Method and device for determining surface shape error tolerance of optical lens and manufacturing method of corresponding optical system

Abstract

The invention provides a method and a device for determining the surface shape error tolerance of an optical lens and a manufacturing method of a corresponding optical system. The method comprises the following steps: determining the initial PV tolerance and RMS tolerance range of the optical lens by using test experience; generating surface error point cloud data in a corresponding range; fitting the surface error point cloud data by using a Zernike polynomial, and decomposing the surface error into Zernike items of different orders; different weight coefficients are distributed according to Zernike items of different orders; determining key node positions of the optical system through simulation; evaluating the imaging quality at the key node position; and determining the PV tolerance range and the RMS tolerance range of the optical lens based on the weight coefficient of the Zernike item and the imaging quality evaluation result of the key node. The method can endow the Zernike coefficients of different orders with weights, combines the evaluation of key node aberration, optimizes the tolerance range of PV and RMS, and improves the overall imaging quality of an optical system.

Description

Technical Field

[0001] The present invention relates to the technical field of manufacturing and testing optical lenses, and in particular to a method and device for determining the surface error tolerance of an optical lens and a manufacturing method of a corresponding optical system. Background Art

[0002] The manufacturing accuracy of optical lenses determines the imaging quality of the optical system to a large extent. Generally, the quality of the imaging system can be effectively improved by controlling the surface error of the lens.

[0003] The PV (Peak-to-Valley) value and RMS (Root Mean Square) value of an optical lens are often used as a standard for measuring the surface error of the lens. The PV value represents the height difference between the highest point and the lowest point on the lens surface, and the RMS value is the root mean square value of the lens surface error. The PV error tolerance and RMS error tolerance refer to the allowable range of the PV value error and the RMS value error.

[0004] If the tolerance range of PV and RMS errors can be optimized, the overall imaging quality of the optical system can be improved. However, the current existing technology still needs to be improved in terms of optimizing the tolerance of PV and RMS errors. Summary of the invention

[0005] In view of this, the present invention provides a method and device for determining the surface error tolerance of an optical lens and a corresponding method for manufacturing an optical system to solve at least one of the above-mentioned problems.

[0006] In order to achieve the above object, the present invention adopts the following scheme:

[0007] According to a first aspect of the present invention, a method for determining the surface error tolerance of an optical lens is provided, the method comprising: determining a preliminary PV tolerance and an RMS tolerance range of the optical lens using experimental experience; generating surface error point cloud data within a corresponding range based on the preliminary PV tolerance and the RMS tolerance range; fitting the surface error point cloud data using Zernike polynomials to decompose the surface error into Zernike terms of different orders; assigning different weight coefficients according to the influence of the Zernike terms of different orders on imaging quality; determining the key node positions of the optical system through simulation; calculating the wavefront error or modulation transfer function at the key node positions to evaluate the imaging quality at the key node positions; and determining the PV tolerance range and the RMS tolerance range of the optical lens based on the weight coefficients of the Zernike terms and the imaging quality evaluation results at the key node positions.

[0008] As an embodiment of the present invention, in the above method, the surface error point cloud data is fitted using Zernike polynomials, and the surface error is decomposed into Zernike terms of different orders, including:

[0009] Step 1: Input the linear system Ax=b and tolerance e, where A is an m×n matrix consisting of surface error point cloud data and b is the observation vector;

[0010] Step 2: Calculate the initial residual r using the following formula 0 , choose the initial solution x 0 is the zero vector and sets k=0,

[0011] r 0 = b-Ax 0 ;

[0012] Step 3: Set up beta 0 =||r 0 || and unit vector Used in subsequent iterations;

[0013] Step 4: Generate a Krylov subspace by iteration. The definition of the Krylov subspace is as follows:

[0014] K k (A,r 0 )=span{r 0 ,Ar 0 ,…,A k-1 r 0};

[0015] For each iteration k, the matrix-vector multiplication v is calculated k =Ap k , p k Represents the direction vector; calculates the new iteration step factor α k And update the orthogonal vector v using the following formula k :

[0016] v k :α k =p k T v k , v k =v k -α k p k ;

[0017] Step 5: Update the residual r using the following formula k :

[0018] r k =r k-1 -α k vk-1 ;

[0019] Step 6: Calculate the new residual norm using the following formula:

[0020] β k+1 =||r k ||;

[0021] Step 7: Determine β k+1 Is it less than e? If so, stop the iteration; if not, calculate the new direction vector And calculate the new orthogonal vector v k+1 :v k+1 =Ap k+1 ;

[0022] Step 8: Update the solution using the following formula and output the final solution vector, which contains Zernike term coefficients of different orders, as well as the corresponding residuals and number of iterations:

[0023]

[0024] As an embodiment of the present invention, in the above method, assigning different weight coefficients according to the influence of the Zernike terms of different orders on the imaging quality includes:

[0025] In the optical design software, the changes of the Zernike term coefficients of different orders are input to observe their influence on the imaging quality index, and the weight coefficient of each order is determined according to the magnitude of the influence using the following formula:

[0026]

[0027] In the above formula, W n is the weight coefficient of the nth order Zernike term; ΔQ n Indicates the influence of the nth-order Zernike term on the imaging quality, ∑ΔQ n is the total effect of all Zernike terms.

[0028] As an embodiment of the present invention, the key node positions in the above method include: the center of the optical axis and / or the edge of the field of view.

[0029] As an embodiment of the present invention, after determining the PV tolerance range and RMS tolerance range of the optical lens based on the weight coefficient of the Zernike term and the imaging quality evaluation result at the key node position in the above method, the method also includes: using simulation software to input different PV and RMS values ​​in the PV tolerance range and RMS tolerance range, observing the changes in the overall imaging quality of the system, and gradually optimizing the PV tolerance range and RMS tolerance range according to the simulation results and combined with the feasibility evaluation of the optical lens manufacturing.

[0030] As an embodiment of the present invention, the generation of surface error point cloud data in the above method adopts a random error distribution model or an actual error model based on measurement data.

[0031] According to a second aspect of the present invention, there is provided a device for determining the surface error tolerance of an optical lens, the device comprising: a preliminary range determination unit, for determining the preliminary PV tolerance and RMS tolerance range of the optical lens by using experimental experience; a point cloud data generation unit, for generating surface error point cloud data within a corresponding range based on the preliminary PV tolerance and RMS tolerance range; a data fitting unit, for fitting the surface error point cloud data by using Zernike polynomials to decompose the surface error into Zernike terms of different orders; a weight assignment unit, for assigning different weight coefficients according to the influence of the Zernike terms of different orders on the imaging quality; a key node determination unit, for determining the key node positions of the optical system by simulation; a key node evaluation unit, for calculating the wavefront error or modulation transfer function at the key node positions to evaluate the imaging quality at the key node positions; a tolerance range determination unit, for determining the PV tolerance range and RMS tolerance range of the optical lens based on the weight coefficients of the Zernike terms and the imaging quality evaluation results at the key node positions.

[0032] As an embodiment of the present invention, the data fitting unit is specifically used to perform the following steps:

[0033] Step 1: Input the linear system Ax=b and tolerance e, where A is an m×n matrix consisting of surface error point cloud data and b is the observation vector;

[0034] Step 2: Calculate the initial residual r using the following formula 0 , choose the initial solution x 0 is the zero vector and sets k=0,

[0035] r 0 = b-Ax 0 ;

[0036] Step 3: Set up beta 0 =||r 0 || and unit vector Used in subsequent iterations;

[0037] Step 4: Generate a Krylov subspace by iteration. The definition of the Krylov subspace is as follows:

[0038] K k (A,r 0 )=span{r 0 ,Ar 0 ,…,A k-1r 0};

[0039] For each iteration k, the matrix-vector multiplication v is calculated k =Ap k , p k Represents the direction vector; calculates the new iteration step factor α k And update the orthogonal vector v using the following formula k :

[0040] v k :α k =p k T v k , v k =v k -α k p k ;

[0041] Step 5: Update the residual r using the following formula k :

[0042] r k =r k-1 -α k v k-1 ;

[0043] Step 6: Calculate the new residual norm using the following formula:

[0044] β k+1 =||r k ||;

[0045] Step 7: Determine β k+1 Is it less than e? If so, stop the iteration; if not, calculate the new direction vector And calculate the new orthogonal vector v k+1 :v k+1 =Ap k+1 ;

[0046] Step 8: Update the solution using the following formula and output the final solution vector, which contains Zernike term coefficients of different orders, as well as the corresponding residuals and number of iterations:

[0047]

[0048] As an embodiment of the present invention, the weight allocation unit is specifically used to: in the optical design software, input the changes of the Zernike term coefficients of different orders, observe their influence on the imaging quality index, and determine the weight coefficient of each order according to the magnitude of the influence using the following formula:

[0049]

[0050] In the above formula, W n is the weight coefficient of the nth order Zernike term; ΔQ n Indicates the influence of the nth-order Zernike term on the imaging quality, ∑ΔQ n is the total effect of all Zernike terms.

[0051] As an embodiment of the present invention, the above-mentioned key node positions include: the center of the optical axis and / or the edge of the field of view.

[0052] As an embodiment of the present invention, the above-mentioned device also includes: a tolerance range optimization unit, which is used to input different PV and RMS values ​​in the PV tolerance range and the RMS tolerance range using simulation software, observe the changes in the overall imaging quality of the system, and gradually optimize the PV tolerance range and the RMS tolerance range based on the simulation results and combined with the feasibility evaluation of the optical lens manufacturing.

[0053] As an embodiment of the present invention, the surface error point cloud data is generated by using a random error distribution model or an actual error model based on measurement data.

[0054] According to a third aspect of the present invention, there is provided a method for manufacturing an optical system, the method comprising: determining the PV tolerance range and the RMS tolerance range of the optical lens according to the method for determining the surface error tolerance of the optical lens as described above; processing the optical lens according to the determined PV tolerance range and the RMS tolerance range; performing surface error detection on the processed optical lens to confirm whether the tolerance requirements are met; evaluating the aberrations of key nodes to ensure that the set imaging quality requirements are met; applying the optical lens that meets the tolerance requirements and the node aberration requirements to the optical system and verifying the imaging quality.

[0055] According to a fourth aspect of the present invention, there is provided an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the above method when executing the computer program.

[0056] According to a fifth aspect of the present invention, there is provided a computer-readable storage medium having a computer program stored thereon, wherein the computer program implements the steps of the above method when executed by a processor.

[0057] It can be seen from the above technical scheme that the method and device for determining the surface error tolerance of an optical lens and the corresponding method for manufacturing an optical system provided by the present invention can optimize the PV and RMS tolerance range by assigning weights to Zernike coefficients of different orders and combining the evaluation of key node aberrations, thereby improving the overall imaging quality of the optical system. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the prior art descriptions. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work. In the drawings:

[0059] Figure 1 It is a flow chart of a method for determining the surface error tolerance of an optical lens provided in an embodiment of the present application;

[0060] Figure 2 It is a flow chart of a method for determining the surface error tolerance of an optical lens provided by another embodiment of the present application;

[0061] Figure 3 It is a schematic flow chart of a method for manufacturing an optical system provided in an embodiment of the present application;

[0062] Figure 4 It is a structural schematic diagram of a device for determining the surface error tolerance of an optical lens provided in an embodiment of the present application;

[0063] Figure 5 It is a schematic block diagram of the system structure of the electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0064] To make the purpose, technical solution and advantages of the embodiments of the present invention more clear, the embodiments of the present invention are further described in detail below in conjunction with the accompanying drawings. Here, the exemplary embodiments of the present invention and their descriptions are used to explain the present invention, but are not intended to limit the present invention.

[0065] like Figure 1 The figure is a flow chart of a method for determining the surface error tolerance of an optical lens provided in an embodiment of the present application, the method comprising the following steps:

[0066] Step S101: Determine the preliminary PV tolerance and RMS tolerance range of the optical lens using experimental experience.

[0067] The experimental experience here refers to the previous manufacturing and testing experience. Although it is pointed out here as experimental experience, it does not mean that this step is completed manually. The previous manufacturing and testing experience will exist in the system in the form of data, such as historical manufacturing records, test results, process parameters, etc. In this embodiment, the system can automatically retrieve this data and determine the preliminary PV tolerance and RMS tolerance range through data analysis as the basic parameters for subsequent optimization.

[0068] Step S102: generating surface error point cloud data within a corresponding range based on the preliminary PV tolerance and RMS tolerance range.

[0069] "Point cloud data" is a common term in optical engineering, computer vision, 3D modeling and other related fields. It is usually used to describe a data set consisting of a large number of discrete points that represent the surface or shape of an object in three-dimensional space. The "point cloud data" in this step refers to the numerical representation of the shape error of the optical lens surface. The generation of this data can adopt a random error distribution model or an actual error model based on measured data.

[0070] The point cloud data here needs to be within the previously determined PV and RMS tolerance ranges, because the PV tolerance defines the maximum allowable range of surface errors, and the RMS tolerance reflects the statistical characteristics of the overall error distribution. Point cloud data beyond these ranges has no analytical value. Therefore, limiting the PV and RMS tolerance ranges can avoid the generation and analysis of invalid data and ensure the effective use of computing resources. In addition, only data within a reasonable range can obtain reliable analysis results, ensuring that the final tolerance range has practical guiding significance. Therefore, the point cloud data must be within the initially determined PV and RMS tolerance ranges to ensure the effectiveness and practicality of the entire analysis process.

[0071] Step S103: fitting the surface error point cloud data using Zernike polynomials to decompose the surface error into Zernike terms of different orders.

[0072] Zernike polynomials are orthogonal polynomials widely used in the field of optics. They can effectively describe spherical, aspheric and free-form surface errors. Each term represents a specific aberration type (such as spherical aberration, coma, astigmatism, etc.). By fitting the surface error point cloud data, the surface error can be decomposed into Zernike terms of different orders.

[0073] Preferably, this step may use the LSQR method to determine the Zernike coefficients of each order during the fitting process, and may specifically include the following sub-steps:

[0074] Step 1: Input the linear system Ax=b and tolerance e, where A is an m×n matrix consisting of surface error point cloud data and b is the observation vector.

[0075] Step 2: Calculate the initial residual r using the following formula 0 , choose the initial solution x 0 is the zero vector and sets k=0,

[0076] r 0 = b-Ax 0 .

[0077] Step 3: Set up beta 0 =||r 0 || and unit vector Used in subsequent iterations.

[0078] Step 4: Generate a Krylov subspace by iteration. The definition of the Krylov subspace is as follows:

[0079] K k (A,r 0 )=span{r 0 ,Ar 0 ,…,A k-1 r 0};

[0080] For each iteration k, the matrix-vector multiplication v is calculated k =Ap k , p k Represents the direction vector; calculates the new iteration step factor α k And update the orthogonal vector v using the following formula k :

[0081] v k :α k =p k T v k , v k =v k -α k p k .

[0082] Step 5: Update the residual r using the following formula k :

[0083] r k =r k-1 -α k v k-1 .

[0084] Step 6: Calculate the new residual norm using the following formula:

[0085] β k+1 =||r k ||.

[0086] Step 7: Determine β k+1 Is it less than e? If so, stop the iteration; if not, calculate the new direction vector And calculate the new orthogonal vector v k+1 :v k+1 =Ap k+1 .

[0087] Step 8: Update the solution using the following formula and output the final solution vector, which contains Zernike term coefficients of different orders, as well as the corresponding residuals and number of iterations:

[0088]

[0089] The Lanczos algorithm generates a bidiagonal matrix through a series of orthogonalization steps. In the LSQR algorithm, although the matrix commonly seen in the Lanczos process is not directly constructed, it indirectly uses the Lanczos idea to generate a bidiagonal matrix related to A. Each time the product of A is calculated and the direction vector is updated, it is actually iterating through the idea of ​​the Lanczos method.

[0090] Step S104: assigning different weight coefficients according to the influence of the Zernike terms of different orders on the imaging quality.

[0091] According to the fitting results, the Zernike polynomials are classified by order. Zernike terms of different orders have different effects on the imaging quality. Generally, low-order (such as 0-4 orders) Zernike terms have a greater impact on the overall imaging quality of the optical system, while high-order (such as 5th order and above) Zernike terms are mainly sensitive to the high-frequency errors of the system and have relatively little impact. Therefore, it is necessary to assign different weight coefficients to Zernike terms of different orders to determine their contribution to the overall error evaluation.

[0092] The imaging quality indicators here include but are not limited to MTF (Modulation Transfer Function), PSF (Point Spread Function), wavefront error (Wavefront Error) and nodal aberration.

[0093] Preferably, the determination of the weight coefficient in this step can be achieved specifically in the following manner:

[0094] In the optical design software, the changes of the Zernike term coefficients of different orders are input to observe their influence on the imaging quality indicators (such as MTF and wavefront error RMS value), and the weight coefficient of each order is determined according to the magnitude of the influence using the following formula:

[0095]

[0096] In the above formula, W n is the weight coefficient of the nth order Zernike term, ΔQ n Indicates the influence of the nth-order Zernike term on the imaging quality, ∑ΔQ n is the total effect of all Zernike terms.

[0097] Step S105: Determine the key node positions of the optical system through simulation.

[0098] In this embodiment, simulation can be performed through optical design software, and the design parameters of the optical system and the lens error data are input into the optical design software, and the positions of key nodes are determined through simulation. Due to different design requirements, the positions of key nodes of different optical systems will also be different. For example, the key nodes of a telescope system usually include the center of the optical axis and the edge of the field of view, the key nodes of a microscope system may be concentrated at the center of the optical axis, and the key nodes of a camera lens include the center of the optical axis, the edge of the field of view, and the middle area. Therefore, during the simulation process, it is necessary to select appropriate key nodes for imaging quality evaluation and optimization based on specific system requirements and application scenarios to ensure that the optical system can meet specific performance requirements.

[0099] Step S106: Calculate the wavefront error or modulation transfer function at the key node position to evaluate the imaging quality at the key node position.

[0100] Similarly, this step can also be simulated by the above-mentioned optical design software. The wavefront error is calculated at each key node. The wavefront error is an important indicator for measuring the imaging quality of the optical system. It evaluates the phase change of the light wave after passing through the optical system. At the same time, the modulation transfer function (MTF) is calculated at each key node. MTF reflects the system's ability to transfer contrast at different spatial frequencies and is an important measure of imaging quality. Therefore, it can be used to evaluate the spatial frequency response capability of the system.

[0101] In this way, the imaging performance of the optical system at key nodes can be accurately evaluated, providing a basis for subsequent error correction and system optimization.

[0102] Step S107: Determine the PV tolerance range and the RMS tolerance range of the optical lens based on the weight coefficient of the Zernike term and the imaging quality evaluation result at the key node position.

[0103] In this embodiment, this step includes two aspects:

[0104] On the one hand, the PV tolerance range and RMS tolerance range of the optical lens are adjusted based on the weight coefficient of the Zernike term. For example, for a certain imaging system, if the weight of the low-order Zernike term is high, it may be necessary to reduce the tolerance of the PV value to ensure that the low-frequency error is within an acceptable range.

[0105] On the other hand, the PV tolerance range and RMS tolerance range of the optical lens are adjusted according to the imaging quality evaluation results at the key node positions, which may specifically include the following steps:

[0106] A1. Input different lens error data through simulation software and calculate the wavefront error or MTF value of each key node.

[0107] A2. If the wavefront error of a certain node exceeds the design tolerance, the lens surface shape needs to be reprocessed, or the PV and RMS values ​​of the lens need to be adjusted to ensure that the nodal aberration meets the requirements.

[0108] A3. After the optimization is completed, the entire optical system is simulated again to ensure that the imaging quality at all nodes meets the design requirements.

[0109] Preferably, Figure 2 As shown, after step S107, the following steps may also be included:

[0110] Step S108: Use simulation software to input different PV and RMS values ​​in the PV tolerance range and the RMS tolerance range, observe changes in the overall imaging quality of the system, and gradually optimize the PV tolerance range and the RMS tolerance range based on the simulation results and combined with the feasibility assessment of the optical lens manufacturing.

[0111] For example, different PV and RMS values ​​are input during simulation to observe the changes in the overall imaging quality of the system (including the wavefront error and MTF value at each key node). According to the simulation results, the tolerance range of the PV and RMS values ​​is gradually optimized. When determining the tolerance range, the actual feasibility of the manufacturing process also needs to be considered. Usually, the PV and RMS tolerances of optical lenses need to be within a reasonable manufacturing accuracy range. Through communication with the manufacturing department, it is ensured that the designed tolerance range can meet the system imaging quality requirements and can be achieved by the existing processing technology.

[0112] Combining simulation results with manufacturing process capabilities, the PV and RMS tolerance ranges of the lens are finally determined. The tolerances at this point can both ensure the imaging quality of the optical system at key nodes and be achieved through actual processing.

[0113] From the above, it can be seen that the method for determining the surface error tolerance of an optical lens provided by the present invention can optimize the PV and RMS tolerance ranges by assigning weights to Zernike coefficients of different orders and combining the evaluation of key node aberrations, thereby improving the overall imaging quality of the optical system.

[0114] like Figure 3 The figure is a schematic flow chart of a method for manufacturing an optical system provided in an embodiment of the present application, the method comprising the following steps:

[0115] Step S301: Determine the PV tolerance range and RMS tolerance range of the optical lens.

[0116] This step utilizes the method for determining the surface error tolerance of the optical lens as described above to determine the PV tolerance range and RMS tolerance range of the optical lens, which will not be further elaborated here.

[0117] Step S302: Processing the optical lens according to the determined PV tolerance range and RMS tolerance range.

[0118] Step S303: performing surface error detection on the processed optical lens to confirm whether it meets the tolerance requirements.

[0119] Step S304: Evaluate the aberration of key nodes to ensure that the set imaging quality requirements are met.

[0120] Step S305: Apply the optical lens that meets the tolerance requirements and the nodal aberration requirements to the optical system and verify the imaging quality.

[0121] The above optical system manufacturing method ensures the imaging quality of the final product through strict tolerance control and multiple verification steps, and is particularly suitable for optical systems that require high-precision imaging.

[0122] like Figure 4 The structure diagram of a device for determining the surface error tolerance of an optical lens provided in an embodiment of the present application is shown, and the device includes: a preliminary range determination unit 410, a point cloud data generation unit 420, a data fitting unit 430, a weight allocation unit 440, a key node determination unit 450, a key node evaluation unit 460 and a tolerance range determination unit 470, which are sequentially connected. Among them:

[0123] The preliminary range determination unit 410 is used to determine the preliminary PV tolerance and RMS tolerance range of the optical lens by using experimental experience.

[0124] The point cloud data generating unit 420 is used to generate surface error point cloud data within a corresponding range based on the preliminary PV tolerance and RMS tolerance range.

[0125] The data fitting unit 430 is used to fit the surface error point cloud data using Zernike polynomials to decompose the surface error into Zernike terms of different orders.

[0126] The weight allocation unit 440 is used to allocate different weight coefficients according to the influence of the Zernike terms of different orders on the imaging quality.

[0127] The key node determination unit 450 is used to determine the key node positions of the optical system through simulation.

[0128] The key node evaluation unit 460 is used to calculate the wavefront error or modulation transfer function at the key node position to evaluate the imaging quality at the key node position.

[0129] The tolerance range determination unit 470 is used to determine the PV tolerance range and the RMS tolerance range of the optical lens based on the weight coefficient of the Zernike term and the imaging quality evaluation result at the key node position.

[0130] As an embodiment of the present invention, the data fitting unit 430 is specifically configured to perform the following steps:

[0131] Step 1: Input the linear system Ax=b and tolerance e, where A is an m×n matrix consisting of surface error point cloud data and b is the observation vector;

[0132] Step 2: Calculate the initial residual r using the following formula 0 , choose the initial solution x 0 is the zero vector and sets k=0,

[0133] r 0 = b-Ax 0 ;

[0134] Step 3: Set up beta 0 =||r 0 || and unit vector Used in subsequent iterations;

[0135] Step 4: Generate a Krylov subspace by iteration. The definition of the Krylov subspace is as follows:

[0136] K k (A,r 0 )=span{r 0 ,Ar 0 ,…,A k-1 r 0};

[0137] For each iteration k, the matrix-vector multiplication v is calculated k =Ap k , p k Represents the direction vector; calculates the new iteration step factor α k And update the orthogonal vector v using the following formula k :

[0138] v k :α k =p k T v k , v k =v k -α k p k ;

[0139] Step 5: Update the residual r using the following formula k :

[0140] r k =r k-1 -α k v k-1 ;

[0141] Step 6: Calculate the new residual norm using the following formula:

[0142] β k+1 =||r k ||;

[0143] Step 7: Determine β k+1 Is it less than e? If so, stop the iteration; if not, calculate the new direction vector And calculate the new orthogonal vector v k+1 :v k+1 =Ap k+1 ;

[0144] Step 8: Update the solution using the following formula and output the final solution vector, which contains Zernike term coefficients of different orders, as well as the corresponding residuals and number of iterations:

[0145]

[0146] As an embodiment of the present invention, the weight allocation unit 440 is specifically used to: input the changes of the Zernike term coefficients of different orders in the optical design software, observe their influence on the imaging quality index, and determine the weight coefficient of each order according to the magnitude of the influence using the following formula:

[0147]

[0148] In the above formula, W n is the weight coefficient of the nth order Zernike term; ΔQ n Indicates the influence of the nth-order Zernike term on the imaging quality, ∑ΔQ n is the total effect of all Zernike terms.

[0149] As an embodiment of the present invention, the above-mentioned key node positions include: the center of the optical axis and / or the edge of the field of view.

[0150] As an embodiment of the present invention, the above-mentioned device also includes: a tolerance range optimization unit, which is used to input different PV and RMS values ​​in the PV tolerance range and the RMS tolerance range using simulation software, observe the changes in the overall imaging quality of the system, and gradually optimize the PV tolerance range and the RMS tolerance range based on the simulation results and combined with the feasibility evaluation of the optical lens manufacturing.

[0151] As an embodiment of the present invention, the surface error point cloud data is generated by using a random error distribution model or an actual error model based on measurement data.

[0152] The detailed description of each of the above units can be found in the corresponding description in the aforementioned method embodiment, which will not be further elaborated here.

[0153] It can be seen from the above technical solution that the device for determining the surface error tolerance of an optical lens provided by the present invention can optimize the PV and RMS tolerance ranges by assigning weights to Zernike coefficients of different orders and combining the evaluation of key node aberrations, thereby improving the overall imaging quality of the optical system.

[0154] An embodiment of the present invention further provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the above method is implemented when the processor executes the program.

[0155] An embodiment of the present invention further provides a computer-readable storage medium, which stores a computer program for executing the above method.

[0156] like Figure 5 As shown, the electronic device 600 may further include: a communication module 110, an input unit 120, an audio processor 130, a display 160, and a power supply 170. It is worth noting that the electronic device 600 does not necessarily have to include Figure 5 In addition, the electronic device 600 may also include Figure 5 For components not shown, reference may be made to the prior art.

[0157] like Figure 5 As shown, the central processor 100 is sometimes also referred to as a controller or an operation control, and may include a microprocessor or other processor devices and / or logic devices. The central processor 100 receives inputs and controls the operations of various components of the electronic device 600.

[0158] The memory 140 may be, for example, one or more of a cache, a flash memory, a hard drive, a removable medium, a volatile memory, a non-volatile memory or other suitable devices. The above-mentioned information related to the failure may be stored, and a program for executing the relevant information may also be stored. The CPU 100 may execute the program stored in the memory 140 to implement information storage or processing.

[0159] The input unit 120 provides input to the CPU 100. The input unit 120 is, for example, a key or a touch input device. The power supply 170 is used to provide power to the electronic device 600. The display 160 is used to display display objects such as images and text. The display may be, for example, an LCD display, but is not limited thereto.

[0160] The memory 140 may be a solid-state memory, such as a read-only memory (ROM), a random access memory (RAM), a SIM card, etc. It may also be a memory that saves information even when the power is off, can be selectively erased, and is provided with more data, examples of which are sometimes referred to as EPROMs, etc. The memory 140 may also be some other type of device. The memory 140 includes a buffer memory 141 (sometimes referred to as a buffer). The memory 140 may include an application / function storage unit 142, which is used to store application programs and function programs or processes for executing the operation of the electronic device 600 through the central processor 100.

[0161] The memory 140 may also include a data storage unit 143 for storing data, such as contacts, digital data, pictures, sounds, and / or any other data used by the electronic device. The driver storage unit 144 of the memory 140 may include various drivers for communication functions of the electronic device and / or for executing other functions of the electronic device (such as messaging applications, address book applications, etc.).

[0162] The communication module 110 is a transmitter / receiver that transmits and receives signals via the antenna 111. The communication module 110 (transmitter / receiver) is coupled to the central processor 100 to provide input signals and receive output signals, which may be the same as the case of a conventional mobile communication terminal.

[0163] Based on different communication technologies, multiple communication modules 110 may be provided in the same electronic device, such as a cellular network module, a Bluetooth module and / or a wireless LAN module. The communication module 110 (transmitter / receiver) is also coupled to a speaker 131 and a microphone 132 via an audio processor 130 to provide an audio output via the speaker 131 and receive an audio input from the microphone 132, thereby realizing a common telecommunication function. The audio processor 130 may include any suitable buffer, decoder, amplifier, etc. In addition, the audio processor 130 is also coupled to the central processor 100, so that recording can be performed on the local machine through the microphone 132, and the sound stored on the local machine can be played through the speaker 131.

[0164] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0165] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0166] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0167] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0168] The present invention uses specific embodiments to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only used to help understand the method of the present invention and its core idea. At the same time, for those skilled in the art, according to the idea of ​​the present invention, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as a limitation on the present invention.

Claims

1. A method for determining the surface error tolerance of an optical lens, characterized in that: The method comprises: Use test experience to determine the preliminary PV tolerance and RMS tolerance range of optical lenses; Generate surface error point cloud data within a corresponding range based on the preliminary PV tolerance and RMS tolerance range; Using Zernike polynomials to fit the surface error point cloud data, and decomposing the surface error into Zernike terms of different orders; Allocating different weight coefficients according to the influence of the Zernike terms of different orders on the imaging quality; Determine the key node positions of the optical system through simulation; Calculating the wavefront error or modulation transfer function at the key node position to evaluate the imaging quality at the key node position; The PV tolerance range and the RMS tolerance range of the optical lens are determined based on the weight coefficient of the Zernike term and the imaging quality evaluation result at the key node position.

2. The method for determining the surface error tolerance of an optical lens according to claim 1, characterized in that: The method of fitting the surface error point cloud data using Zernike polynomials to decompose the surface error into Zernike terms of different orders includes: Step 1: Input the linear system Ax=b and tolerance e, where A is an m×n matrix consisting of surface error point cloud data and b is the observation vector; Step 2: Calculate the initial residual r0 using the following formula, select the initial solution x0 as the zero vector and set k = 0. r0=b-Ax0; Step 3: Set β0 = ||r0|| and unit vector Used in subsequent iterations; Step 4: Generate a Krylov subspace by iteration. The definition of the Krylov subspace is as follows: K k (A,r0)=span{r0,Ar0,…,A k-1 r0}; For each iteration k, the matrix-vector multiplication v is calculated k =Ap k , p k Represents the direction vector; calculates the new iteration step factor α k And update the orthogonal vector v using the following formula k : v k :α k =p k T v k ,v k =v k -α k p k ; Step 5: Update the residual r using the following formula k : r k ＝r k-1 -α k v k-1 ； Step 6: Calculate the new residual norm using the following formula: β k+1 ＝||r k ||； Step 7: Determine β k+1 Is it less than e? If so, stop the iteration; if not, calculate the new direction vector p k : And calculate the new orthogonal vector v k+1 :v k+1 =Ap k+1 ; Step 8: Update the solution using the following formula and output the final solution vector, which contains Zernike term coefficients of different orders, as well as the corresponding residuals and number of iterations:

3. The method for determining the surface error tolerance of an optical lens according to claim 1, characterized in that: The allocating different weight coefficients according to the influence of the Zernike terms of different orders on the imaging quality comprises: In the optical design software, the changes of the Zernike term coefficients of different orders are input to observe their influence on the imaging quality index, and the weight coefficient of each order is determined according to the magnitude of the influence using the following formula: In the above formula, W n is the weight coefficient of the nth order Zernike term; ΔQ n Indicates the influence of the nth-order Zernike term on the imaging quality, ∑ΔQ n is the total effect of all Zernike terms.

4. The method for determining the surface error tolerance of an optical lens according to claim 1, characterized in that: The key node positions include: the center of the optical axis and / or the edge of the field of view.

5. The method for determining the surface error tolerance of an optical lens according to claim 1, characterized in that: After determining the PV tolerance range and the RMS tolerance range of the optical lens based on the weight coefficient of the Zernike term and the imaging quality evaluation result at the key node position, the method further includes: Different PV and RMS values ​​in the PV tolerance range and the RMS tolerance range are inputted by simulation software, and changes in the overall imaging quality of the system are observed. According to the simulation results and in combination with the feasibility evaluation of the optical lens manufacturing, the PV tolerance range and the RMS tolerance range are gradually optimized.

6. The method for determining the surface error tolerance of an optical lens according to claim 1, characterized in that: The surface error point cloud data is generated by adopting a random error distribution model or an actual error model based on measurement data.

7. A device for determining the surface error tolerance of an optical lens, characterized in that: The device comprises: A preliminary range determination unit, used to determine the preliminary PV tolerance and RMS tolerance range of the optical lens using test experience; A point cloud data generating unit, configured to generate surface error point cloud data within a corresponding range based on the preliminary PV tolerance and RMS tolerance range; A data fitting unit, used for fitting the surface error point cloud data using Zernike polynomials, and decomposing the surface error into Zernike terms of different orders; A weight allocation unit, used for allocating different weight coefficients according to the influence of the Zernike terms of different orders on the imaging quality; A key node determination unit, used for determining the key node positions of the optical system through simulation; A key node evaluation unit, used to calculate the wavefront error or modulation transfer function at the key node position to evaluate the imaging quality at the key node position; A tolerance range determination unit is used to determine the PV tolerance range and the RMS tolerance range of the optical lens based on the weight coefficient of the Zernike term and the imaging quality evaluation result at the key node position.

8. The device for determining the surface error tolerance of an optical lens according to claim 7, characterized in that: The data fitting unit is specifically used to perform the following steps: Step 1: Input the linear system Ax=b and tolerance e, where A is an m×n matrix consisting of surface error point cloud data and b is the observation vector; Step 2: Calculate the initial residual r0 using the following formula, select the initial solution x0 as the zero vector and set k = 0. r0=b-Ax0; Step 3: Set β0 = ||r0|| and unit vector Used in subsequent iterations; Step 4: Generate a Krylov subspace by iteration. The definition of the Krylov subspace is as follows: K k (A,r0)=span{r0,Ar0,…,A k-1 r0}; For each iteration k, the matrix-vector multiplication v is calculated k =Ap k , p k Represents the direction vector; calculates the new iteration step factor α k And update the orthogonal vector v using the following formula k : v k :α k =p k T v k ,v k =v k -α k p k ; Step 5: Update the residual r using the following formula k : r k ＝r k-1 -α k v k-1 ； Step 6: Calculate the new residual norm using the following formula: β k+1 ＝||r k ||； Step 7: Determine β k+1 Is it less than e? If so, stop the iteration; if not, calculate the new direction vector p k : And calculate the new orthogonal vector v k+1 :v k+1 =Ap k+1 ; Step 8: Update the solution using the following formula and output the final solution vector, which contains Zernike term coefficients of different orders, as well as the corresponding residuals and number of iterations:

9. The device for determining the surface error tolerance of an optical lens according to claim 7, characterized in that: The weight allocation unit is specifically used for: In the optical design software, the changes of the Zernike term coefficients of different orders are input to observe their influence on the imaging quality index, and the weight coefficient of each order is determined according to the magnitude of the influence using the following formula: In the above formula, W n is the weight coefficient of the nth order Zernike term; ΔQ n Indicates the influence of the nth-order Zernike term on the imaging quality, ∑ΔQ n is the total effect of all Zernike terms.

10. The device for determining the surface error tolerance of an optical lens according to claim 7, characterized in that: The key node positions include: the center of the optical axis and / or the edge of the field of view.

11. The device for determining the surface error tolerance of an optical lens according to claim 7, characterized in that: The device also includes: a tolerance range optimization unit, which is used to input different PV and RMS values ​​in the PV tolerance range and the RMS tolerance range using simulation software, observe changes in the overall imaging quality of the system, and gradually optimize the PV tolerance range and the RMS tolerance range based on the simulation results and combined with the feasibility assessment of the optical lens manufacturing.

12. The device for determining the surface error tolerance of an optical lens according to claim 7, characterized in that: The surface error point cloud data is generated by adopting a random error distribution model or an actual error model based on measurement data.

13. A method for manufacturing an optical system, characterized in that: The method comprises: Determine the PV tolerance range and RMS tolerance range of the optical lens according to the method for determining the surface error tolerance of the optical lens according to any one of claims 1 to 6; Processing the optical lens according to the determined PV tolerance range and RMS tolerance range; Performing surface error detection on the processed optical lens to confirm whether it meets the tolerance requirements; Evaluate the aberration of key nodes to ensure that the set imaging quality requirements are met; Apply optical lenses that meet tolerance requirements and nodal aberration requirements to the optical system and verify the imaging quality.

14. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 6 are implemented.

15. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

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