Lens curvature adaptive optimization design method and system for edge field-of-view correction
By combining global optimization and local fine-tuning, the lens curvature adaptive optimization design method solves the problem of edge field aberration and distortion control, realizes efficient and precise optical system design, and improves imaging quality and environmental adaptability.
Patent Information
- Application Number
- CN202610255657.5
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2026-03-04
- Publication Date
- 2026-05-15
AI Technical Summary
Existing optical design methods are difficult to optimize edge field aberration correction and distortion control in a coordinated manner, and lack adaptive decision-making capabilities. They cannot effectively integrate the influence of multi-physics field actual working conditions, resulting in the performance degradation of optical systems in practical applications.
An adaptive optimization design method for lens curvature oriented to edge field of view correction is adopted. By combining global optimization and local fine-tuning, an effective aberration factor and field of view distortion evaluation index are introduced to construct an adaptive optimization framework. The lens curvature adjustment is dynamically managed to achieve depth correction and distortion control of edge field of view aberrations, and the design is optimized in multi-physics field coupling simulation.
It significantly improves the imaging sharpness and geometric fidelity of the edge field of view, enhances the environmental robustness and design efficiency of the optical system, and ensures the stable performance of the design results under actual working conditions.
Smart Images
Figure CN122043733A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of optical lens design technology, and in particular to a lens curvature adaptive optimization design method and system for edge field-of-view correction. Background Technology
[0002] In modern optical imaging systems, with the increasing demand for wide-angle and large field-of-view imaging, image quality correction in the peripheral field of view has become a key bottleneck in improving overall performance. Traditional optical design methods typically rely on global optimization algorithms to minimize a comprehensive evaluation function covering both the central and peripheral field of view by adjusting parameters such as the overall curvature and thickness of the lens. However, this method has inherent limitations: in pursuit of the optimal value of the overall evaluation function, the optimization process often compromises between aberration correction in the central and peripheral field of view. The result is often that although the evaluation function is numerically optimized, the actual imaging quality in the peripheral field of view, especially higher-order aberrations and asymmetric aberrations, still fails to meet the requirements of high-precision applications, exhibiting significant field curvature, astigmatism, and coma, leading to blurred image edges and reduced resolution.
[0003] To specifically improve edge field of view, some improvement schemes employ a step-by-step optimization strategy, that is, after obtaining a basic structure through global optimization, fine-tuning is then performed on local areas or specific surfaces of the lens. However, such local adjustments often lack clear physical guidance and quantitative constraints, easily leading to blind trial and error. Designers typically need to manually select the adjustment area and magnitude based on experience, a cumbersome and inefficient process. More seriously, while modifying local curvature can suppress specific aberrations, it can easily introduce unpredictable nonlinear distortions, such as barrel or pincushion distortions. These distortions manifest as a warping of the image grid, which is difficult to completely correct through subsequent image processing, severely affecting visual fidelity and measurement accuracy. Therefore, existing technologies face a core challenge: how to achieve depth correction of edge field of view aberrations without significantly degrading or even effectively controlling image distortion.
[0004] Furthermore, existing optimization processes mostly remain at the level of ideal optical models, lacking integration with actual manufacturing and application environments. After lens fabrication, its surface shape undergoes slight elastic deformation under conditions such as assembly stress and temperature changes. This multiphysics coupling effect alters the optical path, causing performance degradation in designs optimized based on ideal models during actual use, and potentially revealing aberrations and distortions at the edges of the field of view. Currently, there is a lack of an optimization mechanism that can proactively incorporate these factors during the design phase and provide adaptive compensation, resulting in insufficient environmental robustness of optical systems.
[0005] In summary, the current design of large field-of-view optical systems urgently needs an integrated, adaptive optimization method that can intelligently and accurately balance edge aberration correction and distortion control, and take into account practical application environment factors. Summary of the Invention
[0006] To address the technical problems in existing technologies, such as the difficulty in coordinating edge field-of-view aberration correction and image distortion control, and the lack of adaptive decision-making capabilities and inability to effectively integrate the influence of multi-physics field actual working conditions, this invention provides a lens curvature adaptive optimization design method and system for edge field-of-view correction.
[0007] The technical solution provided by this invention is as follows: First aspect: The lens curvature adaptive optimization design method for edge field-of-view correction provided by this invention includes: S1. Obtain initial lens structure parameters, target field of view range, and edge field of view aberration tolerance; S2. Based on the initial lens structure parameters, global optimization is performed within the target field of view to obtain a first optimized lens structure. The evaluation function of the global optimization integrates the aberrations of the central field of view and the edge field of view. S3. Calculate the effective aberration factor of the first optimized lens structure at the edge of the target field of view. The effective aberration factor is used to quantitatively characterize the comprehensive performance of the lens in aberration correction at a specific off-axis angle. S4. Based on the comparison result between the effective aberration factor and the preset threshold, adaptively determine the range and intensity of the region where the curvature of a specific surface of the lens is finely adjusted, and calculate the field distortion degree of the region. The field distortion degree is used to characterize the degree of nonlinear distortion of the edge field imaging grid caused by local curvature changes. S5. With the constraint objective of minimizing the field distortion and simultaneously suppressing the effective aberration factor within the aberration tolerance, the region determined in step S4 is optimized for local curvature variables to generate a second optimized lens structure. S6. Output the second optimized lens structure as the final design result.
[0008] The second aspect: The lens curvature adaptive optimization design system for edge field-of-view correction provided by this invention includes: The parameter input module is used to obtain the initial lens structure parameters, target field of view range, and edge field of view aberration tolerance. A global optimization module is used to execute step S2 in claim 1; The performance evaluation module is used to calculate the effective aberration factor and the field distortion. The decision module is used to adaptively determine the area and intensity of curvature fine-tuning for a specific surface of the lens based on the comparison result between the effective aberration factor and the preset threshold. The local optimization module is used to perform step S5 in claim 1; The output module is used to output the final design results; The performance evaluation module, decision-making module, and local optimization module are connected in sequence to form a closed-loop feedback.
[0009] The beneficial effects of the technical solution provided by this invention include at least the following: (1) In this invention, by innovatively proposing and calculating two core evaluation indicators, "effective aberration factor" and "field distortion degree," and constructing a two-stage optimization framework of "global optimization + local fine-tuning," the problem of synergistic optimization of edge aberration and image distortion is effectively solved. The global optimization stage ensures the overall image quality baseline of the system, while the local fine-tuning stage takes the quantified distortion degree as the optimization target and the effective aberration factor as a hard constraint, thereby achieving the active and precise management of nonlinear distortion that may be caused by curvature modification while deeply suppressing edge field distortion. This optimization process based on clear physical quantities and constraints replaces the blind trial and error that relies on experience, enabling the design results to achieve a better balance between aberration correction and distortion control, and significantly improving the imaging sharpness and geometric fidelity of the edge field of view.
[0010] (2) In this invention, by establishing an adaptive decision-making mechanism based on performance evaluation, the intelligence and efficiency of the optimization process are significantly improved. The system automatically determines whether and how to initiate local fine-tuning based on the calculated effective aberration factor, and accurately locates the surface region with prominent aberration contribution by utilizing the wavefront error gradient distribution, realizing data-driven determination of the fine-tuning region and intensity. At the same time, the manufacturability index is dynamically evaluated in local optimization and the optimization weight is adjusted accordingly, ensuring that the design result not only has excellent performance but also good process feasibility. This series of adaptive decision-making closed loops reduces the over-reliance on the designer's experience, gives the optimization process a clear goal orientation and physical basis, and can automatically and efficiently find high-quality design solutions that meet complex multi-constraint conditions, shortening the design cycle and improving the design success rate.
[0011] (3) In this invention, by introducing a multiphysics coupling simulation iterative optimization process, the optical design is extended from an ideal model to the actual application environment. By importing the final optical design into a thermodynamic and structural mechanics simulation environment, its surface shape changes under real working conditions are simulated, and the deformed surface shape is fed back to the optimization process for re-evaluation and re-optimization. This allows the final design scheme to pre-compensate for performance losses that may be caused by environmental factors. This method greatly enhances the performance stability and environmental robustness of the optical system under actual temperature changes, mechanical loads, and other conditions, ensuring that the performance optimized in the laboratory can be transferred to the final product to the greatest extent possible, and meeting the stringent requirements of high-reliability application scenarios for the consistency of optical system performance. Attached Figure Description
[0012] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.
[0013] Figure 1 This is a flowchart illustrating the lens curvature adaptive optimization design method for edge field-of-view correction provided in an embodiment of the present invention. Figure 2 This is a schematic diagram of the structure of a lens curvature adaptive optimization design system for edge field of view correction provided in an embodiment of the present invention. Detailed Implementation
[0014] The technical solution of the present invention will now be described with reference to the accompanying drawings.
[0015] In embodiments of the present invention, words such as "exemplarily," "for example," etc., are used to indicate that something is an example, illustration, or description. Any embodiment or design described as "exemplary" in the present invention should not be construed as being more preferred or advantageous than other embodiments or designs. Specifically, the use of the word "exemplary" is intended to present the concept in a concrete manner. Furthermore, in embodiments of the present invention, the meaning expressed by "and / or" can be both, or either one.
[0016] In the embodiments of this invention, the terms "image" and "picture" may sometimes be used interchangeably. It should be noted that, without emphasizing the distinction between them, they convey the same meaning. Similarly, the terms "of," "corresponding (relevant)," and "corresponding" may sometimes be used interchangeably. It should be noted that, without emphasizing the distinction between them, they convey the same meaning.
[0017] In embodiments of the present invention, sometimes the subscript is as follows: It may be mistakenly written as a non-subscript form such as W1. When the distinction is not emphasized, the meaning they express is the same.
[0018] To make the technical problems, technical solutions and advantages of the present invention clearer, a detailed description will be given below in conjunction with the accompanying drawings and specific embodiments.
[0019] Reference manual attached Figure 1 The diagram shows a flowchart of the lens curvature adaptive optimization design method for edge field of view correction provided by an embodiment of the present invention.
[0020] This invention provides a lens curvature adaptive optimization design method for edge field-of-view correction. This method can be implemented by a lens curvature adaptive optimization design device for edge field-of-view correction, which can be a terminal or a server. The processing flow of the lens curvature adaptive optimization design method for edge field-of-view correction may include the following steps: S1. Obtain the initial lens structure parameters, target field of view range, and edge field of view aberration tolerance.
[0021] This step receives initial lens structure parameters input by the user through a human-computer interface or a pre-set configuration file. These parameters include at least the initial radius of curvature, thickness, refractive index of the optical material, and Abbe number of each surface of the lens. Simultaneously, the system receives specific target field-of-view values, such as the maximum half-field angle, and aberration tolerance indicators for the edge fields of view. These indicators are typically given in the form of peak-to-trough or root-mean-square values of wavefront errors, dot plot radius, or modulation transfer function descent threshold. These input parameters constitute the initial conditions and design objectives for the subsequent adaptive optimization process.
[0022] S2. Based on the initial lens structure parameters, global optimization is performed within the target field of view to obtain a first optimized lens structure. The evaluation function of the global optimization integrates the aberrations of the central field of view and the edge field of view.
[0023] This step invokes the kernel optimization engine of the optical design software, starting with the initial lens structure parameters obtained in step S1, and initiates global optimization within the specified target field of view. The optimization process automatically adjusts variables such as the lens's radius of curvature, thickness, and possible air gaps, driving a comprehensive evaluation function to converge to a minimum. This evaluation function is specially constructed, including not only control terms for central field-of-view aberrations but also mandatory evaluation terms for aberrations at multiple edge field-of-view points, thus ensuring that the optimization direction considers the imaging quality of the entire field of view, ultimately outputting a first optimized lens structure that shows improvement within the global variable space.
[0024] S3. Calculate the effective aberration factor of the first optimized lens structure at the edge of the target field of view. The effective aberration factor is used to quantitatively characterize the overall performance of the lens in aberration correction at a specific off-axis angle.
[0025] This step involves a specific evaluation of the edge field-of-view performance of the first optimized lens structure obtained in step S2. Its core is the calculation of a comprehensive index called the effective aberration factor. This calculation first requires dense ray tracing sampling on a specified target edge field-of-view ring to acquire the wavefront data of each ray. Subsequently, based on a specific algorithm model, various factors such as wavefront error, material dispersion characteristics, and principal ray deviation are integrated to generate a single value, the effective aberration factor. This factor comprehensively and quantitatively reflects the overall aberration correction level of the lens in off-axis edge conditions.
[0026] S4. Based on the comparison result between the effective aberration factor and the preset threshold, adaptively determine the range and intensity of the region where the curvature of a specific surface of the lens is finely adjusted, and calculate the field distortion degree of the region. The field distortion degree is used to characterize the degree of nonlinear distortion of the edge field imaging grid caused by local curvature changes.
[0027] This step includes a decision-making process based on the evaluation results and a local distortion assessment process. First, the effective aberration factor calculated in step S3 is compared with a pre-set performance threshold. Based on the comparison result, the system automatically determines whether local fine-tuning needs to be initiated, and further determines the lens surface requiring curvature fine-tuning, the specific location of the region on that surface, and the desired adjustment intensity. Next, within the determined region to be fine-tuned, by analyzing the changes in the imaging position of the field rays passing through the edge of the region on the image plane, and combining this with the surface geometry of the region, a scalar value called the field distortion degree is calculated. This value is specifically used to predict and quantify the nonlinear distortion risk that may be caused by local curvature modification.
[0028] S5. With the constraint objective of minimizing the field distortion and simultaneously suppressing the effective aberration factor within the aberration tolerance, the region determined in step S4 is optimized for local curvature variables to generate a second optimized lens structure.
[0029] This step initiates an additional, variable-limited optimization loop within the local region defined in step S4. The core optimization objective of this round is to minimize the field-of-view distortion calculated in step S4, while maintaining the effective aberration factor calculated in step S3 within the aberration tolerance given in step S1 as a hard constraint. The optimization process only allows adjustment of the curvature variable within the region defined in step S4, iteratively calculating to find the optimal local surface modification scheme that satisfies the above multi-objective constraints. This effectively suppresses edge aberrations while strictly controlling distortion, ultimately generating a second optimized lens structure that has undergone local fine-tuning.
[0030] S6. Output the second optimized lens structure as the final design result.
[0031] This step organizes all the final parameters of the second optimized lens structure obtained in step S5, including the curvature, thickness, material, and any newly added surface shape description coefficients, into a standard format optical design data file. This data file is completely output and stored, and can be directly used for subsequent drawing generation, optical performance verification simulation, or delivery to the manufacturing stage, serving as the final design basis for lens product production.
[0032] In one possible implementation, in step S3, the formula for calculating the effective aberration factor EA is: Where EA is the effective aberration factor, which is dimensionless; N is the number of rays uniformly sampled on the target edge field ring. Let be the field of view angle of the i-th sampled ray; Design the wavelength around the center; This represents the difference between the wavefront error of the i-th ray at its center wavelength and that of the ideal spherical wave, expressed in nanometers. The wavefront difference corresponding to the Airy disk radius at the field of view point corresponding to the i-th ray is expressed in nanometers and is used for normalization. To and The relevant field-of-view weighting factor is determined by Calculations show that Maximum target field of view; The dispersion effect coefficient is determined by the lens material in the given... Abbe number at the location Decide, The derivative of the refractive index of the lens material at the center wavelength with respect to the wavelength, expressed in units of λ / 2. ; This represents the root mean square value of the angle difference between the actual principal ray and the ideal principal ray within the edge field of view, expressed in radians.
[0033] When calculating the effective aberration factor, the system uniformly samples a predetermined number of rays along a specified target edge field of view ring. For each sampled ray, the system obtains its wavefront error at the designed central wavelength through ray tracing and compares it with the ideal spherical wavefront to obtain the difference. Simultaneously, the system calculates the wavefront difference corresponding to the Airy disk radius at the field of view point for that ray to achieve data normalization. The field of view weighting factor is obtained by processing the ratio of the ray's field of view angle to the maximum target field of view angle using a logarithmic function. The material dispersion influence coefficient is derived from the Abbe number of the lens material at the central wavelength through a specific mathematical relationship. The system further calculates the root mean square value of the angular difference between the actual principal ray and the ideal principal ray within the edge field of view. Finally, the system integrates the wavefront error, weighting factor, material dispersion characteristics, and principal ray deviation according to the defined formula, performs square root and summation operations, and outputs a dimensionless effective aberration factor value.
[0034] In one possible implementation, step S1, obtaining the initial lens structure parameters specifically includes: using a set of analytical equations combined with ray tracing back-calculation, to solve for the initial spherical radius of curvature, thickness, and material that satisfy paraxial optical characteristics based on the system focal length, relative aperture, and back working distance requirements.
[0035] When acquiring the initial lens structure parameters, the system constructs a system of simultaneous equations describing the paraxial ray behavior based on the input optical system focal length, relative aperture, and back working distance requirements. This system of equations includes object-image relationships, Lagrange invariants, and the principle of optical power distribution. The system uses numerical methods to solve this system of equations, directly obtaining a set of initial spherical radius of curvature, lens center thickness, and air gap. Simultaneously, the system selects suitable optical glass from a material library based on chromatic aberration requirements, determining its refractive index and Abbe number. This process ensures that the initial structure meets the basic imaging requirements under Gaussian optical conditions.
[0036] In one possible implementation, in step S2, the global optimization employs a hybrid optimization algorithm that includes non-sequential ray tracing, and the evaluation function additionally introduces a penalty term for the asymmetry of the edge field of view arcs and the meridional point map.
[0037] During global optimization, the system employs a hybrid optimization algorithm that integrates global search methods such as genetic algorithms or particle swarm optimization with local optimization methods such as damped least squares. The algorithm kernel integrates a non-sequential ray tracing engine to accurately evaluate aberrations of rays at large tilt angles. The constructed evaluation function includes traditional geometric aberration terms, wavefront aberration terms, and an additional penalty term. This penalty term specifically targets the edge field of view, quantifying asymmetry by calculating the absolute value of the difference between the second moments of the point distributions in the sagittal and meridional directions. This value is then multiplied by a coefficient and added to the overall evaluation function, driving the optimization process to evenly correct aberrations in both directions.
[0038] In one possible implementation, before optimizing the local curvature variable in step S5, the method further includes: based on the curvature distribution of the first optimized lens structure, introducing a continuously differentiable aspherical base term or a freeform polynomial term as a new optimization variable within the region determined in step S4.
[0039] Before initiating local curvature optimization, the system mathematically expands the surface of the fine-tuning region defined by the decision module. For rotationally symmetric surfaces, the system adds even-order aspherical coefficient terms or Zernike polynomial terms to its spherical or quadratic surface basis. For non-rotationally symmetric surfaces, XY polynomial terms or radial basis function terms are added to describe the freeform surface. The coefficients of these added surface description terms are initially set to zero and are included in the set of optimization variables. The system ensures that the added mathematical terms are continuous and differentiable throughout the region to maintain smooth ray tracing.
[0040] In one possible implementation, in step S5, the manufacturability index of the lens is dynamically evaluated after each iteration during the optimization process. If the index exceeds a preset range, the weight of the field distortion in the constrained target is automatically increased.
[0041] During the local optimization iteration process, the system calculates the manufacturability index of the current surface shape in real time after each variable update. This index is estimated based on the higher-order derivative of the surface elevation with respect to radial or planar coordinates, reflecting the complexity and manufacturability of the surface shape. The system presets reasonable upper and lower limits for this index. When the calculated index exceeds these limits, the system automatically modifies the constraint form of the optimization problem. Specifically, when constructing the Lagrangian function or penalty function, the weighting coefficient of the field distortion term is significantly increased, making the optimization objective more inclined to suppress distortion that may be exacerbated by excessive surface complexity.
[0042] In one possible implementation, in step S4, the formula for calculating the field distortion degree FD is: Where FD is the field distortion degree, which is dimensionless; M is the number of sampling points uniformly distributed within the area of the surface to be fine-tuned; This represents the displacement vector of the actual image point formed on the image plane by the specified edge field of view beam passing through sampling point j, relative to the central field of view image point, under the first optimized lens structure. This indicates that under an ideal, distortion-free imaging model, and The corresponding ideal image point displacement vector; |.| represents the magnitude of the vector; Let be the local Gaussian curvature of the lens surface at sampling point j; It is the arithmetic mean of the local Gaussian curvature of all sampling points within the surface region to be finely tuned; is the curvature change sensitivity coefficient, which is a preset positive real number.
[0043] When calculating the field-of-view distortion, the system establishes a parameterized grid and distributes sampling points on a selected fine-tuned surface region. For each sampling point, the system traces a ray cluster from a specified edge field-of-view point, with the ray cluster incident on a small region centered on that sampling point. The actual centroid coordinates of this ray on the image plane are determined through ray tracing and compared with the coordinates of the central field-of-view image point to obtain the actual displacement vector. Simultaneously, the ideal image point position for the same field-of-view point is calculated based on the paraxial magnification, yielding the ideal displacement vector. The system calculates the ratio of the magnitude of the difference vector between the two displacement vectors to the magnitude of the ideal displacement vector. Furthermore, the system calculates the local Gaussian curvature at each sampling point and the average Gaussian curvature of the entire region through surface fitting. Finally, the system multiplies the above displacement deviation ratio by an exponential attenuation factor based on the relative change in curvature according to a defined formula and averages the results to obtain the field-of-view distortion.
[0044] In one possible implementation, in step S4, the method for adaptively determining the range and intensity of the fine-tuning region is as follows: if the effective aberration factor exceeds a preset threshold, then according to the distribution of the wavefront error gradient on the lens surface, the region with a gradient magnitude greater than 1.5 times the average gradient magnitude is defined as the fine-tuning region, and the fine-tuning intensity is positively correlated with the magnitude of the gradient magnitude.
[0045] When adaptively determining the fine-tuning region and intensity, the system first compares the calculated effective aberration factor with a preset performance threshold. When the effective aberration factor exceeds the threshold, the system performs full-aperture ray tracing on the candidate lens surface and calculates the gradient vector field of the wavefront error in the field point coordinate space. The system calculates the statistical average of the gradient vector magnitudes across the entire surface. Subsequently, the system identifies all surface regions where the gradient magnitude is greater than 1.5 times the average, and merges these connected regions using a region growing algorithm to determine the final fine-tuning region. The assignment function for the fine-tuning intensity is defined as a linear function of the ratio of the gradient magnitude at that point to the average gradient magnitude, so that regions with larger gradients receive higher optimization weights.
[0046] In one possible implementation, after step S6, step S7 is further included: based on the final design results, import the thermodynamic and structural mechanics simulation environment, simulate the surface shape change of the lens under predetermined working conditions, and iterate back to step S4, using the actual surface shape under the working conditions as input to recalculate the field distortion degree, and perform adaptive optimization of multi-physics coupling.
[0047] After completing the optical design optimization, the system imports the final 3D model of the lens structure, material properties, and constraint boundary conditions into a coupled thermodynamics and structural mechanics simulation environment. The system simulates the steady-state or transient response of the lens under target operating temperature range or mechanical installation stress conditions, extracting surface deformation data caused by thermal expansion or stress. This deformed surface geometry data is mapped and converted into optical surface description parameters. Using this deformed surface as a new starting structure, the system re-executes the field-of-view distortion calculation in the performance evaluation module, triggering a new round of local curvature variable optimization loops. This iterative process continues until the imaging performance indicators under multiphysics coupled simulation conditions fully meet the preset aberration tolerance requirements.
[0048] Reference manual attached Figure 2 The diagram shows a schematic of the structure of the lens curvature adaptive optimization design system for edge field of view correction provided in an embodiment of the present invention.
[0049] This invention also provides a lens curvature adaptive optimization design system for edge field-of-view correction, applied to the lens curvature adaptive optimization design method for edge field-of-view correction, including: The parameter input module is used to obtain the initial lens structure parameters, target field of view range, and edge field of view aberration tolerance. A global optimization module is used to execute step S2 in claim 1; The performance evaluation module is used to calculate the effective aberration factor and the field distortion. The decision module is used to adaptively determine the area and intensity of curvature fine-tuning for a specific surface of the lens based on the comparison result between the effective aberration factor and the preset threshold. The local optimization module is used to perform step S5 in claim 1; The output module is used to output the final design results; The performance evaluation module, decision-making module, and local optimization module are connected in sequence to form a closed-loop feedback.
[0050] The system's data flow begins with the parameter input module. This module is specifically implemented as a data entry unit with a graphical user interface or an interface that can parse script files. It is used to receive and verify the initial lens structure parameters, target field of view, and edge field of view aberration tolerances, etc., input by the designer, and convert them into a standardized data format that is unified within the system.
[0051] The global optimization module receives standardized data from the parameter input module. In its implementation, this module encapsulates a hybrid optimization algorithm library that combines global search strategies such as genetic algorithms and particle swarm optimization with local optimization using damped least squares. This module invokes a sophisticated ray tracing engine to perform multivariate optimization within the specified full field of view, starting from the initial input parameters. Its built-in evaluation function constructor automatically synthesizes aberrations at the center and edges of the field of view and incorporates a penalty term for astigmatic asymmetry. After optimization, the module outputs first-optimized lens structure data that shows significant improvement at the global level.
[0052] The performance evaluation module is one of the core computational units of the system. It receives lens structure data from either the global optimization module or the local optimization module. This module specifically comprises two parallel evaluation sub-units. The first sub-unit is dedicated to calculating the effective aberration factor. It strictly adheres to the complex formula defined in claim 2, performing ray sampling on the target edge field-of-view ring, wavefront error calculation, material dispersion impact assessment, and principal ray deviation analysis, ultimately synthesizing the comprehensive performance index. The second sub-unit is responsible for calculating the field-of-view distortion. Based on the region information subsequently defined by the decision module, and according to the formula defined in claim 7, it quantifies and predicts the nonlinear distortion risk that local modifications may introduce by analyzing the image point displacement deviation of the specified edge beam and combining it with the regional curvature distribution. The two calculation results are output synchronously.
[0053] The decision module, acting as an intelligent control unit, takes as input the effective aberration factor calculated by the performance evaluation module. This module internally stores performance thresholds associated with aberration tolerance. The decision logic is implemented as follows: first, the effective aberration factor is compared with the threshold; if the threshold is not exceeded, the system is directly instructed to jump to the output module; if the threshold is exceeded, the fine-tuning decision procedure is triggered. This procedure analyzes the wavefront error gradient distribution map of the lens surface and strictly follows the rules described in claim 8 (e.g., defining the region where the gradient magnitude is greater than 1.5 times the average value as the fine-tuning region), automatically generating a fine-tuning instruction package containing the target lens surface number, the boundary coordinate matrix of the fine-tuning region, and the initial optimized intensity coefficients of each point within the region.
[0054] The local optimization module is a refined solver constrained by multiple objectives. It receives fine-tuning instructions from the decision module and obtains the field-of-view distortion value of the current structure from the performance evaluation module. In practice, this module first introduces aspherical or freeform surface terms as described in claim 5 as new variables into the surface mathematical description of a specified region, according to the instructions. Then, it constructs an optimization problem with minimizing the field-of-view distortion as the core objective and ensuring that the effective aberration factor does not exceed the tolerance limit as a rigid constraint. During the iterative solution process, this module integrates the manufacturability dynamic evaluation mechanism as described in claim 6; if the surface shape is too complex, it automatically adjusts the optimization model to enhance the weighting for distortion suppression. Each iteration of this module generates new lens structure data.
[0055] Specifically, the performance evaluation module, decision-making module, and local optimization module together form a closed-loop feedback loop. The new structural data generated by the local optimization module in each iteration is immediately fed back to the performance evaluation module as input to recalculate the updated field-of-view distortion. The decision-making module may also fine-tune the tuning strategy based on the latest performance evaluation. This closed-loop process continues until the convergence conditions of achieving the effective aberration factor and minimizing the field-of-view distortion are simultaneously met.
[0056] Finally, the final lens structure data that meets all convergence conditions is sent to the output module. This module is specifically implemented as a data format conversion and export interface, which packages complete design parameters, including all curvatures, thicknesses, materials, and higher-order surface coefficients, into industry-standard optical design files such as Zemax and Code V, or common data exchange formats, completing the entire automated design process and delivering it to downstream drawing generation or manufacturing stages. The beneficial effects of the technical solution provided by this invention include at least the following: (1) In this invention, by innovatively proposing and calculating two core evaluation indicators, "effective aberration factor" and "field distortion degree," and constructing a two-stage optimization framework of "global optimization + local fine-tuning," the problem of synergistic optimization of edge aberration and image distortion is effectively solved. The global optimization stage ensures the overall image quality baseline of the system, while the local fine-tuning stage takes the quantified distortion degree as the optimization target and the effective aberration factor as a hard constraint, thereby achieving the active and precise management of nonlinear distortion that may be caused by curvature modification while deeply suppressing edge field distortion. This optimization process based on clear physical quantities and constraints replaces the blind trial and error that relies on experience, enabling the design results to achieve a better balance between aberration correction and distortion control, and significantly improving the imaging sharpness and geometric fidelity of the edge field of view.
[0057] (2) In this invention, by establishing an adaptive decision-making mechanism based on performance evaluation, the intelligence and efficiency of the optimization process are significantly improved. The system automatically determines whether and how to initiate local fine-tuning based on the calculated effective aberration factor, and accurately locates the surface region with prominent aberration contribution by utilizing the wavefront error gradient distribution, realizing data-driven determination of the fine-tuning region and intensity. At the same time, the manufacturability index is dynamically evaluated in local optimization and the optimization weight is adjusted accordingly, ensuring that the design result not only has excellent performance but also good process feasibility. This series of adaptive decision-making closed loops reduces the over-reliance on the designer's experience, gives the optimization process a clear goal orientation and physical basis, and can automatically and efficiently find high-quality design solutions that meet complex multi-constraint conditions, shortening the design cycle and improving the design success rate.
[0058] (3) In this invention, by introducing a multiphysics coupling simulation iterative optimization process, the optical design is extended from an ideal model to the actual application environment. By importing the final optical design into a thermodynamic and structural mechanics simulation environment, its surface shape changes under real working conditions are simulated, and the deformed surface shape is fed back to the optimization process for re-evaluation and re-optimization. This allows the final design scheme to pre-compensate for performance losses that may be caused by environmental factors. This method greatly enhances the performance stability and environmental robustness of the optical system under actual temperature changes, mechanical loads, and other conditions, ensuring that the performance optimized in the laboratory can be transferred to the final product to the greatest extent possible, and meeting the stringent requirements of high-reliability application scenarios for the consistency of optical system performance.
[0059] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.
[0060] The following points need to be explained: (1) The accompanying drawings of the embodiments of the present invention only involve the structures involved in the embodiments of the present invention. Other structures can refer to the general design.
[0061] (2) For clarity, the thickness of layers or regions is enlarged or reduced in the drawings used to describe embodiments of the invention, i.e., these drawings are not drawn to scale. It is understood that when an element such as a layer, film, region or substrate is referred to as being “above” or “below” another element, the element may be “directly” located “above” or “below” the other element or there may be intermediate elements.
[0062] (3) Where there is no conflict, the embodiments of the present invention and the features in the embodiments can be combined with each other to obtain new embodiments.
[0063] The above are merely specific embodiments of the present invention, but the scope of protection of the present invention is not limited thereto. The scope of protection of the present invention should be determined by the scope of the claims.
Claims
1. A lens curvature adaptive optimization design method for edge field-of-view correction, characterized in that, include: S1. Obtain initial lens structure parameters, target field of view range, and edge field of view aberration tolerance; S2. Based on the initial lens structure parameters, global optimization is performed within the target field of view to obtain a first optimized lens structure. The evaluation function of the global optimization integrates the aberrations of the central field of view and the edge field of view. S3. Calculate the effective aberration factor of the first optimized lens structure at the edge of the target field of view. The effective aberration factor is used to quantitatively characterize the comprehensive performance of the lens in aberration correction at a specific off-axis angle. S4. Based on the comparison result between the effective aberration factor and the preset threshold, adaptively determine the range and intensity of the region where the curvature of a specific surface of the lens is finely adjusted, and calculate the field distortion degree of the region. The field distortion degree is used to characterize the degree of nonlinear distortion of the edge field imaging grid caused by local curvature changes. S5. With the constraint objective of minimizing the field distortion and simultaneously suppressing the effective aberration factor within the aberration tolerance, the region determined in step S4 is optimized for local curvature variables to generate a second optimized lens structure. S6. Output the second optimized lens structure as the final design result.
2. The lens curvature adaptive optimization design method for edge field-of-view correction according to claim 1, characterized in that, In step S3, the formula for calculating the effective aberration factor EA is as follows: Where EA is the effective aberration factor, which is dimensionless; N is the number of rays uniformly sampled on the target edge field ring. Let be the field of view angle of the i-th sampled ray; Design the wavelength around the center; This represents the difference between the wavefront error of the i-th ray at its center wavelength and that of the ideal spherical wave, expressed in nanometers. The wavefront difference corresponding to the Airy disk radius at the field of view point corresponding to the i-th ray is expressed in nanometers and is used for normalization. To and The relevant field-of-view weighting factor is determined by Calculations show that Maximum target field of view; The material dispersion influence coefficient is determined by the lens material at the center wavelength. Abbe number at the location Decide, The derivative of the refractive index of the lens material at the center wavelength with respect to the wavelength, expressed in units of λ / 2. ; This represents the root mean square value of the angle difference between the actual principal ray and the ideal principal ray within the edge field of view, expressed in radians.
3. The lens curvature adaptive optimization design method for edge field-of-view correction according to claim 1, characterized in that, In step S1, obtaining the initial lens structure parameters specifically includes: using the analytical equation system combined with the ray tracing back-calculation method, based on the system focal length, relative aperture, and back working distance requirements, solving for the initial spherical curvature radius, thickness, and material that satisfy the paraxial optical characteristics.
4. The lens curvature adaptive optimization design method for edge field-of-view correction according to claim 1, characterized in that, In step S2, the global optimization adopts a hybrid optimization algorithm that includes non-sequential ray tracing, and the evaluation function additionally introduces a penalty term for the asymmetry of the edge field of view arc and the meridional direction point map.
5. The lens curvature adaptive optimization design method for edge field-of-view correction according to claim 1, characterized in that, Before optimizing the local curvature variable in step S5, the method further includes: based on the curvature distribution of the first optimized lens structure, introducing a continuously differentiable aspherical base term or freeform polynomial term as a new optimization variable within the region determined in step S4.
6. The lens curvature adaptive optimization design method for edge field-of-view correction according to claim 5, characterized in that, In step S5, the manufacturability index of the lens is dynamically evaluated after each iteration during the optimization process. If the index exceeds the preset range, the weight of the field distortion in the constrained target is automatically increased.
7. The lens curvature adaptive optimization design method for edge field-of-view correction according to claim 1, characterized in that, In step S4, the formula for calculating the field distortion degree FD is: Where FD is the field distortion degree, which is dimensionless; M is the number of sampling points uniformly distributed within the surface region to be fine-tuned; This represents the displacement vector of the actual image point formed on the image plane by the specified edge field of view beam passing through sampling point j, relative to the central field of view image point, under the first optimized lens structure. This indicates that under an ideal, distortion-free imaging model, and The corresponding ideal image point displacement vector; |.| represents the magnitude of the vector; Let be the local Gaussian curvature of the lens surface at sampling point j; It is the arithmetic mean of the local Gaussian curvature of all sampling points within the surface region to be finely tuned; is the curvature change sensitivity coefficient, which is a preset positive real number.
8. The lens curvature adaptive optimization design method for edge field-of-view correction according to claim 1 or 7, characterized in that, In step S4, the method for adaptively determining the range and intensity of the fine-tuning region is as follows: if the effective aberration factor exceeds a preset threshold, then according to the distribution of the wavefront error gradient on the lens surface, the region with a gradient magnitude greater than 1.5 times the average gradient magnitude is defined as the fine-tuning region, and the fine-tuning intensity is positively correlated with the magnitude of the gradient magnitude.
9. The lens curvature adaptive optimization design method for edge field-of-view correction according to claim 1, characterized in that, After step S6, step S7 is also included: based on the final design results, import the thermodynamic and structural mechanics simulation environment, simulate the surface shape change of the lens under the predetermined working conditions, and iterate back to step S4, using the actual surface shape under the working conditions as input to recalculate the field distortion degree and perform adaptive optimization of multi-physics coupling.
10. A lens curvature adaptive optimization design system for edge field-of-view correction, used to implement the lens curvature adaptive optimization design method for edge field-of-view correction as described in any one of claims 1-9, characterized in that, include: The parameter input module is used to obtain the initial lens structure parameters, target field of view range, and edge field of view aberration tolerance. A global optimization module is used to execute step S2 in claim 1; The performance evaluation module is used to calculate the effective aberration factor and the field distortion. The decision module is used to adaptively determine the area and intensity of curvature fine-tuning for a specific surface of the lens based on the comparison result between the effective aberration factor and the preset threshold. The local optimization module is used to perform step S5 in claim 1; The output module is used to output the final design results; The performance evaluation module, decision-making module, and local optimization module are connected in sequence to form a closed-loop feedback.