Optical system glass selection and optimization method

Abstract

The present application relates to the technical field of optical design, and specifically provides an optical system glass selection and optimization method, which first establishes a candidate glass database according to optical system constraints, and determines the material role of each lens element to limit the glass family allowed to be selected by each lens element, unitizes each lens element in the optical system, generates multiple local candidate glass combinations for each glass optimization unit, constructs an agent loss function to evaluate and optimize the local candidate glass combinations, arranges and combines the selected local candidate glass combinations according to the optical system requirements to generate a global candidate glass combination, performs optimization again and then performs real ray tracing verification, finally establishes a real performance evaluation function to evaluate the real performance of each selection scheme, and generates an engineering report containing multiple recommended selection schemes from different angles. The present application solves the problems of traditional lens glass selection, such as high dependence on artificial experience, long design and verification time, etc.

Description

Technical Field

[0001] This invention belongs to the field of optical design technology, and in particular relates to a method for selecting and optimizing glass for optical systems. Background Technology

[0002] The imaging quality of an optical system is determined not only by structural parameters such as curvature, thickness, air gap, aperture position, and aspherical coefficient, but also by material properties such as refractive index, dispersion, relative partial dispersion, internal transmittance, thermo-optical coefficient, coefficient of thermal expansion, density, aperture availability, and processing cost of the optical glass selected for each lens element. For broadband imaging systems, high-resolution lenses, ultraviolet or deep ultraviolet systems, and high-power laser optical systems, glass selection typically directly affects axial chromatic aberration, magnification chromatic aberration, second-order spectral density, monochromatic aberration balance, thermal focus shift, system transmittance, and manufacturing risk.

[0003] Existing methods for selecting optical glass generally fall into three categories. The first category is a manual selection method based on Abbe diagrams, glass diagrams, and the designer's experience. This type of method primarily relies on the refractive index. Abbe number Relative partial dispersion This method also relies on experience with glass families to make judgments. For example, a combination of crown glass and flint glass is used for general achromatic purposes, while a combination of low-dispersion glass and anomalously partially dispersed flint glass is used for apochromatic or APO designs. This method is physically intuitive, but it is highly dependent on the designer's experience and makes it difficult to automatically determine the material role of a specific lens element in the system.

[0004] The second category consists of glass replacement or glass expert tools in optical design software. These tools typically allow users to replace specified glass in a real glass library and improve image quality through local optimization. Their advantages include the ability to integrate with real ray tracing, error functions, and structural constraints; however, their disadvantages include an overly broad search range if prior material role identification and glass family hierarchical screening are lacking, insufficient physical specificity of candidate glass combinations, and potential over-reliance on local optimization results.

[0005] The third category involves directly enumerating the glass library or using a global optimization algorithm to search for glass combinations. This method is feasible when the number of components involved in the replacement is small; however, for multi-element optical systems, the number of candidate combinations increases exponentially with the number of participating components. For example, if there are... Each lens element retains [a certain number of lens elements]. There are 10 candidate glasses, with approximately 100 combinations. Many combinations have no engineering significance in terms of color difference balance, refractive index continuity, transmittance, aperture, and supply risk, resulting in low computational efficiency, many failure candidates, and difficulty in explaining the physical rationale of glass combinations.

[0006] One proposed approach involves designers pre-defining several glass categories based on their experience with glass families, then using optical design software for glass replacement and local optimization. While this approach improves the efficiency of manual glass adjustment, it still has two shortcomings: First, the glass family is usually subjectively specified by the designer, without considering the role of each lens element in the optical system based on its optical power, rim ray height, principal ray height, thickness, and aperture. Second, there is a lack of a unified surrogate loss function before candidate glass combinations enter actual ray tracing, making it impossible to quickly screen glass combinations considering paraxial chromatic aberration, anomalous dispersion, transmittance, thermal stability, and manufacturing feasibility. Summary of the Invention

[0007] In view of this, the present invention aims to provide a method for selecting and optimizing glass in optical systems. It designs a standardized closed-loop design process that includes complete lens element material role identification, glass family layer screening, proxy evaluation and ranking, real ray tracing optimization, and robustness verification. The design process no longer relies entirely on human experience, which facilitates the formation of traceable engineering reports and design basis.

[0008] To achieve the above objectives, the technical solution created by this invention is implemented as follows: This invention provides a method for selecting and optimizing glass in an optical system, comprising: S1: Obtain the structural parameters, operating wavelength, field of view, aperture, evaluation indicators, and engineering constraints of the optical system, and establish a candidate glass database; S2: Based on the optical power, rim ray height, principal ray height, element thickness, and element aperture of each lens element in the optical system, calculate the material role index of each lens element. The material role index shall include at least one of the following: the tendency of the lens element to control axial chromatic aberration, magnification chromatic aberration, monochromatic aberration, and thermal stability. The material role of each lens element is determined based on its material role index. The material role includes at least one of the following: axial chromatic aberration control, magnification chromatic aberration control, monochromatic aberration control, and thermal stability control. S3: The allowed glass family is limited according to the material role corresponding to each lens element; S4: Construct glass optimization units, each glass optimization unit including at least one lens element; generate corresponding local candidate glass combinations for each glass optimization unit; S5: Construct a proxy loss function, use the proxy loss function to calculate the proxy evaluation value of the local candidate glass combination corresponding to each glass optimization unit, and select M local candidate glass combinations from low to high according to the proxy evaluation value; S6: Arrange and combine the M local candidate glass combinations selected by each glass optimization unit to generate global candidate glass combinations for multiple optical systems, and use the surrogate loss function to calculate the system-level surrogate evaluation value of each global candidate glass combination. Select N global candidate glass combinations from low to high according to the system-level surrogate evaluation value. S7: Perform real ray tracing verification on the selected N global candidate glass combinations, and release at least one structural variable for optimization; S8: Establish the true performance evaluation function of the entire optical system based on the performance evaluation index of the entire optical system, calculate the true performance evaluation value of the global candidate glass combination after verification by real ray tracing and optimization of structural variables based on the true performance evaluation function, and determine the global glass combination of the optical system based on the true performance evaluation value.

[0009] Preferably, the structural parameters of the optical system include at least one of the following: the surface curvature, thickness, air gap, effective aperture, aperture position, and aspherical parameters of the lens element.

[0010] Preferably, the engineering constraints include at least one of the following: permitted glass manufacturers, prohibited glass list, maximum cost level, minimum internal transmittance, maximum supply risk level, and maximum caliber demand.

[0011] Preferably, after establishing the candidate glass database, the glass data in the candidate glass database is also subjected to engineering hard screening. If the glass data in the candidate glass database meets any of the following criteria, it is removed from the candidate glass database: The operating wavelength differs from that of optical systems; The internal transmittance does not meet the requirements of the optical system; The maximum available aperture does not meet the requirements of the optical system; The cost level does not meet the requirements of the optical system; Supply risks do not meet optical system requirements; It is a type of glass that is prohibited from use.

[0012] Preferably, the formula for calculating the material role index of the lens element is: ; ; ; ; in, As a material role indicator used to characterize the tendency of lens elements to control axial chromatic aberration, As a material role indicator used to characterize the tendency of lens elements to control chromatic aberration at magnification, As a material role indicator used to characterize the tendency of lens elements to control monochromatic aberration, As a material role indicator used to characterize the tendency of lens elements to control thermal stability; This represents the height of the edge ray at the i-th lens element. This represents the height of the principal ray at the i-th lens element. This represents the optical power of the i-th lens element. Indicates the thickness of the lens element. This indicates the aperture of the lens element.

[0013] Preferably, the material role of the lens element is determined by weighting each material role index of the lens element, or by determining the material role of the lens element based on the material role index with the largest value.

[0014] Preferably, the glass optimization unit is a single piece, a glued assembly, or an air gap color correction assembly.

[0015] Preferably, the proxy loss function is: ; in, This represents the agent's evaluation value. , , , , and Both represent custom weights. These are parameters used to characterize first-order color difference balance. These are parameters used to characterize the ability to compensate for dispersion in second-order spectra or anomalous parts. This is a parameter used to characterize the deviation of the refractive index of a candidate glass assembly from that of an initial glass assembly. The parameter used to characterize transmittance loss, These are parameters used to characterize thermal stability risk. These are parameters used to characterize the feasibility risks of materials engineering. ; ; ; ; ; ; in, The symbol is for optical power; the lens element is positive optical power. Taking a positive value, the lens element has a negative optical power. Take the negative, Represents a collection of lens elements. For intermediate parameters, For Abbe numbers, For anomalous partial dispersion, Let be the refractive index of the candidate glass combination. The refractive index of the initial glass assembly. Indicates the thickness of the lens element. Indicates the aperture of the lens element. Internal transmittance, Thermo-optic coefficient, The coefficient of thermal expansion is... For cost level, To mitigate supply risks, To maximize the risk of obtaining the caliber, , and For parameter weights.

[0016] The preferred true performance evaluation function is:

[0017] in, This represents the true performance evaluation value. This represents the wave aberration of an optical system. Indicates the optical system at a specified spatial frequency The modulation transfer function at that point, Indicates axial color difference or color shift. Indicates color difference by magnification. Indicates the transmittance of an optical system. This is a penalty item.

[0018] Preferably, in S8: after calculating the true performance evaluation value of the global candidate glass combination after verification by real ray tracing and optimization of structural variables, the method further includes: applying a perturbation to the global candidate glass combination after verification by real ray tracing and optimization of structural variables, and constructing a robust evaluation function as follows: ; in, Indicates the robustness score of the optical system. This represents the performance evaluation value obtained by real ray tracing of the global candidate glass combination under undisturbed conditions. This represents the standard deviation of the performance evaluation value obtained by real ray tracing of the global candidate glass combination after applying a preset number of perturbations. This represents the minimum performance evaluation value obtained by real ray tracing of the global candidate glass combination after applying a preset number of perturbations. The robustness evaluation function is used to calculate the robustness evaluation value of the global candidate glass combination after verification by real ray tracing and optimization of structural variables, which is used to reflect the robustness of each global candidate glass combination.

[0019] Compared with the prior art, the present invention can achieve the following beneficial effects: Compared to traditional methods that rely on manual experience to select glass, this invention solves the problem that traditional methods lack quantitative judgment of the function of each lens element in the system. Starting from the inside of the optical system, by calculating the optical power, edge ray height, principal ray height, thickness, and aperture of each lens element, it constructs multi-dimensional role discrimination parameters such as axial chromatic aberration effect index, magnification chromatic aberration effect index, monochromatic aberration effect index, and thermal stability effect index. This achieves parameterized automatic identification of the role of lens materials, classifies the main control elements of axial chromatic aberration, magnification chromatic aberration, monochromatic aberration, and thermal stability, and matches different glass family screening strategies to them, avoiding the drawback of using the same screening standard for all lens elements.

[0020] Compared to the combinatorial explosion problem faced by direct full-glass library enumeration methods, this invention designs engineering hard screening and glass family hierarchical screening, and then selects high-value, low-risk candidate combinations through a surrogate loss function. This ensures that a large number of combinations with weak physical meaning, high manufacturing risk, or engineering unusability are eliminated before entering real ray tracing and structural optimization. A small number of high-value, low-risk candidate glass combinations are sent to real ray tracing and structural co-optimization, and robustness and candidate glass verification are performed. This greatly reduces the number of candidates and ray tracing calculation time, improves the physical rationality of candidate combinations, and can quickly obtain glass combinations that take into account image quality, chromatic aberration, transmittance, thermal stability, and manufacturing feasibility, significantly improving the efficiency of glass selection and optimization.

[0021] Compared to simple software-automated glass replacement methods, this invention not only performs glass replacement and structural optimization, but also introduces material roles, glass family constraints, and proxy pre-evaluation before replacement, and incorporates real performance ranking, robustness verification, and alternative glass output after replacement. The result not only has superior nominal image quality, but also takes into account chromatic aberration control, transmittance, thermal stability, cost, aperture availability, and supply risks, ensuring the feasibility of the design. Furthermore, the robustness verification step ensures that the final selected solution not only performs excellently under nominal conditions, but also maintains good optical performance under disturbed environments, exhibiting good optical robustness.

[0022] Furthermore, this invention integrates engineering hard screening, material role identification, glass family stratified screening, proxy evaluation and ranking, real ray tracing optimization, robustness verification, and glass selection output into a complete design closed loop. Each step's screening criteria and evaluation results have clear physical meanings and quantifiable mathematical expressions, allowing designers to clearly trace the reasons for retaining or eliminating each group of candidate glasses. Simultaneously, the weights of different factors during the design process can be flexibly adjusted according to the system type, enabling designers to freely switch between different engineering objectives such as image quality priority, cost priority, high transmittance priority, low thermal drift priority, or supply security priority. The glass selection process exhibits high repeatability and auditability. Attached Figure Description

[0023] The accompanying drawings, which form part of this invention, are used to provide a further understanding of the invention. The illustrative embodiments and descriptions of the invention are used to explain the invention and do not constitute an undue limitation of the invention. In the drawings: Figure 1 This is a flowchart of the selection and optimization of optical system glass according to an embodiment of the present invention; Figure 2 This is a schematic diagram of the role recognition of lens element materials according to an embodiment of the present invention. Detailed Implementation

[0024] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are only for explaining the invention and do not constitute a limitation thereof. Similar elements in different embodiments are referred to by associated similar element reference numerals. In the following embodiments, many details are described to facilitate a better understanding of the invention. However, those skilled in the art will readily recognize that some features may be omitted in different situations, or may be replaced by other elements, materials, or methods. In some cases, some operations related to the invention are not shown or described in the specification. This is to avoid obscuring the core parts of the invention with excessive description. For those skilled in the art, detailed description of these related operations is not necessary; the relevant operations can be fully understood based on the description in the specification and general technical knowledge in the art.

[0025] It should be noted that, unless otherwise specified, the embodiments and features described in this invention can be combined to form various implementations. Furthermore, the order of the steps or actions in the method description can be changed or adjusted in a manner readily apparent to those skilled in the art. Therefore, the various orders in the specification and drawings are merely for the clear description of a particular embodiment and do not imply a mandatory order, unless otherwise stated that a particular order must be followed.

[0026] In the description of this invention, it should be understood that the terms "center," "longitudinal," "lateral," "length," "width," "thickness," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," "outer," "clockwise," and "counterclockwise," etc., indicating orientations or positional relationships based on the orientations or positional relationships shown in the accompanying drawings, are only for the convenience of describing this invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation on this invention. Furthermore, the terms "first," "second," etc., are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Thus, features defined with "first," "second," etc., may explicitly or implicitly include one or more of that feature. In the description of this invention, unless otherwise stated, "a plurality of" means two or more.

[0027] In the description of this invention, it should be noted that, unless otherwise explicitly specified and limited, the terms "installation," "connection," and "linking" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection of two components. Those skilled in the art will understand the specific meaning of the above terms in this invention based on the specific circumstances.

[0028] The invention will now be described in detail with reference to the accompanying drawings and embodiments.

[0029] Please see Figure 1 In one embodiment of the present invention, a method for selecting and optimizing glass for an optical system is provided, comprising: S1: Obtain the structural parameters, operating wavelength, field of view, aperture, evaluation indicators, and engineering constraints of the optical system, and establish a candidate glass database; S2: Based on the optical power, rim ray height, principal ray height, element thickness, and element aperture of each lens element in the optical system, calculate the material role index of each lens element. The material role index shall include at least one of the following: the tendency of the lens element to control axial chromatic aberration, magnification chromatic aberration, monochromatic aberration, and thermal stability. The material role of each lens element is determined based on its material role index. The material role includes at least one of the following: axial chromatic aberration control, magnification chromatic aberration control, monochromatic aberration control, and thermal stability control. S3: The permitted glass family is limited according to the material role corresponding to each lens element; S4: Construct glass optimization units, each glass optimization unit including at least one lens element; generate corresponding local candidate glass combinations for each glass optimization unit; S5: Construct a proxy loss function, use the proxy loss function to calculate the proxy evaluation value of the local candidate glass combination corresponding to each glass optimization unit, and select M local candidate glass combinations from low to high according to the proxy evaluation value; S6: Arrange and combine the M local candidate glass combinations selected by each glass optimization unit to generate global candidate glass combinations for multiple optical systems, and use the surrogate loss function to calculate the system-level surrogate evaluation value of each global candidate glass combination. Select N global candidate glass combinations from low to high according to the system-level surrogate evaluation value. S7: Perform real ray tracing verification on the selected N global candidate glass combinations, and release at least one structural variable for optimization; S8: Establish the true performance evaluation function of the entire optical system based on the performance evaluation index of the entire optical system, calculate the true performance evaluation value of the global candidate glass combination after verification by real ray tracing and optimization of structural variables based on the true performance evaluation function, and determine the global glass combination of the optical system based on the true performance evaluation value.

[0030] Specifically, in step S1, during the glass selection and optimization process for each lens element in the optical system, input data is first required. This input data includes: the structural parameters of the optical system, operating wavelength, field of view, aperture, evaluation indicators, and engineering constraints. Additionally, considering specific lens manufacturing, target focal length and mechanical constraints can be added as input data. Evaluation indicators are pre-defined metrics used to measure the rated performance indicators that the optical system needs to achieve, typically using image quality as the evaluation indicator. The structural parameters of the optical system specifically include the surface curvature, thickness, air gap, material, effective aperture, aperture stop position, and aspherical parameters of each lens element. The material refers to the initial glass set during the modeling of the optical system, which serves as a reference for subsequent glass selection and replacement and will be replaced by the newly selected glass in the final output scheme. In specific cases, the input conditions for structural parameters can be relaxed, selecting only one or more of the above structural parameters as input.

[0031] Engineering constraints are rigid indicators for glass selection, set after comprehensive consideration of multiple aspects such as cost, supply risk, and glass quality. Specifically, they include permitted glass manufacturers, prohibited glass lists, maximum cost tiers, minimum internal transmittance, maximum supply risk tiers, and maximum caliber requirements. Similarly, depending on the specific application scenario, the input conditions for engineering constraints can be relaxed, allowing only one or more of the above constraints to be selected as input.

[0032] Based on the input data above, a candidate glass database is established. This database should include at least the glass name, manufacturer, glass family, refractive index, Abbe number, relative partial dispersion, anomalous partial dispersion, internal transmittance, thermo-optical coefficient, coefficient of thermal expansion, density, cost grade, maximum available diameter, and supply risk level. Candidate glasses can be sourced from publicly available glass catalogs, company internal material databases, or user-defined material lists.

[0033] The process of establishing a candidate glass database is usually done by computer software to automatically filter data based on input data. After computer screening, manual verification can also be performed to further eliminate data that does not meet the design requirements.

[0034] like Figure 2 As shown, in this embodiment of the invention, taking a double Gaussian lens as an example, six glass lens elements arranged from the object side to the image side are selected as the objects for material selection in the double Gaussian lens optical system, and are denoted as E1, E2, E3, E4, E5 and E6. According to their functions, they can be divided into front positive lens, front cemented color correction group, rear cemented color correction group and rear positive lens. In the specific process of acquiring input data and establishing the candidate glass database, the candidate glass database can be constructed based on the structural parameters, working wavelength, field of view, aperture, evaluation index and engineering constraints of these six lens elements.

[0035] As an optional embodiment, after establishing the candidate glass database, candidate glass combinations are designed to eliminate unusable data for backend permutation and combination as much as possible. This embodiment of the invention also requires calculating anomalous partial dispersion and performing engineering hard screening. By calculating the deviation between the actual partial dispersion of the candidate glass and the normal value of ordinary glass, anomalous glasses are identified. This provides a quantitative basis for prioritizing the selection of these special materials to suppress secondary spectra in subsequent apochromatic or broadband systems. Simultaneously, glasses with insufficient transmittance, too small aperture, excessively high cost, or high supply risk are directly eliminated at this step. Specifically, for any candidate glass in the candidate glass database... Its relative partial dispersion is expressed as Abbe numbers are represented as Then, the normal glass line fitted from the ordinary glass sample is expressed as: Then the anomalous partial dispersion is defined as: ; when A larger value indicates that the glass exhibits more pronounced aberrant dispersion compared to normal glass lines, thus having higher priority in apochromatic or secondary spectral control. After the dispersion calculation and calibration of the abnormal parts are completed, an engineering hard screening is performed. If the glass data in the candidate glass database meets any of the following criteria, it is removed from the candidate glass database: The operating wavelength differs from that of optical systems; The internal transmittance does not meet the requirements of the optical system; The maximum available aperture does not meet the requirements of the optical system; The cost level does not meet the requirements of the optical system; Supply risks do not meet optical system requirements; It is a type of glass that is prohibited from use.

[0036] For double-Gaussian lenses, specific constraints can be imposed. For the large-aperture positive lenses in the front and rear groups, priority is given to retaining crown glass, dense crown glass, or lanthanide crown glass that are readily available, have high aperture availability, and low supply risk. For the negative lenses in the cemented color correction group, priority is given to retaining flint glass, short flint glass, heavy flint glass, or flint-type glass with anomalous partial dispersion. After this step, the glass data in the candidate glass database are all engineering-usable glass. Through the above multiple constraints, it is ensured that completely useless data in the candidate glass database is minimized before entering the actual ray tracing, avoiding the participation of unusable materials in subsequent combination searches.

[0037] In step S2, the role identification parameters of the lens elements are first obtained, including the optical power, rim ray height, principal ray height, element thickness, and element diameter of each lens element. Specifically, for the six lenses E1, E2, E3, E4, E5, and E6 in the double Gaussian lens, the above parameters need to be obtained. Specifically, for any i-th lens element, its optical power... This is usually determined during the optical system design process and can be directly used. For thinner lens elements, the optical power is... It is usually approximated as: ; in, Let be the refractive index of any i-th lens element at the reference wavelength. and These are the curvatures of the front and rear surfaces of the lens element, respectively.

[0038] For thick lenses or complex components, equivalent optical power can also be obtained by real ray tracing or paraxial ray tracing.

[0039] As for the height of the edge light Principal ray height Component thickness and component diameter All of these are determined during the system modeling and design phase and can be directly referenced during glass selection and optimization.

[0040] In a double Gaussian lens, E1 and E6 are located on the front and rear outer sides, and have a significant impact on edge lighting and the first-order quantity of the system; E2, E3, E4, and E5 form the front and rear color correction group, which are highly sensitive to axial chromatic aberration, magnification chromatic aberration, and second-order spectral balance; the principal ray height and edge ray height of the elements near the aperture jointly determine their contribution to astigmatism, field curvature, magnification chromatic aberration, and monochromatic aberration balance.

[0041] The material role indicators defined based on the acquired role recognition parameters specifically include: material role indicators used to characterize the tendency of lens elements to control axial chromatic aberration. Material role index used to characterize the tendency of lens elements to control chromatic aberration at magnification. Material role index used to characterize the tendency of lens elements to control monochromatic aberration. And material role indicators used to characterize the tendency of lens elements to control thermal stability. The calculation formulas for the above material role indicators are as follows: ; ; ; .

[0042] After calculating the material role index of each lens element in the entire optical system, its material role can be determined, that is, the role and function of the lens element in the entire optical system. Typically, the material role of a lens element is determined by weighting each material role index, but depending on design requirements, the material role can also be determined solely by the material role index with the highest value. In this embodiment of the invention, the material role is roughly defined as axial chromatic aberration control, magnification chromatic aberration control, monochromatic aberration control, and thermal stability control, corresponding to the four material role indices mentioned above.

[0043] For double Gaussian lenses, the E1 and E6 and The E2 and E3 models are prominent, playing a key role in controlling monochromatic aberration and thermal stability. Their primary functions in the system are monochromatic aberration balancing and edge ray control. Furthermore, the selection and optimization of materials must consider the potential risks associated with large-diameter manufacturing. and It stands out, playing a key role in controlling axial and magnification color differences, primarily focusing on controlling the first-order color difference and secondary spectrum within the system. E4 and E5... , and It stands out as the main controller of axial chromatic aberration, magnification chromatic aberration, and monochromatic aberration. Its role in the system is primarily to coordinate the control of group chromatic aberration, magnification chromatic aberration, and astigmatism.

[0044] In step S3, after determining the material role corresponding to each lens element, the allowed glass families can be limited. For example, for a positive lens belonging to the ordinary axial chromatic aberration dominant role, the preferred glass families include ordinary crown glass, low-dispersion crown glass, and phosphate crown glass, to provide positive optical power and reduce first-order axial chromatic aberration; for a negative lens belonging to the ordinary axial chromatic aberration dominant role, the preferred glass families include ordinary flint glass and heavy flint glass, which, together with positive crown glass, form dispersion compensation to achieve basic achromaticity; for axial chromatic aberration dominant roles in apochromatic or broadband applications... For positive lenses used in correction, the preferred glass families include fluorophosphorus crown glass, phosphate crown glass, and low-dispersion anomalous partial-dispersion glass. These utilize low dispersion and anomalous partial dispersion to reduce the secondary spectrum for broadband, APO, or high-requirement chromatic aberration correction. For negative lenses used in apochromatic or broadband correction where axial chromatic aberration is the primary control, the preferred glass families include short flint glass, anomalous partial-dispersion flint glass, and heavy short flint glass. These are used in combination with fluorophosphorus crown or low-dispersion positive lenses to improve the secondary spectrum and apochromatic capabilities. For lenses that are the primary control for magnification chromatic aberration... For positive lenses, the preferred glass families include crown glass, short flint glass, heavy flint glass, and lanthanide glass, used to provide dispersion compensation at positions with high principal ray height and control chromatic aberration at the edge field of view. For positive lenses that play a dominant role in monochromatic aberration control, the preferred glass families include lanthanide crown glass, dense crown glass, and high-refractive-index low-dispersion glass, used to increase the refractive index and reduce curvature pressure, which is beneficial for controlling spherical aberration, coma, astigmatism, and field curvature. For negative lenses that play a dominant role in monochromatic aberration control, the preferred glass families include heavy flint glass and lanthanide glass. Flint glass and high-refractive-index flint glass are used in negative lenses to provide strong refractive power, which is beneficial for controlling Petzval and field curvature. For lenses where thermal stability is the primary factor, the preferred glass families are limited to fused silica, low-absorption glass, low dn / dT glass, and low thermal expansion glass to reduce the risks of thermal focal shift, thermally induced OPD, and absorption heating. When the lens diameter or thickness is large, the selection is further limited to readily available crown glass, readily available flint glass, large-size available glass, and low-risk glass to ensure material availability, processability, and cost control. For cemented doublets or cemented assemblies, commonly used cemented combinations include: crown glass-flint glass, fluorophosphorus crown-short flint, crown glass-short flint-heavy flint, etc.

[0045] For the six lens elements in a double Gaussian lens, the front positive lens E1 and the rear positive lens E6 can be preferentially selected from crown glass, compact crown glass, lanthanide crown glass, or other glass families with high transmittance and sufficient aperture. Positive power correction lens elements E2 and E4 can preferentially be crown glass, compact crown glass, lanthanide crown glass, or low-dispersion glass. Negative power correction lens elements E3 and E5 can preferentially be flint glass, short flint glass, heavy flint glass, or anomalously partially dispersed glass. For double Gaussian lenses with near-symmetrical front and rear elements, further constraints can be imposed, requiring that the corresponding front and rear lens elements maintain consistency or similarity in their glass family selection to avoid excessive material differences between the front and rear symmetrical groups, which would make structural re-optimization difficult.

[0046] In step S4, to reduce the combination explosion caused by simultaneous enumeration of the entire system and to maintain the physical relationship between the glass combination and the system's color correction structure, this embodiment of the invention changes the traditional design of optimizing individual lenses one by one to optimizing by establishing glass optimization units. That is, based on factors such as the combined effect and correlation between different lenses, the lens elements in the optical system are divided into glass optimization units. Each glass optimization unit includes at least one lens element, so a glass optimization unit can specifically be a single lens, a cemented group, an air-spaced color correction group, or a local lens group specified by the designer. Generating candidate glass combinations on a unit-by-unit basis achieves an exponential reduction in candidate glass combinations. During the candidate glass combination generation process, the glass selection for the lens element within each glass optimization unit is enumerated within its allowed glass family, forming a large number of local candidate glass combinations.

[0047] For a double-Gaussian lens, the six glass elements are divided into four optimization units: U1={E1}, U2={E2,E3}, U3={E4,E5}, and U4={E6}. U1 and U4 represent the front and rear outer positive lens units, primarily responsible for first-order power, edge ray control, and manufacturing risk control. U2 and U3 represent the front and rear cemented color correction units, primarily responsible for axial chromatic aberration, magnification chromatic aberration, second-order spectral density, and some monochromatic aberration balance. This division eliminates the need for completely independent enumeration of the six elements in the glass combination search; instead, combinations are performed within local units with clear physical meaning, generating numerous local candidate glass combinations for each of the four optimization units U1, U2, U3, and U4.

[0048] In step S5, to screen and evaluate the local candidate glass combinations for each optimization unit, a surrogate loss function is further constructed. This surrogate loss function is used to calculate the surrogate evaluation value of the local candidate glass combinations corresponding to each glass optimization unit, thereby evaluating the quality of the local candidate glass combinations for each glass optimization unit. The constructed surrogate loss function is as follows: ; in, This represents the agent's evaluation value. , , , , and Both represent custom weights. These are parameters used to characterize first-order color difference balance. These are parameters used to characterize the ability to compensate for dispersion in second-order spectra or anomalous parts. This is a parameter used to characterize the deviation of the refractive index of a candidate glass assembly from that of an initial glass assembly. The parameter used to characterize transmittance loss, These are parameters used to characterize thermal stability risk. These are parameters used to characterize the feasibility risk of materials engineering; each term represents a non-negative penalty, with higher values ​​indicating greater risk or loss, hence the proxy evaluation value. The lower the value, the more suitable the combination is.

[0049] The detailed calculation formulas for each term in the formula are as follows: ; ; ; ; ; ; in, The symbol is for optical power; the lens element is positive optical power. Taking a positive value, the lens element has a negative optical power. Take the negative, Represents a collection of lens elements. For intermediate parameters, For Abbe numbers, For anomalous partial dispersion, Let be the refractive index of the candidate glass combination. The refractive index of the initial glass assembly. Indicates the thickness of the lens element. Indicates the aperture of the lens element. Internal transmittance, Thermo-optic coefficient, The coefficient of thermal expansion is... , and For parameter weights. This is a materials engineering feasibility risk item used to evaluate the engineering risks of candidate glass combinations in terms of cost, supply stability, and availability of glass blank diameter. Used to characterize the Cost risk of the glass selected for each lens element Used to characterize the supply risk of this glass, Used to characterize the caliber risk when the required caliber exceeds the maximum achievable caliber of the candidate glass. At that time, the risk item is zero; when At that time, the risk item of the caliber increases as the caliber of the export caliber increases. The smaller the value, the better the materials engineering feasibility of the candidate glass combination.

[0050] Based on the function of different glass optimization units, or their corresponding material roles, custom weights can be further adjusted. , , , , and For example, in a double Gaussian lens, U2 and U3 are mainly used for color calibration. and It should have a larger weight, therefore, it can be increased. and U1 and U4, on the other hand, need to be improved. , and Increase the weight , and This is to avoid excessive refractive index deviation, unavailability of large-diameter glass, or insufficient thermal stability. For a double Gaussian structure with near-symmetrical front and back, a consistency penalty term can be added to the evaluation of the front and back units to keep the material families and overall properties of U1 and U4, and U2 and U3 similar.

[0051] For each glass optimization unit, after calculating the surrogate evaluation value for each corresponding local candidate glass combination, the surrogate evaluation value is used as the basis for further calculation. Sort all local candidate glass combinations and select M local candidate glass combinations from low to high. M can be determined according to computing resources and design stage, for example, it can be 20, 50, 100 or other values.

[0052] In step S6, after selecting several local candidate glass combinations for each glass optimization unit, the glass optimization units in the optical system need to be arranged and combined to generate a system-level global candidate glass combination. The system-level surrogate evaluation value of the global candidate glass combination is then calculated to facilitate the ranking of the overall global candidate glass combinations of the optical system. Specifically, in the selection process of the dual-Gaussian lens glass, several local candidate glass combinations have been reserved for U1, U2, U3, and U4 through step S5. These are then arranged and combined into a complete six-element global candidate glass combination, and the glass selection is evaluated at the system level. The system-level screening and evaluation process still uses the surrogate loss function designed in step S5. That is, the surrogate loss function is used to calculate the system-level surrogate evaluation value of the entire optical system when using each global candidate glass combination. The global candidate glass combinations are then ranked according to the system-level surrogate evaluation value, and N global candidate glass combinations are selected from low to high according to the system-level surrogate evaluation value. The value of N can be determined based on design requirements, error tolerance, and the allowable amount of ray tracing. For a dual-Gaussian lens, the top 120 global candidate glass combinations are selected and retained for real ray tracing and structural co-optimization. This step aims to exclude a large number of low-value glass combinations before real optimization, thereby improving computational efficiency.

[0053] In step S7, for each selected global candidate glass combination, the initial glass material of the corresponding lens element is first replaced with the design material of the lens element in the global candidate glass combination. Then, real ray tracing verification is performed. At this time, at least one structural variable among curvature, thickness, air gap, image plane position, and aspherical coefficient can be released for collaborative optimization. However, during the optimization process, the effective focal length, total system length, minimum center thickness, minimum edge thickness, distortion, and image quality indicators still need to be kept within the engineering allowable range. That is, some structural parameters can be changed during the optimization process, but the final optical system objective must remain unchanged. When performing real ray tracing and structural collaborative optimization in a dual Gaussian lens, the curvature of all lens element surfaces and the center thickness of each lens element are generally used as optimization variables; if necessary, key air gap and image plane position can also be released as compensation variables.

[0054] The process of real ray tracing and structural co-optimization can be directly implemented using existing CODE V, Zemax, other commercial optical design software, or self-developed ray tracing optimization programs. As long as the optical system structure can be read, glass materials can be replaced, ray tracing can be performed, and optical performance indicators can be output, this invention can be implemented.

[0055] In step S8, after the global candidate glass combinations have been verified by real ray tracing and optimized by structural variables, a real performance evaluation function for the entire optical system needs to be established to evaluate the real ray tracing results and thus determine the quality of each global candidate glass combination. Specifically, the real performance evaluation function is as follows:

[0056] in, This represents the true performance evaluation value. Represents the wave aberration of an optical system. Indicates the optical system at a specified spatial frequency The modulation transfer function at that point, Indicates axial color difference or color shift. Indicates color difference by magnification. Indicates the transmittance of an optical system. This is a penalty item.

[0057] As an optional implementation, for different types of systems, The evaluation metrics can be replaced by dot plot radius, bounding energy, Strehl ratio, depth of focus, thermally induced OPD, or other optical performance metrics.

[0058] After calculating the true performance evaluation value for each global candidate glass combination, the true performance evaluation value is used as the basis for further calculation. Each global candidate glass combination is sorted from highest to lowest quality. Global candidate glass combinations that fail to complete real ray tracing during the actual ray tracing verification process, exhibit insufficient edge thickness, insufficient center thickness, excessive effective focal length deviation, or image quality below the threshold are directly marked as unqualified, and their actual performance evaluation value is not calculated. The remaining qualified global candidate glass combinations are listed to generate an optical system glass selection list. Each global candidate glass combination in the list corresponds to an independent glass selection scheme, referred to as a global glass combination. The actual performance evaluation value corresponding to each global glass combination represents the performance of the corresponding scheme under a undisturbed standard environment. Designers can determine the global glass combination of the optical system based on the actual performance evaluation value.

[0059] As an optional implementation, the real performance evaluation value reflects the performance under a undisturbed standard environment. Based on the application environment of the optical system, a robustness verification of the global glass combinations in the glass selection list is also proposed. This involves applying perturbations to the global candidate glass combinations (i.e., the global glass combinations in the selection list) after verification through real ray tracing and structural variable optimization. The perturbations can take the form of applying refractive index deviation, Abbe number deviation, curvature deviation, thickness deviation, air gap deviation, temperature deviation, or material melting deviation, etc. After applying the perturbations, the robustness score of the optical system corresponding to each global glass combination is calculated. The robustness score is also marked in the selection list to facilitate designers in selecting a solution based on a combination of the real performance evaluation value and the robustness score according to the application scenario.

[0060] The robustness evaluation function is constructed as follows: ; in, Indicates the robustness score of the optical system. This represents the performance evaluation value obtained by real ray tracing of the global candidate glass combination under undisturbed conditions. This represents the standard deviation of the performance evaluation value obtained by real ray tracing of the global candidate glass combination after applying a preset number of perturbations. This represents the minimum performance evaluation value obtained by real ray tracing of the global candidate glass combination after applying a preset number of perturbations.

[0061] The robustness evaluation function is used to calculate the robustness evaluation value of the global candidate glass combination after verification by real ray tracing and optimization of structural variables. This value is used to reflect the robustness of each global candidate glass combination. Through robustness verification, glass combinations that only have good performance under nominal conditions but are highly sensitive to material or manufacturing errors can be avoided.

[0062] The final selection of different global glass combinations will be ranked based on a combination of real-world performance evaluation values ​​and robustness scores. For example, if a certain global glass combination performs well under the unperturbed nominal state, i.e. If the value is relatively large, but the MTF or wavelet aberration deteriorates significantly after the perturbation, then its If a solution performs poorly, it will be given lower priority in the final ranking.

[0063] The final selection list is output, containing recommended primary options and alternative glass choices. This list can be further manually adjusted based on application scenarios and system objectives, such as calculating substitution scores based on refractive index difference, Abbe number difference, anomalous dispersion difference, transmittance difference, cost difference, and supply risk difference. The final output can include primary glass combinations, low-cost alternative combinations, high-transmittance alternative combinations, low-thermal-drift alternative combinations, and lowest-supply-risk alternative combinations, each addressing different design requirements. Furthermore, the selection list retains information on each option's glass configuration, structural parameters, image quality indicators, color difference indicators, transmittance indicators, manufacturing constraints, and risk assessments for manual verification, guiding designers in selecting the final engineering solution.

[0064] The technical solution of this invention has been verified by a dual Gaussian optical system. In addition, it can also be applied to photographic lenses, microscope objectives, telescope objectives, projection lenses, ultraviolet optical systems, infrared optical systems, and laser optical systems. For different systems, only the glass library field, material role weight, surrogate loss function weight, and real performance evaluation index need to be adjusted without changing the methodological framework of this invention.

[0065] In summary, the above description is merely a preferred embodiment of this specification and is not intended to limit the scope of protection of this specification. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this specification should be included within the scope of protection of this specification.

[0066] The systems, apparatuses, modules, or units described in one or more of the above embodiments may be implemented by a computer chip or entity, or by a product having a certain function. A typical implementation device is a computer. Specifically, a computer may be, for example, a personal computer, a laptop computer, a cellular phone, a camera phone, a smartphone, a personal digital assistant, a media player, a navigation device, an email device, a game console, a tablet computer, a wearable device, or any combination of these devices.

[0067] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0068] The various embodiments in this specification are described in a progressive manner. Similar or identical parts between embodiments can be referred to interchangeably. Each embodiment focuses on describing the differences from other embodiments. In particular, the system embodiments are basically similar to the method embodiments, so the description is relatively simple; relevant parts can be referred to the descriptions in the method embodiments.

Claims

1. A method for selecting and optimizing glass in an optical system, characterized in that, include: S1: Obtain the structural parameters, operating wavelength, field of view, aperture, evaluation indicators, and engineering constraints of the optical system, and establish a candidate glass database; S2: Based on the optical power, rim ray height, principal ray height, element thickness, and element aperture of each lens element in the optical system, calculate the material role index of each lens element. The material role index includes at least one of the following: the tendency of the lens element to control axial chromatic aberration, magnification chromatic aberration, monochromatic aberration, and thermal stability. The material role of each lens element is determined based on its material role index. The material role includes at least one of the following: axial chromatic aberration control, magnification chromatic aberration control, monochromatic aberration control, and thermal stability control. S3: The allowed glass family is limited according to the material role corresponding to each lens element; S4: Construct glass optimization units, each glass optimization unit including at least one lens element; generate corresponding local candidate glass combinations for each glass optimization unit; S5: Construct a proxy loss function, use the proxy loss function to calculate the proxy evaluation value of the local candidate glass combination corresponding to each glass optimization unit, and select M local candidate glass combinations from low to high according to the proxy evaluation value; S6: Arrange and combine the M local candidate glass combinations selected by each glass optimization unit to generate global candidate glass combinations for multiple optical systems, and use the surrogate loss function to calculate the system-level surrogate evaluation value of each global candidate glass combination. Select N global candidate glass combinations from low to high according to the system-level surrogate evaluation value. S7: Perform real ray tracing verification on the selected N global candidate glass combinations, and release at least one structural variable for optimization; S8: Establish the true performance evaluation function of the entire optical system based on the performance evaluation index of the entire optical system, calculate the true performance evaluation value of the global candidate glass combination after verification by real ray tracing and optimization of structural variables based on the true performance evaluation function, and determine the global glass combination of the optical system based on the true performance evaluation value.

2. The method for selecting and optimizing glass for an optical system according to claim 1, characterized in that, The structural parameters of an optical system include at least one of the following: the surface curvature, thickness, air gap, effective aperture, aperture position, and aspherical parameters of the lens element.

3. The method for selecting and optimizing glass for an optical system according to claim 1, characterized in that, Engineering constraints must include at least one of the following: permitted glass manufacturers, prohibited glass list, maximum cost level, minimum internal transmittance, maximum supply risk level, and maximum caliber demand.

4. The method for selecting and optimizing glass for an optical system according to claim 1, characterized in that, After establishing the candidate glass database, a hard screening process is performed on the glass data in the database. Glass data in the candidate glass database is removed from the database if it meets any of the following criteria: The operating wavelength differs from that of optical systems; The internal transmittance does not meet the requirements of the optical system; The maximum available aperture does not meet the requirements of the optical system; The cost level does not meet the requirements of the optical system; Supply risks do not meet optical system requirements; It is a type of glass that is prohibited from use.

5. The method for selecting and optimizing glass for an optical system according to claim 1, characterized in that, The formula for calculating the material role index of a lens element is: ； ； ； ； in, As a material role indicator used to characterize the tendency of lens elements to control axial chromatic aberration, As a material role indicator used to characterize the tendency of lens elements to control chromatic aberration at magnification, As a material role indicator used to characterize the tendency of lens elements to control monochromatic aberration, As a material role indicator used to characterize the tendency of lens elements to control thermal stability; This represents the height of the edge ray at the i-th lens element. This represents the height of the principal ray at the i-th lens element. This represents the optical power of the i-th lens element. Indicates the thickness of the lens element. This indicates the aperture of the lens element.

6. The method for selecting and optimizing glass for an optical system according to claim 5, characterized in that, The material role of a lens element is determined by weighting each material role index of the lens element, or by determining the material role of the lens element based on the material role index with the largest value.

7. The method for selecting and optimizing glass for an optical system according to claim 1, characterized in that, The glass optimization unit can be a single piece, a laminated assembly, or an air-gap color correction assembly.

8. The method for selecting and optimizing glass for an optical system according to claim 1, characterized in that, The proxy loss function is: ； in, This represents the agent's evaluation value. , , , , and Both represent custom weights. These are parameters used to characterize first-order color difference balance. These are parameters used to characterize the ability to compensate for dispersion in second-order spectra or anomalous parts. This is a parameter used to characterize the deviation of the refractive index of a candidate glass assembly from that of an initial glass assembly. The parameter used to characterize transmittance loss, These are parameters used to characterize thermal stability risk. These are parameters used to characterize the feasibility risks of materials engineering. ； ； ； ； ； ； in, The symbol is for optical power; the lens element is positive optical power. Taking a positive value, the lens element has a negative optical power. Take the negative, Represents a collection of lens elements. For intermediate parameters, For Abbe numbers, For anomalous partial dispersion, Let be the refractive index of the candidate glass combination. The refractive index of the initial glass assembly. Indicates the thickness of the lens element. Indicates the aperture of the lens element. Internal transmittance, Thermo-optic coefficient, The coefficient of thermal expansion is For cost level, To mitigate supply risks, To maximize the risk of obtaining the caliber, , and For parameter weights.

9. The method for selecting and optimizing glass for an optical system according to claim 1, characterized in that, The actual performance evaluation function is: in, This represents the true performance evaluation value. Represents the wave aberration of an optical system. Indicates the optical system at a specified spatial frequency The modulation transfer function at that point, Indicates axial color difference or color shift. Indicates color difference by magnification. Indicates the transmittance of an optical system. This is a penalty item.

10. The method for selecting and optimizing glass for an optical system according to claim 1, characterized in that, In step S8: after calculating the true performance evaluation value of the global candidate glass combination after verification by real ray tracing and optimization of structural variables, the method further includes: applying a perturbation to the global candidate glass combination after verification by real ray tracing and optimization of structural variables, and constructing a robust evaluation function as follows: ； in, Indicates the robustness score of the optical system. This represents the performance evaluation value obtained by real ray tracing of the global candidate glass combination under undisturbed conditions. This represents the standard deviation of the performance evaluation value obtained by real ray tracing of the global candidate glass combination after applying a preset number of perturbations. This represents the minimum performance evaluation value obtained by real ray tracing of the global candidate glass combination after applying a preset number of perturbations. The robustness evaluation function is used to calculate the robustness evaluation value of the global candidate glass combination after verification by real ray tracing and optimization of structural variables, which is used to reflect the robustness of each global candidate glass combination.

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