Method and device for determining a tolerance on a surface error of an optical lens, and corresponding method for manufacturing an optical system

Abstract

This invention provides a method, apparatus, and manufacturing method for determining the surface shape error tolerance of an optical lens and a corresponding optical system. The method includes: determining the preliminary PV and RMS tolerance ranges of the optical lens using experimental experience; generating surface error point cloud data within the corresponding 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 to the Zernike terms of different orders; determining the key node positions of the optical system through simulation; evaluating the imaging quality at the key node positions; and determining the PV and RMS tolerance ranges of the optical lens based on the weight coefficients of the Zernike terms and the imaging quality evaluation results of the key nodes. This invention can optimize the PV and RMS tolerance ranges by assigning weights to Zernike coefficients of different orders and combining them with the evaluation of key node aberrations, thereby improving the overall imaging quality of the optical system.

Description

Technical Field

[0001] This invention relates to the field of optical lens manufacturing and testing technology, and in particular to a method, apparatus, and manufacturing method of a corresponding optical system for determining the surface shape error tolerance of an optical lens. Background Technology

[0002] The manufacturing precision of optical lenses largely determines the imaging quality of an optical system. Generally, the quality of the imaging system can be effectively improved by controlling the surface shape error of the lenses.

[0003] The Peak-to-Valley (PV) and Root Mean Square (RMS) values ​​of optical lenses are commonly used as standards for measuring lens surface shape errors. The PV value represents the height difference between the highest and lowest points on the lens surface, while the RMS value is the root mean square value of the lens surface error. PV and RMS tolerances refer to the permissible range of PV and RMS value errors, respectively.

[0004] Optimizing the tolerance range of PV and RMS errors can improve the overall imaging quality of optical systems, but current technologies still have room for improvement in optimizing PV and RMS error tolerances. Summary of the Invention

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

[0006] To achieve the above objectives, the present invention adopts the following solution:

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

[0008] As an embodiment of the present invention, the method described above uses Zernike polynomials to fit the surface error point cloud data, decomposing the surface error into Zernike terms of different orders, including:

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

[0010] Step 2: Calculate the initial residual r0 using the following formula, select the initial solution x0 as the zero vector and set k = 0.

[0011] r0 = b - Ax0;

[0012] Step 3: Set β0 = ||r0|| and the unit vector Used for subsequent iterations;

[0013] Step 4: Generate the Krylov subspace through iteration. The Krylov subspace is defined as follows:

[0014] K k (A,r0)=span{r0,Ar0,…,A k-1 r0};

[0015] For each iteration k, the matrix-vector multiplication v is calculated. k =Ap k p k Represent the direction vector; calculate the new iteration step size 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 v k-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 If the value is less than e, stop the iteration; otherwise, 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 the coefficients of Zernike terms of different orders, as well as the corresponding residuals and iteration numbers:

[0023]

[0024] As an embodiment of the present invention, the method described above, in which different weighting coefficients are assigned according to the influence of the Zernike terms of different orders on the imaging quality, includes:

[0025] In optical design software, input the changes in the Zernike coefficients of different orders, observe their impact on image quality indicators, and determine the weighting coefficient for each order based on the magnitude of the impact using the following formula:

[0026]

[0027] In the above formula, W n ΔQ is the weighting coefficient of the nth Zernike term; n ∑ΔQ represents the degree of influence of the nth-order Zernike term on image quality. n The total impact of all Zernike items.

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

[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, the method further 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 based on the simulation results and in conjunction with the feasibility assessment of the manufacturing of the optical lens.

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

[0031] According to a second aspect of the present invention, an apparatus for determining the surface shape error tolerance of an optical lens is provided. The apparatus includes: a preliminary range determination unit for determining the preliminary PV tolerance and RMS tolerance range of the optical lens using experimental experience; a point cloud data generation unit for generating surface error point cloud data within the corresponding range based on the preliminary PV tolerance and RMS tolerance range; a data fitting unit for fitting the surface error point cloud data using a Zernike polynomial to decompose the surface error into Zernike terms of different orders; a weight allocation unit 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 for determining the key node positions of the optical system through simulation; a key node evaluation unit for evaluating the imaging quality at the key node positions by calculating the wavefront error or modulation transfer function at the key node positions; and 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 above-mentioned data fitting unit is specifically used to perform the following steps:

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

[0034] Step 2: Calculate the initial residual r0 using the following formula, select the initial solution x0 as the zero vector and set k = 0.

[0035] r0 = b - Ax0;

[0036] Step 3: Set β0 = ||r0|| and the unit vector Used for subsequent iterations;

[0037] Step 4: Generate the Krylov subspace through iteration. The Krylov subspace is defined as follows:

[0038] K k (A,r0)=span{r0,Ar0,…,A k-1 r0};

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

[0040] v k αk =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 If the value is less than e, stop the iteration; otherwise, 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 the coefficients of Zernike terms of different orders, as well as the corresponding residuals and iteration numbers:

[0047]

[0048] As an embodiment of the present invention, the aforementioned weight allocation unit is specifically used for: inputting the changes in the Zernike coefficients of different orders in optical design software, observing their impact on imaging quality indicators, and determining the weight coefficient of each order according to the magnitude of the impact using the following formula:

[0049]

[0050] In the above formula, W n ΔQ is the weighting coefficient of the nth Zernike term; n ∑ΔQ represents the degree of influence of the nth-order Zernike term on image quality. n This represents the total effect of all Zernike terms.

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

[0052] As an embodiment of the present invention, the above-mentioned device further includes: a tolerance range optimization unit, used to input different PV and RMS values ​​in the PV tolerance range and 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 RMS tolerance range based on the simulation results and in conjunction with the feasibility assessment of the manufacturing of the optical lens.

[0053] As an embodiment of the present invention, the above-mentioned surface error point cloud data is generated 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, an optical system manufacturing method is provided, the method comprising: determining the PV tolerance range and RMS tolerance range of an optical lens according to the method for determining the surface shape error tolerance of an optical lens as described above; machining the optical lens according to the determined PV tolerance range and RMS tolerance range; performing surface shape error detection on the machined optical lens to confirm whether it meets the tolerance requirements; evaluating the aberrations of key nodes to ensure that the set imaging quality requirements are met; and applying the optical lens that meets the tolerance requirements and node aberration requirements to the optical system and verifying the imaging quality.

[0055] According to a fourth aspect of the present invention, an electronic device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the above-described method.

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

[0057] As can be seen from the above technical solutions, the method, device and manufacturing method of the corresponding optical system for determining the surface shape error tolerance of optical lenses 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. Attached Figure Description

[0058] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort. In the drawings:

[0059] Figure 1This is a flowchart illustrating a method for determining the surface shape error tolerance of an optical lens according to an embodiment of this application;

[0060] Figure 2 This is a flowchart illustrating a method for determining the surface shape error tolerance of an optical lens according to another embodiment of this application;

[0061] Figure 3 This is a schematic flowchart of an optical system manufacturing method provided in an embodiment of this application;

[0062] Figure 4 This is a schematic diagram of the structure of a device for determining the surface shape error tolerance of an optical lens provided in an embodiment of this application;

[0063] Figure 5 This is a schematic block diagram of the system configuration of the electronic device provided in the embodiments of this application. Detailed Implementation

[0064] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the embodiments of the present invention will be further described in detail below with reference to the accompanying drawings. Here, the illustrative 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 diagram shown is a flowchart illustrating a method for determining the surface shape error tolerance of an optical lens according to an embodiment of this application. The method includes 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 mentioned here refers to past manufacturing and testing experience. Although it is specified as experimental experience, it does not mean that this step is performed manually. Past manufacturing and testing experience exists in the system in the form of data, such as historical manufacturing records, test results, and process parameters. In this embodiment, the system can automatically retrieve this data and determine the initial PV tolerance and RMS tolerance range through data analysis, which will serve as the basis for subsequent optimization.

[0068] Step S102: Generate surface error point cloud data within the 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 typically used to describe a dataset consisting of a large number of discrete points that represent the surface or shape of an object in three-dimensional space. In this step, "point cloud data" refers to the numerical representation of the surface shape error of an optical lens. This data can be generated using a random error distribution model or an actual error model based on measurement data.

[0070] The point cloud data here needs to be within the previously determined PV and RMS tolerances. This is because the PV tolerance defines the maximum permissible range of surface error, while the RMS tolerance reflects the statistical characteristics of the overall error distribution. Point cloud data exceeding these ranges has no analytical value. Therefore, limiting the data to the PV and RMS tolerances avoids the generation and analysis of invalid data, ensuring the efficient use of computational resources. Furthermore, only data within a reasonable range can yield reliable analytical results, ensuring that the final determined tolerance range has practical guiding significance. Therefore, the point cloud data must be within the initially determined PV and RMS tolerances to ensure the effectiveness and practicality of the entire analysis process.

[0071] Step S103: Fit 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 optics. They can effectively describe errors on spherical, aspherical, and freeform surfaces, with each term representing a specific type of aberration (such as spherical aberration, coma, astigmatism, etc.). By fitting surface error point cloud data, surface errors can be decomposed into Zernike terms of different orders.

[0073] Preferably, this step can 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 the tolerance e, where A is an m×n matrix composed of surface error point cloud data and b is the observation vector.

[0075] Step 2: Calculate the initial residual r0 using the following formula, select the initial solution x0 as the zero vector and set k = 0.

[0076] r0 = b - Ax0.

[0077] Step 3: Set β0 = ||r0|| and the unit vector Used for subsequent iterations.

[0078] Step 4: Generate the Krylov subspace through iteration. The Krylov subspace is defined as follows:

[0079] K k (A,r0)=span{r0,Ar0,…,A k-1 r0};

[0080] For each iteration k, the matrix-vector multiplication v is calculated. k =Ap k p k Represent the direction vector; calculate the new iteration step size 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 If the value is less than e, stop the iteration; otherwise, 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 the coefficients of Zernike terms of different orders, as well as the corresponding residuals and iteration numbers:

[0088]

[0089] The Lanczos algorithm generates a bidiagonal matrix through a series of orthogonalization steps. While the LSQR algorithm does not directly construct the matrices commonly found in the Lanczos process, it indirectly utilizes the Lanczos idea to generate a bidiagonal matrix associated with A. Each iteration, calculating the product of A and updating the direction vector, is essentially iterating using the Lanczos method.

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

[0091] Based on the fitting results, the Zernike polynomials are classified according to their order. Different orders of Zernike terms have different impacts on image quality. Generally, low-order (e.g., 0-4) Zernike terms have a greater impact on the overall image quality of the optical system, while high-order (e.g., 5th and above) Zernike terms are mainly sensitive to high-frequency errors of the system and have a relatively smaller impact. Therefore, different weighting coefficients need to be assigned to Zernike terms of different orders to determine their contribution to the overall error assessment.

[0092] The imaging quality metrics here include, but are not limited to, MTF (modulation transfer function), PSF (point spread function), wavefront error, and nodal aberration.

[0093] Preferably, the determination of the weighting coefficients in this step can be achieved in the following way:

[0094] In optical design software, input the changes in the Zernike coefficients of different orders and observe their impact on imaging quality indicators (such as MTF and wavefront error RMS). Based on the magnitude of the impact, determine the weighting coefficient for each order using the following formula:

[0095]

[0096] In the above formula, W n Let ΔQ be the weighting coefficient of the nth Zernike term. n ∑ΔQ represents the degree of influence of the nth-order Zernike term on image quality. n This represents the total effect of all Zernike terms.

[0097] Step S105: Determine the location of key nodes in the optical system through simulation.

[0098] In this embodiment, simulation can be performed using optical design software. The design parameters of the optical system and lens error data are input into the software, and the locations of critical nodes are determined through simulation. Different optical systems have different design requirements, resulting in different locations for their critical nodes. For example, critical nodes in a telescope system typically include the optical axis center and the edge of the field of view; critical nodes in a microscope system may be concentrated at the optical axis center; and critical nodes in a camera lens include the optical axis center, the edge of the field of view, and the central region. Therefore, during simulation, it is necessary to select appropriate critical nodes for image quality evaluation and optimization based on specific system requirements and application scenarios to ensure that the optical system meets specific performance requirements.

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

[0100] Similarly, this step can also be simulated using the aforementioned optical design software. Wavefront error is calculated at each critical node. Wavefront error is an important indicator of the imaging quality of an optical system, evaluating the phase change of the light wave after passing through the optical system. Simultaneously, modulation transfer function (MTF) is calculated at each critical node. MTF reflects the system's ability to transfer contrast at different spatial frequencies and is an important metric for imaging quality; therefore, it can be used to evaluate the system's spatial frequency response.

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

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

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

[0104] On the one hand, the PV tolerance range and RMS tolerance range of optical lenses are adjusted based on the weighting 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 lenses are adjusted based on the imaging quality evaluation results at key node locations. This can specifically include the following steps:

[0106] A1. Using simulation software, input different lens error data 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 surface shape of the lens needs to be reprocessed, or the PV and RMS values ​​of the lens need to be adjusted to ensure that the nodal aberrations meet the requirements.

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

[0109] Preferred, such as Figure 2 As shown, after step S107, the following may also be included:

[0110] Step S108: Input different PV and RMS values ​​in the PV tolerance range and 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 RMS tolerance range based on the simulation results and the feasibility assessment of the optical lens manufacturing.

[0111] For example, different PV and RMS values ​​can be input during simulation to observe the changes in the overall imaging quality of the system (including wavefront error and MTF value at each key node). Based on the simulation results, the tolerance range of PV and RMS values ​​can be gradually optimized. When determining the tolerance range, the actual feasibility of the manufacturing process also needs to be considered. Typically, the PV and RMS tolerances of optical lenses need to be within a reasonable manufacturing precision range. Through communication with the manufacturing department, it can be ensured that the designed tolerance range meets the system's imaging quality requirements while also being achievable with existing processing techniques.

[0112] By combining simulation results and manufacturing capabilities, the PV and RMS tolerance ranges of the lens were finally determined. These tolerances ensure imaging quality at critical points in the optical system while also being achievable through actual manufacturing.

[0113] As can be seen from the above, the method for determining the surface shape error tolerance of optical lenses 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.

[0114] like Figure 3 The diagram shown is a flowchart illustrating a method for manufacturing an optical system according to an embodiment of this application. The method includes the following steps:

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

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

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

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

[0119] Step S304: Evaluate the aberrations 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 nodal aberration requirements to the optical system and verify the imaging quality.

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

[0122] like Figure 4 The diagram shown is a structural schematic of an optical lens surface shape error tolerance determination device provided in an embodiment of this application. 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 connected sequentially.

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

[0124] The point cloud data generation unit 420 is used to generate surface error point cloud data within the 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, decomposing the surface error into Zernike terms of different orders.

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

[0127] The critical node determination unit 450 is used to determine the location of critical nodes in 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 location to evaluate the imaging quality at the key node location.

[0129] The tolerance range determination unit 470 is used to determine the PV tolerance range and RMS tolerance range of the optical lens based on the weighting 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 used to perform the following steps:

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

[0132] Step 2: Calculate the initial residual r0 using the following formula, select the initial solution x0 as the zero vector and set k = 0.

[0133] r0 = b - Ax0;

[0134] Step 3: Set β0 = ||r0|| and the unit vector Used for subsequent iterations;

[0135] Step 4: Generate the Krylov subspace through iteration. The Krylov subspace is defined as follows:

[0136] K k (A,r0)=span{r0,Ar0,…,A k-1 r0};

[0137] For each iteration k, the matrix-vector multiplication v is calculated. k =Ap k p k Represent the direction vector; calculate the new iteration step size 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 If the value is less than e, stop the iteration; otherwise, 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 the coefficients of Zernike terms of different orders, as well as the corresponding residuals and iteration numbers:

[0145]

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

[0147]

[0148] In the above formula, W n ΔQ is the weighting coefficient of the nth Zernike term; n ∑ΔQ represents the degree of influence of the nth-order Zernike term on image quality. n This represents the total effect of all Zernike terms.

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

[0150] As an embodiment of the present invention, the above-mentioned device further includes: a tolerance range optimization unit, used to input different PV and RMS values ​​in the PV tolerance range and 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 RMS tolerance range based on the simulation results and in conjunction with the feasibility assessment of the manufacturing of the optical lens.

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

[0152] For a detailed description of each of the above units, please refer to the corresponding descriptions in the foregoing method embodiments, which will not be repeated here.

[0153] As can be seen from the above technical solution, the optical lens surface shape error tolerance determination device 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.

[0154] This invention also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the above-described method.

[0155] This invention also provides a computer-readable storage medium storing a computer program for performing the above-described methods.

[0156] like Figure 5 As shown, the electronic device 600 may also 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 need to include these components. Figure 5 All components shown; in addition, the electronic device 600 may also include Figure 5 For components not shown, please refer to existing technologies.

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

[0158] The memory 140 may be, for example, one or more of a cache, flash memory, hard drive, removable media, volatile memory, non-volatile memory, or other suitable devices. It may store the aforementioned failure-related information, and also store a program for executing that information. The central processing unit 100 may execute the program stored in the memory 140 to perform information storage or processing, etc.

[0159] Input unit 120 provides input to central processing unit 100. Input unit 120 may be, for example, a keypad or touch input device. Power supply 170 provides power to electronic device 600. Display 160 displays images and text. Display may be, for example, an LCD display, but is not limited thereto.

[0160] The memory 140 can be a solid-state memory, such as a read-only memory (ROM), random access memory (RAM), a SIM card, etc. It can also be a memory that retains information even when power is off, can be selectively erased, and contains more data; examples of this type of memory are sometimes referred to as EPROMs. The memory 140 can 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 for storing application programs and function programs or processes for executing the operation of the electronic device 600 via the central processing unit 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 the electronic device's communication functions and / or for performing 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 processing unit 100 to provide input signals and receive output signals, which can be the same as in a conventional mobile communication terminal.

[0163] Based on different communication technologies, multiple communication modules 110 can be configured in the same electronic device, such as cellular network modules, Bluetooth modules, and / or wireless LAN modules. The communication module 110 (transmitter / receiver) is also coupled to a speaker 131 and a microphone 132 via an audio processor 130 to provide audio output via the speaker 131 and receive audio input from the microphone 132, thereby enabling typical telecommunications functions. The audio processor 130 may include any suitable buffer, decoder, amplifier, etc. Additionally, the audio processor 130 is coupled to a central processing unit 100, enabling on-device recording via the microphone 132 and on-device playback of stored audio via the speaker 131.

[0164] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied 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] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0166] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0167] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0168] Specific embodiments have been used to illustrate the principles and implementation methods of this invention. The descriptions of the embodiments above are only for the purpose of helping to understand the method and core ideas of this invention. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of this invention. Therefore, the content of this specification should not be construed as a limitation of this invention.

Claims

1. A method for determining the surface shape error tolerance of an optical lens, characterized in that, The method includes: The preliminary PV and RMS tolerance ranges of optical lenses were determined using experimental experience. Based on the preliminary PV tolerance and RMS tolerance range, generate surface error point cloud data within the corresponding range; The surface error point cloud data is fitted using Zernike polynomials to decompose the surface error into Zernike terms of different orders. Different weighting coefficients are assigned based on the influence of Zernike terms of different orders on image quality; The locations of key nodes in the optical system are determined through simulation. The wavefront error or modulation transfer function at the key node location is calculated to evaluate the imaging quality at the key node location. The PV tolerance range and RMS tolerance range of the optical lens are determined based on the weighting coefficient of the Zernike term and the imaging quality evaluation results at the key node locations. The process 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 linear system And tolerance e, where A is a set of surface error point cloud data. The matrix is ​​denoted by b, where b is the observation vector. Step 2: Calculate the initial residuals using the following formula Choose an initial solution Set as zero vector and set , ； Step 3: Settings and unit vector Used for subsequent iterations; Step 4: Generate the Krylov subspace through iteration. The Krylov subspace is defined as follows: ； For each iteration k, the matrix-vector multiplication is calculated. , Represent the direction vector; calculate the new iteration step size factor. And update the orthogonal vector using the following formula : ： ， ； Step 5: Update the residuals using the following formula : ； Step 6: Calculate the new residual norm using the following formula: ； Step 7: Determine If yes, stop the iteration; otherwise, calculate the new direction vector. : And calculate new orthogonal vectors : ; Step 8: Update the solution using the following formula and output the final solution vector, which contains the coefficients of Zernike terms of different orders, as well as the corresponding residuals and iteration numbers: ； The allocation of different weighting coefficients based on the influence of Zernike terms of different orders on image quality includes: In optical design software, input the changes in the Zernike coefficients of different orders, observe their impact on image quality indicators, and determine the weighting coefficient for each order based on the magnitude of the impact using the following formula: In the above formula, The weight coefficients of the nth Zernike term; This indicates the degree of influence of the nth-order Zernike term on image quality. This represents the total effect of all Zernike terms.

2. The method for determining the surface shape error tolerance of an optical lens as described in claim 1, characterized in that, The key node locations include: the optical axis center and / or the edge of the field of view.

3. The method for determining the surface shape error tolerance of an optical lens as described in claim 1, characterized in that, After determining the PV tolerance range and RMS tolerance range of the optical lens based on the weighting coefficients of the Zernike term and the imaging quality evaluation results at the key node locations, the method further includes: By inputting different PV and RMS values ​​from the PV tolerance range and RMS tolerance range into simulation software, the changes in the overall imaging quality of the system are observed. Based on the simulation results and the feasibility assessment of the optical lens manufacturing, the PV tolerance range and RMS tolerance range are gradually optimized.

4. The method for determining the surface shape error tolerance of an optical lens as described in claim 1, characterized in that, The surface error point cloud data is generated using a random error distribution model or an actual error model based on measurement data.

5. A device for determining the surface shape error tolerance of an optical lens, characterized in that, The device includes: The preliminary range determination unit is used to determine the preliminary PV tolerance and RMS tolerance range of optical lenses based on experimental experience; A point cloud data generation unit is used to generate surface error point cloud data within the corresponding range based on the preliminary PV tolerance and RMS tolerance range; The data fitting unit is used to fit the surface error point cloud data using Zernike polynomials, decomposing the surface error into Zernike terms of different orders. The weighting allocation unit is used to allocate different weighting coefficients according to the influence of the Zernike terms of different orders on the imaging quality. The critical node determination unit is used to determine the location of critical nodes in the optical system through simulation. The critical node evaluation unit is used to calculate the wavefront error or modulation transfer function at the critical node location to evaluate the imaging quality at the critical node location. The tolerance range determination unit is used to determine the PV tolerance range and RMS tolerance range of the optical lens based on the weighting coefficient of the Zernike term and the imaging quality evaluation result at the key node position; The data fitting unit is specifically used to perform the following steps: Step 1: Input linear system And tolerance e, where A is a set of surface error point cloud data. The matrix is ​​denoted by b, where b is the observation vector. Step 2: Calculate the initial residuals using the following formula Choose an initial solution Set as zero vector and set , ； Step 3: Settings and unit vector Used for subsequent iterations; Step 4: Generate the Krylov subspace through iteration. The Krylov subspace is defined as follows: ； For each iteration k, the matrix-vector multiplication is calculated. , Represent the direction vector; calculate the new iteration step size factor. And update the orthogonal vector using the following formula : ： ， ； Step 5: Update the residuals using the following formula : ； Step 6: Calculate the new residual norm using the following formula: ； Step 7: Determine If yes, stop the iteration; otherwise, calculate the new direction vector. : And calculate new orthogonal vectors : ; Step 8: Update the solution using the following formula and output the final solution vector, which contains the coefficients of Zernike terms of different orders, as well as the corresponding residuals and iteration numbers: ； The weight allocation unit is specifically used for: In optical design software, input the changes in the Zernike coefficients of different orders, observe their impact on image quality indicators, and determine the weighting coefficient for each order based on the magnitude of the impact using the following formula: In the above formula, The weight coefficients of the nth Zernike term; This indicates the degree of influence of the nth-order Zernike term on image quality. This represents the total effect of all Zernike terms.

6. The apparatus for determining the surface shape error tolerance of an optical lens as described in claim 5, characterized in that, The key node locations include: the optical axis center and / or the edge of the field of view.

7. The apparatus for determining the surface shape error tolerance of an optical lens as described in claim 5, characterized in that, The device further includes a tolerance range optimization unit, which is used to input different PV and RMS values ​​in the PV tolerance range and 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 RMS tolerance range based on the simulation results and in conjunction with the feasibility assessment of the manufacturing of the optical lens.

8. The apparatus for determining the surface shape error tolerance of an optical lens as described in claim 5, characterized in that, The surface error point cloud data is generated using a random error distribution model or an actual error model based on measurement data.

9. A method for manufacturing an optical system, characterized in that, The method includes: The method for determining the surface shape error tolerance of an optical lens according to any one of claims 1-4 determines the PV tolerance range and RMS tolerance range of the optical lens; Optical lenses are manufactured according to the determined PV tolerance range and RMS tolerance range; The surface shape error of the processed optical lens is inspected to confirm whether it meets the tolerance requirements; Evaluate the aberrations of key nodes to ensure that the set imaging quality requirements are met; Optical lenses that meet tolerance and nodal aberration requirements are applied to optical systems and their imaging quality is verified.

10. 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, it implements the steps of the method according to any one of claims 1 to 4.

11. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 4.

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