural distribution characteristics of the multi-point defocus lens to be optimized, and simulate the optical simulation model according to the user refractive clustering characteristics, wherein the refractive correction effect of the lens is simulated by adjusting the refractive power in the optical simulation model, the light propagation characteristics are obtained by ray tracing simulation using the axial length as a parameter, and the restriction of the pupil on the incidence of light is simulated by adjusting the aperture size of the optical simulation model; analyze the defocus filling rate, aberration, and astigmatism correction effects according to the simulation results of the optical simulation model to obtain optical test results.

[0024] In the embodiment of the present application, the user's refractive clustering features are first obtained, including the diopter range, axial length and pupil diameter. The diopter range is measured by an automatic ophthalmometer, the axial length is measured by an axial measuring instrument (such as A-ultrasound or optical biometer), and the pupil diameter is recorded by a dynamic pupil measurement device (infrared pupil measurement instrument) under different lighting conditions. The range of pupil diameters (such as 2mm to 6mm).

[0025] Then, based on the structural distribution characteristics of the multi-point defocus lens to be optimized (such as the radius of curvature, arrangement spacing, distribution density, and edge astigmatism characteristics of the microlens), an optical simulation model is constructed using optical simulation software (such as Zemax and TracePro). Specifically, the physical parameters and optical properties of the lens are first input to ensure that the model can accurately reproduce the actual behavior of the lens; then, according to the user's refractive clustering characteristics, the simulation environment is set, including the axial length, corneal curvature, and retinal focus position, to simulate the conditions that match the user's eye optical system. After this simulation model is built, the optical performance simulation stage begins.

[0026] In the optical performance simulation, the optical simulation model is adjusted according to the user's refractive clustering characteristics. First, the refractive correction effect of the lens is simulated by adjusting the refractive parameters in the simulation model. Specifically, by modifying the average additional refractive power distribution of the microlens area (such as +2.00D to +5.00D), the accuracy of the lens focusing on light under different refractive conditions is analyzed, and the focus distribution characteristics after refractive correction are recorded. Then, the user's axial length is used as the key parameter of optical simulation, and the propagation path of light from the lens surface to the retina is simulated by ray tracing technology. The light propagation trajectory is analyzed by ray tracing software, the matching degree between the focal position and the retinal position is recorded, the defocus deviation of the light is quantified, and the defocus intervention effect of the lens is further evaluated. Specifically, the focal position of the light on the main optical axis after passing through the lens is determined by ray tracing technology, and the retinal position is set as the ideal imaging plane, combined with the user's axial length simulation setting. In the propagation path of each light ray, the absolute distance between its focal position and the retinal position is calculated, and the average deviation value of the defocus area is calculated by the statistical data of the light in the area. The defocus deviation is displayed through a heat map or distribution map to generate a visualization of the defocus distribution in the area, providing quantitative data for evaluating the defocus intervention effect of the lens. Next, by adjusting the aperture size in the simulation model, the limiting effect of the user's pupil on the incident light is simulated. The dynamic range of the user's pupil diameter (such as 2mm to 6mm) is input to observe the changes in light transmission rate under different pupil sizes, as well as the impact of the light incident range on the optical performance of the lens (such as imaging brightness and clarity). These simulation processes fully reflect the comprehensive performance of the lens in refractive correction, light propagation, and incident light adjustment.

[0027] According to the simulation results of the simulation model, the defocus filling rate, aberration and astigmatism correction effects are further analyzed. The defocus filling rate analysis uses ray tracing data to count the focus distribution range of the defocus area and calculate the ratio of the focus density in the defocus area to the ideal focus density, that is, the focus distribution ratio of the defocus area. For example, the density of the focus in the defocus area can be quantified by a heat map or a focus distribution map, and the difference between it and the ideal density can be calculated to obtain the defocus filling rate; the aberration analysis is combined with the imaging deviation calculation to quantify the aberration types such as spherical aberration, coma and astigmatism, and generate an aberration analysis curve to show the impact of aberration on imaging quality; the astigmatism correction effect analysis combines the focus distribution data and the imaging clarity index to quantify the reduction ratio of astigmatism intensity, evaluate the correction ability of the lens for the astigmatism area, and compare the imaging resolution changes before and after correction. These performance data generate specific visual outputs through simulation software, including defocus filling rate distribution maps, aberration curves and astigmatism correction performance maps.

[0028] Finally, the analysis data of defocus fill rate, aberration and astigmatism correction effect are integrated to generate optical test results, which show the optical performance of the lens in detail and provide a scientific basis for the design optimization of the lens.

[0029] Step S200: performing partition aggregation according to the defocus filling rate, aberration, and astigmatism correction effect to obtain deviation areas and deviation characteristics of each diopter.

[0030] Furthermore, in the method provided in the embodiment of the application, partition aggregation is performed according to the defocus filling rate, aberration, and astigmatism correction effect to obtain the deviation area and deviation characteristics of each diopter, and further includes: Based on the user's refractive clustering characteristics, the range of each functional partition is set, and the functional partition includes an intermediate zone, an intermediate transition zone, a peripheral defocus zone, and an astigmatism correction zone; according to the range of each functional partition, the defocus filling rate, aberration, and astigmatism correction effect are positioned and projected according to the partition range to obtain the optical test results of each functional partition; based on the optical test results of each functional partition, aggregation is performed to obtain the aggregated optical test characteristics of the functional partition; refractive type characteristics are aggregated according to the aggregated optical test characteristics of functional partitions with different refractive powers to obtain the aggregated characteristics of each functional partition with different refractive powers, all functional partitions are integrated and spliced ​​to obtain the deviation areas and deviation characteristics of each refractive power.

[0031] In the embodiment of the present application, the functional partition range is first set based on the user's refractive clustering characteristics, and the optical area of ​​the lens is divided into four functional areas: the middle area, the middle transition area, the peripheral defocus area, and the astigmatism correction area by combining the physical characteristics of the lens and the user's refractive clustering characteristics (diopter range, axial length, pupil diameter, etc.) through optical simulation software (such as Zemax or TracePro). Among them, the middle area, as the optical center, is mainly responsible for refractive correction and provides clear vision; the middle transition area connects the middle area with the peripheral area to reduce optical mutations and improve the smoothness of the transition; the peripheral defocus area produces a defocus effect by adjusting the distribution of microlenses to control the progression of myopia; the astigmatism correction area is designed for astigmatism correction to improve visual clarity. The partitioning process is completed through user data input, lens optical parameter setting, and functional area division.

[0032] Then, the optical performance data is positioned and projected according to the functional partition range. Through ray tracing technology, the path of light propagating from the lens surface to the retina is simulated to extract and locate the optical performance data. In the intermediate zone, focus on the refractive correction effect, and count the uniformity and clarity of the focus distribution; in the intermediate transition zone, analyze the smooth connection of the optical performance and quantify the continuity of the transition; in the peripheral defocus zone, measure the defocus filling rate, and evaluate the distribution uniformity and intervention effect of the defocus zone by counting the proportion and density of the focus distribution in the defocus area; in the astigmatism correction area, record the shape and size changes of the astigmatism spot, and analyze the astigmatism correction effect. The optical performance data of each partition is mapped through simulation software to generate optical test results for each functional partition, including defocus filling rate distribution, aberration characteristics, and astigmatism correction effect.

[0033] Then, the optical test results of each functional partition are aggregated. Specifically, the same amount of optical test results of the functional partitions are first merged, and the test data is divided into multiple test quantity partitions; then, the difference of adjacent areas is determined based on multiple test quantity partitions. When the difference of optical performance of adjacent partitions is less than the preset test deviation threshold, neighborhood aggregation is performed to integrate these partition data into a whole; when the difference of adjacent partitions is not less than the test deviation threshold, the data of these partitions are retained independently. Finally, by repeating this difference determination and aggregation process until the difference of all adjacent partitions is not less than the test deviation threshold, the aggregated optical test features of the functional partitions are finally generated.

[0034] Then, the refractive type features are aggregated according to different refractive power ranges. The aggregated optical test features of the functional partitions are classified and sorted according to the average additional refractive power range (such as +2.00D to +5.00D) to generate aggregated features for different refractive power ranges. For example, for the average additional refractive power range of +3.00D, the aggregated features extracted in the middle zone may include a defocus fill rate between 40% and 60%, indicating that the density of the focus distribution is high and uniform within the target area, the mean spherical aberration is 0.02, and the astigmatism correction strength is improved by 50%. By integrating and summarizing the data of each partition, an aggregated feature table of the refractive type is formed to clarify the optical performance indicators of each refractive power range, including the changing trend of the defocus fill rate, the distribution law of the aberration characteristics, and the improvement of the astigmatism correction effect.

[0035] Finally, all functional partitions are integrated to generate the deviation area and deviation characteristics of each diopter. The functional partition data of each diopter range are spliced ​​into the overall optical performance model of the lens through optical simulation software, and the deviation area in each diopter range is marked to generate an overall description including the defocus filling rate, aberration characteristics and the distribution law of astigmatism correction effect.

[0036] Furthermore, in the method provided in the embodiment of the application, the optical test results of each functional partition are aggregated to obtain the aggregated optical test features of the functional partition, and further includes: The optical test results of the functional partitions are merged with the same quantities to obtain multiple test quantity partitions; adjacent area difference determination is performed based on the multiple test quantity partitions, and when the adjacent partition difference is less than the test deviation threshold, neighborhood aggregation is performed; when the adjacent partition difference is not less than the test deviation threshold, the test quantity partition is retained; all adjacent area difference determinations are repeated until all adjacent partition differences are not less than the test deviation threshold, to obtain the aggregated optical test characteristics of the functional partition.

[0037] In an embodiment of the present application, the optical test results of the functional partitions are first merged with the same quantity. In each functional partition, the optical test results are classified and integrated according to the same type of indicators (such as defocus filling rate, aberration, and astigmatism correction effect). For example, for the defocus filling rate, the proportion of focus distribution in the defocus area is counted, and the average focus density of each partition is calculated; for aberration, the overall test results in the partition are extracted as the representative value of the area; for the astigmatism correction effect, the correction performance in the area is statistically performed. In this way, multiple test quantity partitions are generated, and the data of each partition are the overall test values ​​of the optical performance indicators.

[0038] Next, the difference determination of adjacent areas is performed based on multiple test quantity partitions. For the data of two adjacent partitions, their absolute difference in a certain optical performance index is calculated. Specifically, for the two adjacent partitions, test quantity partition 1 and test quantity partition 2, their absolute difference in a certain optical performance index is calculated. Among them, test quantity partition 1 and test quantity partition 2 respectively represent the test values ​​of two adjacent partitions on a certain optical performance (such as defocus filling rate, aberration or astigmatism correction effect). Through difference calculation, compare the difference with the preset test deviation threshold (for example, 5%). When the difference is less than the threshold, it is considered that the difference in optical performance of the two partitions is small and meets the aggregation condition.

[0039] When the aggregation conditions are met, neighborhood aggregation is performed. The core of neighborhood aggregation is to merge the data of two partitions and calculate the optical performance value of the new partition by simple arithmetic average. For example, if the defocus filling rate of test volume partition 1 is 80% and the defocus filling rate of test volume partition 2 is 75%, then the defocus filling rate of the new test volume partition is .

[0040] When the difference between adjacent partitions is not less than the set test deviation threshold, it is considered that the optical performance differences between these partitions are significant and not suitable for aggregation. At this time, these partition data are retained as independent test volume partitions to retain the local characteristics of the regional optical performance.

[0041] Repeat the above difference determination and neighborhood aggregation steps, and process all adjacent test volume partitions one by one until the difference of all adjacent partitions is not less than the set test deviation threshold. At this point, all the areas that can be aggregated have been merged, and the areas with large differences remain independent.

[0042] Finally, the aggregated optical test characteristics of the functional partitions are obtained. These characteristics include the overall optical performance of each functional partition, such as the comprehensive value of the defocus filling rate, the characteristic value of the aberration distribution, and the improvement value of the astigmatism correction effect.

[0043] Step S300: constructing an optimization parameter library, wherein the optimization parameter library includes microlens array parameters, dot array structure parameters and their corresponding optical optimization relationships.

[0044] In the embodiment of the present application, an optical experiment is first performed based on the microlens array parameters and the dot array structure parameters, and the experimental data is collected and an experimental database is established. Then, based on the experimental database, the microlens array parameters (such as distribution mode, arrangement density and curvature radius) and the corresponding optical effects are analyzed, and the relationship between the microlens array parameters and the optical performance is extracted through data mining to obtain the optical optimization relationship of the microlens array parameters. Then, the relationship between the dot array structure parameters (such as dot array depth and dot array spacing) and the optical performance of the lens is mined, and the influence of the well-shaped fine line characteristics of the dot array structure parameters engraved on the edge on the optical effect is clarified, and the optical optimization relationship of the dot array structure parameters is generated. Finally, the optical optimization relationship between the microlens array parameters and the dot array structure parameters is parameter-decomposed and mapped, and these data are integrated to finally construct an optimization parameter library.

[0045] Furthermore, in the method provided in the embodiment of the application, the optimization parameter library is constructed, and the optimization parameter library includes microlens array parameters, point array structure parameters and their corresponding optical optimization relationships, and also includes: Based on the microlens array parameters and the point array structure parameters, an optical experiment is performed to obtain an experimental database; based on the experimental database, the distribution mode, arrangement density, radius of curvature of the microlens array parameters and the corresponding optical effects are mined to obtain the optical optimization relationship of the microlens array parameters; based on the experimental database, the point array depth, point array spacing and the corresponding optical effects of the edge point array structure parameters at the adjacent position of the microlens and the lens surface are mined to obtain the optical optimization relationship of the point array structure parameters; the optical optimization relationship of the microlens array parameters and the optical optimization relationship of the point array structure parameters are parameter relationship decomposition and mapping, and the optimization parameter library is constructed.

[0046] In the embodiment of the present application, an optical experiment is first performed based on the microlens array parameters and the point array structure parameters to establish an experimental database. In the sample preparation stage, nanolithography and electron beam lithography are used to make microlens arrays and point array structure samples, and a variety of parameter combinations are designed. The microlens array parameters include the radius of curvature, arrangement density, and distribution mode (regular arrangement or random arrangement), and the point array structure parameters include the point array depth and the point array spacing. The optical performance data of the sample is collected by high-precision optical measurement equipment (such as laser interferometers, beam analyzers, and scatterometers), and key optical indicators such as defocus filling rate, spot size, aberration distribution, and astigmatism correction effect are recorded. All test data are classified by parameter combination and stored in the experimental database, providing a basis for subsequent data mining and relationship analysis.

[0047] Then, based on the experimental database, the relationship between microlens array parameters and optical performance is mined. First, the key parameters of the microlens are screened, including the radius of curvature, arrangement density and distribution mode, and optical simulation software (such as Zemax) is used to simulate the influence of different parameter combinations on optical performance, and calculate indicators such as focal position, light energy distribution and defocus filling rate. At the same time, the accuracy of the simulation results is verified by experimental data. For example, a smaller radius of curvature can enhance the focusing ability, but may lead to an increase in spherical aberration; a higher arrangement density improves the uniformity of light distribution, but may introduce more diffraction effects; regular arrangement is suitable for improving focus accuracy, while random arrangement helps reduce edge aberrations. In the data mining process, multivariate regression analysis is used to construct a quantitative relationship between the radius of curvature, arrangement density, distribution mode and optical performance, and finally generate an optical optimization relationship for microlens array parameters.

[0048] Subsequently, the optical performance relationship of the dot array structure parameters was mined to obtain the optical optimization relationship of the dot array structure parameters. The focus was on studying the influence of the dot array depth and dot array spacing on the light propagation path and diffraction effect. In the experiment, the arrangement density and well-shaped fine line characteristics of the dot array were analyzed to evaluate its optimization effect on the refraction and scattering of light in the edge area of ​​the lens. Specifically, a deeper dot array structure can enhance the deflection ability of light and improve the uniformity of astigmatism correction; while a smaller dot array spacing can increase the diffraction intensity and optimize the edge defocus filling rate, but may increase the local stray light intensity. By extracting relevant data from the experimental database, the effect of the dot array structure on the refraction and deflection of light was analyzed using a scattering spectrometer, and the beam deflection angle and spot distribution at different dot array depths were recorded. Through fitting tools (such as MATLAB), the mapping relationship between the dot array depth and dot array spacing and optical performance (such as astigmatism correction ability and diffraction efficiency) was constructed, the optimal configuration range of each parameter was clarified, and the optical optimization relationship of the dot array structure parameters was generated.

[0049] Finally, the optical optimization relationship of the microlens array parameters is integrated with the optical optimization relationship of the dot array structural parameters to construct an optimization parameter library. First, the individual optimization relationships of the two types of parameters are layered and decomposed, such as the radius of curvature and the arrangement density as one layer, and the dot array depth and the dot array spacing as another layer. Then, the interaction between the microlens parameters and the dot array structural parameters is studied through multivariate analysis (such as experimental design method, DOE), such as how the arrangement density affects the diffraction performance of the dot array spacing, and whether different curvature radii change the deflection effect of the dot array depth. In the optimization process, the arrangement mode of the dot array (regular and random) and the curvature radius are jointly modeled to further clarify the synergy between the parameters, especially in the improvement of edge light scattering and the improvement of diffraction efficiency. All parameter relationships are mapped to a unified optimization database and stored in the form of a query table. For example, according to the optical performance target (such as improving the defocus fill rate or reducing aberrations), the optimal parameter combination of the radius of curvature, arrangement density, dot array depth and dot array spacing is queried.

[0050] Furthermore, in the method provided in the embodiment of the application, constructing the optimization parameter library further includes: According to the optical optimization relationship of the microlens array parameters, experimental analysis of functional partition radius and defocus gradient distribution is performed based on the microlens array parameters to determine the optical optimization relationship between the partition radius and the defocus gradient distribution; the optical optimization relationship between the partition radius and the defocus gradient distribution is decomposed and mapped into a parameter relationship and added to the optimization parameter library.

[0051] In the embodiment of the present application, firstly, based on the optical optimization relationship of the microlens array parameters (including core parameters such as curvature radius, arrangement density and distribution mode), an experimental analysis of the functional partition radius and defocus gradient distribution is carried out. The experimental design involves the division of functional partitions, including the middle zone, the middle transition zone and the peripheral defocus zone. The partition radius and defocus gradient distribution of each zone directly affect the optical performance of the lens. The partition radius refers to the radius range of each functional zone, and the defocus gradient distribution is the law of the change of the defocus amount with the partition position. The experiment evaluates the impact on the optical performance by adjusting the radius of the middle zone (such as from 6 mm to 8 mm) and the peripheral defocus gradient distribution range (such as +2.00D to +5.00D). Optical simulation software is used in combination with actual tests to record the key optical performance indicators under different configurations, including focus distribution, defocus filling rate and light energy uniformity. Through experimental analysis, it is clear that the optimal partition radius range is 6 mm to 7 mm, the optimal defocus gradient distribution range is +3.00D to +4.50D, and the optical optimization relationship between the partition radius and the defocus gradient distribution is determined.

[0052] The optical optimization relationship between the partition radius and the defocus gradient distribution is then parameter-mapped and added to the optimization parameter library. Specifically, first, the determined optical optimization relationship is parameter-decomposed, and the respective ranges of the partition radius and the defocus gradient distribution are recorded independently, while the corresponding optical performance results are associated. Then, by analyzing the relationship between these parameters and the microlens array parameters, their synergistic optimization effects under different combinations are studied. For example, when the radius of curvature is 10 microns and the arrangement density is 300 per square millimeter, how does the optimal range of the partition radius and the defocus gradient change? Finally, these parameters are integrated into the optimization parameter library and structured as a multi-layer query table, allowing users to quickly retrieve the best configuration combination based on specific optical requirements (such as optimizing imaging clarity or improving peripheral defocus fill rate).

[0053] Through the above steps, the optical optimization relationship between the partition radius and the defocus gradient distribution is integrated into the optimization parameter library.

[0054] Step S400: performing compensation target analysis according to the deviation areas and deviation characteristics of each diopter to obtain optical target parameters.

[0055] In the embodiment of the present application, in order to perform compensation target analysis and obtain optical target parameters according to the deviation areas and deviation characteristics of each diopter, the optical performance differences of each deviation area are first quantitatively analyzed. The deviation area refers to the difference range between the actual optical performance and the ideal design performance in different functional areas of the lens, including functional areas such as the intermediate area, peripheral defocus area, and astigmatism correction area. These areas may show characteristics such as insufficient defocus filling rate, uneven aberration distribution, or poor astigmatism correction effect. Through optical simulation and experimental testing, the actual optical performance indicators of these areas, such as defocus filling rate, aberration value and astigmatism correction parameters, are accurately measured to clarify the specific areas where deviations exist and their characteristics.

[0056] After the deviation area is identified, the optical performance in each area is further analyzed in detail to determine the target that needs to be compensated. First, the performance deviation is quantified to calculate the difference between the actual performance and the theoretical target value in each functional area. For example, for the defocus fill rate, the defocus fill rate deviation is quantified by counting the ratio of the focus density in the defocus area to the ideal focus density, that is, the focus distribution ratio in the defocus area; for aberrations, the absolute difference between the actual aberration value and the design value is used for quantitative analysis. The results of the deviation quantification directly reflect the improvement needs of each functional area in terms of optical performance.

[0057] Then, based on the specific manifestations of the performance deviation, the compensation target for each area is analyzed step by step. For example, for the middle area, if the deviation quantification results show that the aberration is high, the compensation target is to reduce the aberration, and the corresponding adjustment parameters may include optimizing the radius of curvature of the microlens array or redefining the radius range of the functional partition. For the peripheral defocus area, if the quantification results show that the defocus fill rate is insufficient, the compensation target is to increase the defocus fill rate, which may require adjusting the range of the defocus gradient or optimizing the distribution pattern of the defocus gradient.

[0058] After the compensation target is determined, specific optical target parameters are derived, which are directly used to guide design optimization. The derivation process includes calculating the influence of adjusted optical parameters (such as curvature radius, partition radius or defocus gradient value) on defocus fill rate and aberration. Specifically, these adjusted parameters are input through an optical simulation model (such as Zemax) to simulate and analyze the improvement effect on defocus fill rate and aberration. For example, if the curvature radius is adjusted, the simulation model will calculate the influence of the new curvature on the focus distribution and aberration; if the defocus gradient is adjusted, its influence on the light distribution in the defocus area is simulated. If the simulation results show that the improvement is in line with expectations, that is, the defocus fill rate is increased and the aberration is reduced, then these parameters are confirmed as the final optical target parameters.

[0059] Through the above steps, a set of precise optical target parameters are finally obtained. These parameters include specific adjustment values ​​of functional partitions, such as the optimized value of the curvature radius of the middle zone, the extended range of the peripheral defocus gradient, etc.

[0060] Step S500: Taking the optical target parameters as constraints, a matching search is performed in the optimization parameter library to determine a compensation parameter strategy.

[0061] In the embodiment of the present application, the obtained optical target parameters are first input into the optimization parameter library as constraints. The optical target parameters are specific design adjustment values ​​obtained based on the analysis of each diopter deviation area and deviation characteristics, such as the optimization range of the functional partition radius (such as 6 mm to 7 mm) or the target range of the defocus gradient distribution (such as +3.00D to +4.50D). These parameters clarify the optical performance goals that need to be achieved by the lens design and are the core basis for the formulation of the compensation strategy.

[0062] Next, perform a parameter matching search in the optimization parameter library. The optimization parameter library contains the optical optimization relationship between microlens array parameters (such as curvature radius, arrangement density, and distribution method) and functional partition parameters (such as partition radius and defocus gradient distribution). By matching conditions, select the design combination that meets the optical target parameters. For example, if the optical goal requires reducing aberrations in the middle area, select the configuration that meets the aberration reduction conditions within the curvature radius; if the goal is to increase the peripheral defocus fill rate, select the parameter combination that meets the gradient optimization.

[0063] After screening, the suitability of candidate parameter combinations is verified. The optical performance data stored in the optimization parameter library is directly used to confirm whether the candidate combinations meet the optical goals. For example, verify whether the defocus fill rate value meets the target requirements and whether the middle area has clear imaging capabilities. If some combinations do not meet specific optical performance, they are excluded and other qualified combinations are selected.

[0064] Finally, the final compensation parameter strategy is determined, which includes a set of specific design parameters, such as the optimized value of the microlens curvature radius or the adjustment range of the defocus gradient distribution.

[0065] Step S600: performing structural design optimization on the structural distribution characteristics of the multi-point defocus lens to be optimized based on the compensation parameter strategy.

[0066] In the embodiment of the present application, based on the compensation parameter strategy, when the structural design optimization is performed on the structural distribution characteristics of the multi-point defocus lens to be optimized, the curvature radius, arrangement density and distribution mode of the microlenses are first adjusted according to the design requirements specified in the compensation parameter strategy to meet the optical performance requirements of different functional partitions. At the same time, the radius range and defocus gradient distribution of the functional partitions are re-divided to make the structures of the intermediate zone, transition zone and peripheral defocus zone more in line with the design goals, thereby optimizing the structural distribution characteristics of the overall lens.

[0067] Furthermore, the method provided in the application embodiment also includes: According to the structural design optimization results, the functional partition boundaries and the microlens array angles are identified; according to the functional partition boundaries and the microlens array angles, the center position and the functional angle position are determined; and the center position and the functional angle position are used to mark the lens installation processing position.

[0068] In the embodiment of the present application, according to the structural design optimization results, the functional partition boundaries and the microlens array angles are first identified and determined. The functional partition boundaries are the division positions of different optical functional areas (such as the middle area, transition area, and peripheral defocus area) on the lens. These boundaries are directly determined by the partition radius and gradient distribution specified in the optimization design. The microstructure pattern precisely engraved by the nanolithography process provides a basis for the identification of the partition boundaries. The optical microlens arrangement characteristics on the surface of the lens are detected by high-resolution surface scanning technology (such as laser confocal microscopy), and the boundary lines of each functional area are quickly identified and locked by analyzing the density changes and the edges of the optical pattern. At the same time, according to the directional characteristics of the microlens array, the array angles of each functional partition are extracted by the arrangement rules. These angles reflect the arrangement direction of the microlenses and are used for the direction calibration during the subsequent lens installation. After this step, the position of the functional partition boundary and the microlens array angle are determined.

[0069] Next, the center position and the functional angle position are determined based on the functional partition boundary and the microlens array angle. The center position is the geometric center or optical center of the lens and is the core reference point for lens installation and processing. The center point of the middle area is directly calculated as the center position through the data of the functional partition boundary. The functional angle position is an identification point determined based on the main arrangement direction of the microlens array, which is used to indicate the angle direction when the lens is installed. For example, by analyzing the arrangement rules of the microlenses in the middle area and the transition area, the axis direction with the most significant arrangement direction is selected as the functional angle position.

[0070] Finally, the center position and functional angle position are used to complete the lens installation processing position marking. In order to avoid permanent damage to the lens, invisible marks or external auxiliary tools are used for non-invasive positioning. For example, low-visibility dot or short line marks are engraved on the surface of the lens through nanolithography. These marks are invisible under ordinary light and can only be identified by polarized light or ultraviolet light detection equipment under specific optical conditions. Another method is to use the alignment system of the processing equipment, load the coordinate information of the center position and functional angle position, and use external fixtures or laser alignment devices to achieve precise positioning of the lens. In these ways, the geometric and directional positioning of the lens installation is completed, while avoiding the optical influence of the marks when the user wears it.

[0071] Through the above steps, the functional partition boundaries and the microlens array angles are accurately identified, the center position and the functional angle position are clearly located, and finally the lens installation and processing position marking is achieved through invisible marking or equipment assistance.

[0072] In the embodiments of the present application, in summary, the embodiments of the present application have at least the following technical effects: The present application analyzes the structural distribution characteristics of a multi-point defocus lens to be optimized, performs a multi-refractive simulated optical test on the structural distribution characteristics of the multi-point defocus lens to be optimized, and obtains defocus filling rate, aberration, and astigmatism correction effects; performs partition aggregation according to the defocus filling rate, aberration, and astigmatism correction effects to obtain deviation areas and deviation characteristics of each diopter; constructs an optimization parameter library, which includes microlens array parameters, point array structure parameters and their corresponding optical optimization relationships; performs compensation target analysis according to the deviation areas and deviation characteristics of each diopter to obtain optical target parameters; uses the optical target parameters as constraints to perform a matching search in the optimization parameter library to determine a compensation parameter strategy; and performs structural design optimization on the structural distribution characteristics of the multi-point defocus lens to be optimized based on the compensation parameter strategy. The present invention solves the technical problems in the prior art that the unified defocus design leads to over-correction or under-correction, serious stray light at the edge of the lens, and insufficient processing and assembly precision. Through nonlinear defocus distribution, optical simulation test and nano-lithography process, the structural characteristics of multi-point defocus lenses are analyzed, and optical performance data are obtained. Combined with the optimization parameter library, partition aggregation and compensation target analysis are performed, the best parameter strategy is matched, the microlens layout and angle are optimized, and the edge stray light bands are reduced, so as to achieve the technical effect of improving the correction precision of the lens and the visual stability.

[0073] Embodiment 2: Based on the inventive concept of the multi-point defocus lens design optimization method for refractive error in the aforementioned embodiment, the second aspect of the present application provides a lens, which is obtained by any of the multi-point defocus lens design optimization methods for refractive error, and has a multi-point defocus lens optimization design structure obtained by any of the multi-point defocus lens design optimization methods for refractive error, and the lens optimization design structure includes microlens array parameters and a point array structure.

[0074] It should be noted that the above-mentioned sequence of the embodiments of the present application is only for description and does not represent the advantages and disadvantages of the embodiments. And the above-mentioned specific embodiments of this specification are described. The processes depicted in the accompanying drawings do not necessarily require the specific order and continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0075] The above description is only a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present application shall be included in the protection scope of the present application.

[0076] This specification and drawings are merely exemplary illustrations of the present application and are deemed to cover any and all modifications, variations, combinations or equivalents within the scope of the present application. Obviously, a person skilled in the art may make various modifications and variations to the present application without departing from the scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the present application and its equivalents, the present application intends to include these modifications and variations.

Claims

1. A method for designing and optimizing a multi-point defocus lens for ametropia, characterized in that: include: Analyze the structural distribution characteristics of the multi-point defocus lens to be optimized, perform multi-refractive simulation optical testing on the structural distribution characteristics of the multi-point defocus lens to be optimized, and obtain the defocus filling rate, aberration, and astigmatism correction effects; Performing partition aggregation according to the defocus filling rate, aberration, and astigmatism correction effect to obtain deviation areas and deviation characteristics of each diopter; Constructing an optimization parameter library, wherein the optimization parameter library includes microlens array parameters, dot array structure parameters and their corresponding optical optimization relationships; Perform compensation target analysis according to the deviation areas and deviation characteristics of each diopter to obtain optical target parameters; Taking the optical target parameters as constraints, performing a matching search in the optimization parameter library to determine a compensation parameter strategy; Based on the compensation parameter strategy, structural design optimization is performed on the structural distribution characteristics of the multi-point defocus lens to be optimized.

2. The method for designing and optimizing a multi-point defocus lens for ametropia according to claim 1, wherein: The analysis of the structural distribution characteristics of the multi-point defocus lens to be optimized includes: Extract the key parameters of each microlens, including the radius of curvature, arrangement spacing, and distribution density; Extract the edge astigmatism band parameters of the position where the microlens is adjacent to the lens surface; According to the distribution and positioning of the microlenses, the curvature radius, arrangement spacing, distribution density and edge astigmatism band parameters are positioned and integrated to obtain the structural distribution characteristics of the multi-point defocus lens to be optimized.

3. The method for designing and optimizing a multi-point defocus lens for ametropia according to claim 2, wherein: The structural distribution characteristics of the multi-point defocus lens to be optimized are subjected to a multi-refractive simulation optical test to obtain the defocus filling rate, aberration, and astigmatism correction effects, including: Obtain user refractive clustering features, including diopter range, axial length, and pupil diameter; Based on the structural distribution characteristics of the multi-point defocus lens to be optimized, an optical simulation model is constructed, and the optical simulation model is simulated according to the user refractive clustering characteristics, wherein the refractive correction effect of the lens is simulated by adjusting the refractive power in the optical simulation model, the light propagation characteristics are obtained by ray tracing simulation by taking the axial length as a parameter, and the restriction effect of the pupil on the incidence of light is simulated by adjusting the aperture size of the optical simulation model; The defocus filling rate, aberration and astigmatism correction effects are analyzed according to the simulation results of the optical simulation model to obtain optical test results.

4. The method for designing and optimizing a multi-point defocus lens for ametropia according to claim 2, wherein: According to the defocus filling rate, aberration, and astigmatism correction effect, partition aggregation is performed to obtain the deviation area and deviation characteristics of each diopter, including: Based on the user's refractive clustering characteristics, the range of each functional partition is set, and the functional partition includes an intermediate area, an intermediate transition area, a peripheral defocus area, and an astigmatism correction area; According to the functional partition ranges, the defocus filling rate, aberration, and astigmatism correction effects are positioned and projected according to the partition ranges to obtain optical test results of each functional partition; Aggregating the optical test results of each functional partition to obtain aggregated optical test features of the functional partition; Refractive type features are aggregated according to the aggregated optical test features of functional partitions with different refractive powers to obtain aggregated features of each functional partition with different refractive powers, and all functional partitions are integrated and spliced ​​to obtain the deviation areas and deviation features of each refractive power.

5. The method for designing and optimizing a multi-point defocus lens for ametropia according to claim 4, characterized in that: Aggregating the optical test results of each functional partition to obtain aggregated optical test features of the functional partition includes: Merging the same quantities of the optical test results of the functional partitions to obtain multiple test quantity partitions; Based on the multiple test quantity partitions, adjacent area difference determination is performed, when the adjacent partition difference is less than the test deviation threshold, neighborhood aggregation is performed, and when the adjacent partition difference is not less than the test deviation threshold, the test quantity partition is retained; The determination of the differences of all adjacent regions is repeated until the differences of all adjacent partitions are no less than the test deviation threshold, thereby obtaining the aggregated optical test characteristics of the functional partitions.

6. The method for designing and optimizing a multi-point defocus lens for ametropia according to claim 1, wherein: The construction of the optimization parameter library includes microlens array parameters, point array structure parameters and their corresponding optical optimization relationships, including: Based on the microlens array parameters and the dot array structure parameters, an optical experiment is performed to obtain an experimental database; According to the experimental database, the distribution mode, arrangement density, curvature radius of the microlens array parameters and the corresponding optical effects are mined to obtain the optical optimization relationship of the microlens array parameters; According to the experimental database, the relationship between the dot array depth, the dot array spacing and the corresponding optical effect is mined for the edge dot array structure parameters at the adjacent position of the microlens and the lens surface, so as to obtain the optical optimization relationship of the dot array structure parameters; The optical optimization relationship of the microlens array parameters and the optical optimization relationship of the dot array structure parameters are decomposed and mapped to construct the optimization parameter library.

7. The method for designing and optimizing a multi-point defocus lens for ametropia according to claim 6, wherein: Constructing the optimization parameter library also includes: According to the optical optimization relationship of the microlens array parameters, an experimental analysis of the functional partition radius and the defocus gradient distribution is performed based on the microlens array parameters to determine the optical optimization relationship between the partition radius and the defocus gradient distribution; The optical optimization relationship between the partition radius and the defocus gradient distribution is decomposed and mapped with parameters, and added to the optimization parameter library.

8. The method for designing and optimizing a multi-point defocus lens for ametropia according to claim 4, wherein: Also includes: According to the structural design optimization results, identify the functional partition boundaries and microlens array angles; Determine the center position and the functional angle position according to the functional partition boundary and the microlens array angle; The center position and functional angle position are used to mark the lens installation processing position.

9. A lens, characterized in that: The lens is obtained by the multi-point defocus lens design optimization method for refractive error as described in any one of claims 1-8, and has a multi-point defocus lens optimized design structure obtained by the multi-point defocus lens design optimization method for refractive error as described in any one of claims 1-8.

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