Simulation analysis method and system of optical lens

Abstract

The application relates to the technical field of optical engineering, in particular to a simulation analysis method and system of an optical lens. The method comprises the following steps: obtaining an optical lens structure drawing; determining an assembly relationship according to the optical lens structure drawing to obtain assembly relationship data; constructing an optical lens model according to the assembly relationship data; performing light ray tracing simulation according to the optical lens model to obtain light ray tracing data; performing imaging offset analysis based on the light ray tracing data to obtain imaging offset data; performing photosensitive chip eccentricity analysis according to the imaging offset data to obtain photosensitive chip eccentricity data; performing lens thermal stability detection according to the photosensitive chip eccentricity data to obtain lens thermal stability data; and performing Strehl ratio analysis based on the light ray tracing data to obtain Strehl ratio values. The application improves the precision of lens design and the pertinence and reliability of manufacturing process improvement based on optical engineering technology.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of optical engineering, in particular to a simulation analysis method and system of an optical lens. BACKGROUND

[0002] Traditional optical lens simulation analysis usually relies on simulation of a single physical field, such as only ray tracing or wavefront analysis, lacking comprehensive consideration of multi-physical field coupling effects such as thermodynamics and material mechanics, resulting in large deviations between simulation results and actual use environment. In terms of detection of photosensitive chip eccentricity and abnormal packaging structure, there are limited means, and it is difficult to accurately reflect the influence of microscopic structure deformation and welding defects on imaging quality. Many simulation methods focus on single static analysis, lacking real-time simulation of dynamic environmental changes such as temperature fluctuations and mechanical vibrations, and cannot effectively evaluate the thermal stability and mechanical properties of the lens under complex working conditions. The detection sensitivity of microscopic defects such as lens warping, material stress relaxation and micro-cracks is insufficient, and potential failure risks cannot be timely warned, limiting the pertinence and effectiveness of manufacturing process improvement. The traditional simulation analysis process is scattered, lacking integrated automation processing and defect feedback mechanism, resulting in low simulation efficiency, and it is difficult to realize closed-loop optimization of design and manufacturing, restricting the further improvement of optical lens performance and the guarantee of product reliability. SUMMARY

[0003] Therefore, it is necessary to provide an optical lens simulation analysis method and system to solve at least one of the above technical problems.

[0004] To achieve the above-mentioned purpose, an optical lens simulation analysis method comprises the following steps:

[0005] Step S1: Obtain an optical lens structure drawing; determine an assembly relationship according to the optical lens structure drawing to obtain assembly relationship data; and construct an optical lens model according to the assembly relationship data;

[0006] Step S2: Perform ray tracing simulation according to the optical lens model to obtain ray tracing data; perform imaging offset analysis based on the ray tracing data to obtain imaging offset data; and perform photosensitive chip eccentricity analysis according to the imaging offset data to obtain photosensitive chip eccentricity data;

[0007] Step S3: Perform lens thermal stability detection according to the photosensitive chip eccentricity data to obtain lens thermal stability data; perform Strehl ratio analysis based on the ray tracing data to obtain Strehl ratio values; and perform lens warping detection according to the Strehl ratio values to obtain lens warping data;

[0008] Step S4: material stress relaxation analysis is performed on the lens warping data according to the lens thermal stability data, material stress relaxation data is obtained, micro crack detection is performed according to the material stress relaxation data, micro crack data is obtained, the micro crack data is input into the optical lens model, and aberration simulation calculation is performed, aberration simulation data is obtained, and an optical lens defect report is generated according to the aberration simulation data to perform a manufacturing process improvement task.

[0009] The present application realizes accurate modeling of the overall structure of the lens by obtaining the optical lens structure drawing and constructing the optical lens model according to the assembly relationship, ensures the accuracy and integrity of the simulation basic data, can deeply reveal the deviation in the light path and the specific offset characteristics of the imaging point by performing ray tracing simulation and imaging offset analysis based on the optical lens model, and further accurately identifies the photosensitive chip eccentricity phenomenon, thereby improving the precision of the imaging quality evaluation; the lens thermal stability detection is carried out in combination with the photosensitive chip eccentricity data, the thermal expansion and deformation behavior of the lens material under the dynamic temperature environment is effectively simulated and evaluated, thereby guaranteeing the thermal stability performance of the lens under complex working conditions; the Strehl ratio analysis and lens warping detection are performed based on the ray tracing data, the lens wavefront error and warping degree are reflected in detail, and early warning of the degradation of the optical performance is promoted; the material stress relaxation analysis is further performed on the lens thermal stability data and the warping data, the material fatigue and relaxation evolution process is further revealed, and the monitoring capability of the micro crack generation and expansion risk is improved; the micro crack data is fed back to the optical lens model and the aberration simulation is performed, the comprehensive evaluation capability of the influence of the defects on the overall optical performance is enhanced; finally, the optical lens defect report is generated, a precise basis is provided for the manufacturing process improvement, a complete closed loop from structure modeling, optical simulation, mechanical analysis to defect feedback is realized, the systematicness and automation level of the simulation analysis are effectively improved, and the reliability and performance optimization capability of the optical lens design and manufacturing are significantly enhanced.

[0010] Preferably, the present specification also provides an optical lens simulation analysis system for performing the optical lens simulation analysis method as described above, the optical lens simulation analysis system comprising:

[0011] An optical lens model construction module is configured to obtain an optical lens structure drawing, determine an assembly relationship according to the optical lens structure drawing, obtain assembly relationship data, and construct an optical lens model according to the assembly relationship data.

[0012] A photosensitive chip eccentricity analysis module is configured to perform ray tracing simulation according to the optical lens model, obtain ray tracing data, perform imaging offset analysis based on the ray tracing data, obtain imaging offset data, and perform photosensitive chip eccentricity analysis according to the imaging offset data, and obtain photosensitive chip eccentricity data.

[0013] The lens warping detection module is used for lens thermal stability detection according to the photosensitive chip eccentricity data, obtaining lens thermal stability data; Strehl ratio analysis based on light ray tracing data, obtaining Strehl ratio value; lens warping detection according to Strehl ratio value, obtaining lens warping data;

[0014] The optical lens process improvement module is used for material stress relaxation analysis of lens warping data according to lens thermal stability data, obtaining material stress relaxation data; micro-crack detection according to material stress relaxation data, obtaining micro-crack data; inputting the micro-crack data into the optical lens model, and performing aberration simulation calculation, obtaining aberration simulation data; generating an optical lens defect report according to the aberration simulation data to perform manufacturing process improvement tasks.

[0015] The simulation analysis system of the optical lens of the application can realize the simulation analysis method of any one of the optical lenses of the application, and is used as a medium for joint operation and signal transmission between modules to complete the simulation analysis method of the optical lens. BRIEF DESCRIPTION OF DRAWINGS

[0016] Other features, objects and advantages of the application will become more apparent from the following detailed description of non-limiting embodiments made with reference to the accompanying drawings:

[0017] Fig. 1 The figure is a schematic diagram of the step flow of the simulation analysis method of the optical lens of the application;

[0018] Fig. 2 The figure is a schematic diagram of the detailed step flow of step S1 in the application;

[0019] Fig. 3 The figure is a schematic diagram of the detailed step flow of step S4 in the application;

[0020] The implementation of the object of the application, functional features and advantages will be further described with reference to the accompanying drawings. DETAILED DESCRIPTION

[0021] The technical method of the application will be described clearly and completely below with reference to the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the application, not all the embodiments. Based on the embodiments in the application, all other embodiments obtained by those skilled in the art without creative labor belong to the protection scope of the application.

[0022] Further, the accompanying drawings are included to provide a further understanding of the present application, and are incorporated in and constitute a part of this specification. The drawings illustrate embodiments of the present application and, together with the description, serve to explain the principles of the present application. In the drawings:

[0023] It is to be understood that, although terms such as "first", "second", and so on can be used herein to describe various elements, these elements should not be limited by these terms. These terms are only used to distinguish one element from another. For example, without departing from the scope of the exemplary embodiments, a first element can be referred to as a second element, and similarly a second element can be referred to as a first element. The term "and / or" as used herein includes any and all combinations of one or more of the associated associated items.

[0024] To achieve the above object, there is provided Figs. 1 to 3 The present application provides a simulation analysis method of an optical lens, comprising the following steps:

[0025] Step S1: obtaining an optical lens structure drawing; determining an assembly relationship according to the optical lens structure drawing to obtain assembly relationship data; and constructing an optical lens model according to the assembly relationship data;

[0026] In this embodiment, the obtained optical lens structure drawing is a standardized two-dimensional CAD format file, the file type is *.dwg or *.dxf, and the drawing contains all component numbers, size parameters, connection methods, lens positions, and material type labels of the optical lens. AutoCAD Mechanical 2024 is used to perform a structure boundary identification operation on the drawing, call its geometry recognition module to convert all optical component contour lines into identifiable structure areas, and use a topological analysis algorithm to identify assembly contact boundaries. The contact boundary data is converted into structure assembly constraint relationships, and the connection sequence, fitting clearance (in units of μm, with a precision control within ±1 μm), structure fixed point position (recorded in the form of X, Y, and Z three-axis coordinates), and assembly tolerance (set according to the ISO 286-2 H7 / f7 standard) between each lens, gasket, and lens barrel are recorded in table form. After the assembly relationship data is extracted, Siemens NX 2306 modeling tools are used to perform three-dimensional assembly modeling, and the assembly modeling module is used to input the material parameters of each lens (such as refractive index n = 1.5168 ± 0.0002, Abbe number V = 64.1, and material N-BK7), and the material of the lens barrel (such as hard aluminum alloy AL6061-T6, and thermal expansion coefficient 23.6 x 10-6 The size data is input in millimeters, and a complete optical lens three-dimensional structure model is constructed.

[0027] Step S2: performing ray tracing simulation according to the optical lens model to obtain ray tracing data; performing imaging offset analysis based on the ray tracing data to obtain imaging offset data; and performing photosensitive chip eccentricity analysis according to the imaging offset data to obtain photosensitive chip eccentricity data;

[0028] In this embodiment, based on the completed three-dimensional lens structure model, ZemaxOpticStudio 23.2 version is called to perform ray tracing simulation. The lens three-dimensional data file (format:.STEP) is imported into the system, the chief ray incidence angle is set to 0°, the incidence wavelength range is set to 486.1 nm (blue), 587.6 nm (yellow), and 656.3 nm (red) three design wavelengths, the light source type is set to point light source, the total number of simulated rays is set to 1,000,000, and the energy weight of each ray is set to 1×10 -6 W. The complete non-sequence tracing mode is enabled, and all interface reflection, refraction, and scattering conditions are analyzed. The ray tracing data table is output, and the fields include the three-dimensional coordinates (unit: μm) of the starting point, the refraction interface, and the final imaging point of each ray, the path length, the energy attenuation rate, etc. The ray tracing data is imported into the Matlab R2023a environment, and a program is written to calculate the Euclidean distance between the image plane coordinates of each ray endpoint and the theoretical focal point to obtain the offset vector (Δx, Δy, Δz), which is calculated by the formula Δ = √[(x-x0) 2 +(y-y0) 2 +(z-z0) 2 ]. According to the vector, the offset angle θ = arctan(Δr / f) is calculated using the arctangent function, where Δr is the image plane distance offset, and f is the lens design focal length (such as f = 50 mm). The offset angle data is divided into a 1 mm × 1 mm grid on the image plane and statistically analyzed to form a histogram of the offset direction.

[0029] Step S3: performing lens thermal stability detection according to the photosensitive chip eccentricity data to obtain lens thermal stability data; performing Strehl ratio analysis based on the ray tracing data to obtain Strehl ratio values; and performing lens warping detection according to the Strehl ratio values to obtain lens warping data;

[0030] In this embodiment, the photosensitive chip eccentricity data is input into the thermal stability detection system based on the (x, y, z) offset vector and the offset time, and ANSYS Mechanical 2023R1 is used to perform thermal load analysis of the lens material. The thermal expansion coefficient range is set to 1.2×10 -6 / K to 4.8×10-6 K, the thermal conductivity is 0.3 W / (m·K) to 1.8 W / (m·K), and specific parameter values are input according to different lens materials such as N-SF6 and CaF2. The environmental temperature is set to a range of -20℃ to 85℃, the temperature change gradient is 2℃ / min, the simulation step is 10s, the heat boundary condition is applied to the fixed end surface of the lens barrel, and the contact thermal resistance r=0.002 K·m 2 / W is applied to the lens interface. After the simulation is completed, the thermal expansion displacement vectors of each structure point are extracted and the chip center drift amount is compared, and the thermal stability data chart is output. At the same time, the ray tracing data is loaded in Zemax, the wavefront difference data is extracted through the built-in StrehlRatio analyzer, the point spread function (PSF) is calculated, and according to the ratio of the actual spot energy normalization to the ideal diffraction limit, the Strehl ratio value (generally in the range of 0.6-0.95) is obtained. Compare the Strehl ratio threshold value 0.8, if the value is lower than the threshold value, start the warping detection. The lens warping detection is based on three-dimensional deformation grid analysis, using GOM Inspect Professional to extract in-plane displacement and normal displacement, and analyzing the maximum warping point position and its corresponding warping amount (unit: μm).

[0031] Step S4: According to the lens thermal stability data, material stress relaxation analysis is performed on the lens warping data to obtain material stress relaxation data; according to the material stress relaxation data, micro-crack detection is performed to obtain micro-crack data; the micro-crack data is input into the optical lens model, and aberration simulation calculation is performed to obtain aberration simulation data; according to the aberration simulation data, an optical lens defect report is generated to perform manufacturing process improvement tasks.

[0032] In this embodiment, first, the temperature distribution data in the lens thermal stability analysis is used to generate a thermal expansion response stress map in ANSYS Workbench, and the heat source input is taken as a volume heat flow q=5.5×10 3 W / m 3The lens structure grid model is imported, the grid size is set to 0.1 mm, and the material properties include Young's modulus E = 72 GPa and Poisson's ratio v = 0.23. The obtained thermal strain e th = aD T is superimposed with the mechanical strain to obtain the total deformation vector. Through the overlapping area of the maximum deformation point and the previous lens warping point, the warping deformation growth rate is calculated, and the initial residual stress s 0 = 15 MPa is set in the area, and the stress attenuation trend under the continuous heat load for 30 min is analyzed. The material stress relaxation analysis adopts the standard Prony model, sets the relaxation time constant t = 300 s and the modulus ratio g 1 = 0.2, calculates the stress attenuation curve, and extracts the residual stress s r at t = 900 s as the relaxation reference value. The ultrasonic phased array probe (center frequency of 10 MHz, detection sensitivity of 0.01 mm) is used to detect the micro-cracks in the corresponding lens area, and the crack echo time and reflection amplitude are obtained. According to the crack echo reflection energy lower than the baseline by 10 dB, it is determined that the crack exists, and if the crack length exceeds 30 pm, it is marked as data input into the aberration simulation module. The built-in Aberration analyzer of Zemax is called, the Zernike polynomial term coefficient is fitted, the aberration simulation report is generated, and the spherical aberration, coma and coma data are output. Finally, the structural defect report is formed.

[0033] Preferably, step S1 comprises the following steps:

[0034] Step S11: obtaining an optical lens structure drawing;

[0035] In this embodiment, the obtained optical lens structure drawing is a three-view CAD drawing, the file format is DWG, and the drawing includes a front view, a sectional view and an axonometric view. The drawing is exported from SolidWorks by the structure design department, and the marking content includes the geometric dimensions (unit: mm, accuracy: ±0.01 mm) of each optical component, the relative coordinate system and the material attribute identification code (for example, lens numbers L1-L6, material codes N-BK7, N-SF10, etc.). The drawing is imported into the Autodesk AutoCAD Mechanical 2023 environment, the "boundary recognition" plug-in is used to enable the grouping of graphics elements, the continuous frame is identified as a component candidate area, and the layers are distinguished. In the drawing, different layers represent lenses, apertures, supports and other components, and the coordinate reference is set with the optical axis as the positive direction of the Z axis. After importing, the drawing is checked to ensure that all component boundaries in the drawing are closed to avoid errors in subsequent identification.

[0036] Step S12: identifying the component structure boundary based on the optical lens structure drawing to obtain component structure boundary data;

[0037] In this embodiment, an algorithm based on image edge tracking combined with geometric topology analysis is used to extract the component boundary of the CAD drawing. The contour detection function cv2.findContours of OpenCV 4.8 is called to identify the pixel-level boundary of each component in the drawing, and the geometric feature comparison rules (such as ellipse for lens recognition, rectangle for support recognition) are used for component classification. The contour data is converted into a structural boundary box coordinate form (x_min, y_min, x_max, y_max) with a unit of mm and a conversion accuracy of 0.01 mm through the minimum circumscribed rectangle fitting method. The structural boundary data is formed into a dictionary structure according to the component number and material type after extraction, and is exported as a structural boundary table for subsequent structural constraint analysis.

[0038] Step S13: performing component constraint analysis according to the component structural boundary data to obtain component constraint relationship data;

[0039] In this embodiment, the connection mode between components is identified by analyzing the contact surface position, overlapping area and angle relationship between the boundaries of each component. A finite surface contact determination algorithm is used to calculate the shared length of the boundary, and a contact threshold of more than 5% overlapping length is set to determine the structural connection. If the contact position is in the X-Y plane and the contact width is less than 10% of the lens diameter, it is marked as a gasket contact; if the boundary is completely nested, it is determined as a threaded cooperation or sliding connection. The constraint types are “fixed”, “interference”, “gap”, “sliding” and “elastic”, and the results are arranged into a component connection matrix and assigned a number, such as L1-support for fixed connection (number 01) and L2-gasket for elastic connection (number 04). A component constraint relationship data table is formed, which includes component number, contact surface direction, connection type and connection force path.

[0040] Step S14: deriving a structural connection path based on the component constraint relationship data to obtain structural connection path data; determining a structure assembly sequence according to the structural connection path data to obtain structure assembly sequence data;

[0041] In this embodiment, a structure connection path derivation method based on graph traversal is used, which takes the component constraint relationship data as input, constructs an undirected graph model, and the nodes are components and the edges are connection relationships. The depth-first search (DFS) algorithm is used to extract the shortest connection path of each component and identify the sub-path group that exists in parallel or series structure. In order to construct the assembly sequence, the topological sorting algorithm is used to convert the acyclic structure graph into a linear sequence, and the order priority is set according to the actual assembly rules (such as support installation is better than lens insertion, lens insertion is better than screw ring rotation), and finally the structure assembly sequence data list is formed, which is in the format of [L1, gasket 1, support, L2, gasket 2, fixed ring], and is stored in the assembly sequence control file.

[0042] Step S15: Assembling relationship modeling according to the structure assembling sequence data, obtaining assembling relationship data;

[0043] In this embodiment, the assembling relationship modeling is completed in PTC Creo Parametric 10.0. The aforementioned assembling sequence data is imported, and the standard three-dimensional geometric model (STEP format, resolution of 0.01 mm) of each component is loaded. The outer circle of the lens is aligned with the inner wall of the lens barrel by using the automatic constraint method based on the assembling reference surface, the positioning step surface is set as the contact interface, the distance constraint is 0 mm, and the rotational degree of freedom is locked. The compression force direction along the Z axis is applied to all lens components (simulate elastic assembly), the interference value is set to ±3 μm in the assembly simulation, the relationship mapping table of all assembling pairs is established according to the ISO 286 IT6 tolerance requirements, including assembling part ID, connection mode, axial position, radial position, eccentricity limit (controlled within ±5 μm), etc. The assembling relationship data structure body is output to the local database for subsequent calling.

[0044] Step S16: Constructing the optical lens model according to the assembling relationship data.

[0045] In this embodiment, the Siemens NX 2306 advanced modeling module is called, each component model is imported in sequence according to the assembling relationship data, and the NX assembly constraint module is used for precise positioning. The lens coordinate origin is set as the center point of the first lens L1, the Z axis direction is the optical axis direction, and the component assembling sequence strictly follows the structure assembling sequence list. The lens gap is modeled in the form of tolerance constraint (such as 0.02 mm ± 0.005 mm gap between L2 and the gasket), and the material properties of each lens are set, the corresponding refractive index (such as n = 1.647), density, and thermal expansion coefficient (such as 3.4 × 10 -6 / K) are given through the built-in material library. After modeling, the whole lens 3D structure file (format.x_t) is generated, and the component assembling state and number information is embedded in the quality attribute, which is used for subsequent simulation data scheduling and light tracing system loading.

[0046] Preferably, the light tracing simulation according to the optical lens model in step S2 comprises:

[0047] Extracting optical lens surface attribute information according to the optical lens model;

[0048] In this embodiment, the structural data of the three-dimensional optical lens model is imported into the Siemens NX 2206 platform in the.STEP format through three-dimensional CAD software. In the Siemens NX environment, the "surface evaluation" function under the "analysis" module is enabled, and a piece-by-piece identification operation is performed on each lens component. The outer surface and the inner surface of each lens need to be independently identified and attribute extracted, including: curvature radius, main cross-section position, surface center point three-dimensional coordinates, normal direction vector and surface normal continuity. The curvature radius is measured in millimeters, the measurement range is limited to 1 mm to 150 mm, the extraction accuracy is fixed at 0.001 mm, and the extracted three-dimensional coordinate values need to meet the consistency constraints of the XYZ coordinate axes in the structural data, with an allowable error of not more than ±0.002 mm. In order to extract the coating parameters of the lens surface, the material code needs to be queried from the ERP material code system according to the material code marked in the structural drawing, and positioned to the optical coating parameter library. From this database, read the corresponding coating type of each lens surface (such as MgF2 single-layer anti-reflection film, Ta2O5 / SiO2 multi-layer dielectric film), and simultaneously extract the corresponding waveband range (such as 450nm to 650nm), film thickness (such as 90nm to 130nm), spectral transmittance (such as ≥98%) and film layer base material (such as K9, N-BK7). For lenses without explicit material number marking, use the optical path direction and coating arrow diagram on the drawing, combined with the lens position logic to deduce its acting surface, and then match the database. The microscopic roughness of the optical lens surface is measured by laser confocal microscope (model: Keyence VK-X200), the scanning area is set to 100μm×100μm, the scanning interval is 0.2μm, the longitudinal resolution is set to 10nm, the average surface roughness Ra parameter is extracted from the result, and the standard deviation is recorded. The Ra value range is controlled between 2nm to 10nm, and any surface exceeding this range needs to be recorded as an anomaly. All the above structural parameters and material parameters are recorded in a unified structure-optical interface document template (Excel format, fields include: component number, surface serial number, curvature, thickness, material name, coating name, waveband range, roughness, extraction time, extractor, etc.), and automatically numbered and archived according to the lens serial number, exported as a.csv format file, providing input data for subsequent ray propagation simulation and performance analysis. The entire operation process prohibits omission of component identification, and each lens surface is independently numbered to ensure one-to-one correspondence between the model structure and the material physical properties.

[0049] Calculating the refractive index of the lens based on the surface attribute information of the optical lens;

[0050] In this embodiment, the lens material is retrieved from the material database according to the material identification number in the structural drawing, and the specific material is determined, such as N-BK7, Fused Silica, etc. Then the measured refractive index value of the material in the target working waveband is called. Taking 550 nm wavelength as the reference, the corresponding waveband range is set to 400-700 nm. The refractive index values of each optical glass are recorded in the material database, and the standard experimental data is used as the source, with an accuracy of 0.0001. The refractive index data of all lens materials is stored at 10 nm intervals in the current wavelength range, and can be directly used for subsequent calculation of the light propagation path.

[0051] According to the lens refractive index, the light propagation simulation is performed to obtain the light propagation trajectory data;

[0052] In this embodiment, the "sequential ray tracing mode" is selected in the Zemax OpticStudio platform, and the geometric structure and material parameters of all lenses are manually entered into the software. The incident light source is set as a parallel light source, the spot diameter is 10 mm, the wavelength is 550 nm, and the emission direction is parallel to the lens optical axis. The system automatically calculates the refractive path of the light according to the lens surface shape and refractive index. Each light ray enters the lens system from the object side, passes through multiple mirrors and lenses in turn, and terminates at the image plane. The software outputs data including the incident point position, exit point position, light transmission direction, lens interaction record of each light ray, which is saved as a text file with light ray number and tracing sequence number for subsequent analysis.

[0053] According to the light propagation trajectory data, the imaging point is identified, and the image plane imaging distribution data is obtained;

[0054] In this embodiment, all light ray endpoint coordinates in Zemax output are taken and projected onto the set image sensor plane. The size of the image sensor is set to 6.4 mm x 4.8 mm, the resolution is 2048 x 1536 pixels, and the size of a single pixel is 3.1 μm. All light ray intersection points are assigned to the corresponding pixel grid according to their positions, the number of hit light rays for each pixel point is counted, and the image plane spot intensity value is recorded. The image processing library in Python (such as OpenCV) is used to read these intensity distribution data and generate a two-dimensional grayscale image. The highlight area in the image corresponds to the light concentration point, and the dark area is the spot edge area. The final image plane imaging distribution data is the spatial coordinates and light intensity value of each pixel point.

[0055] Based on the image plane imaging distribution data, the light beam energy concentration degree is analyzed to obtain the energy focusing data;

[0056] In this embodiment, the above-mentioned gray scale image data is read to identify the light spot center area with the most concentrated energy. The position point with the maximum gray scale value in the light spot is found as the energy center point. Then, the minimum circular area covering 80% of the total energy is measured from the center outward, and the radius of the area is recorded as the focusing radius. Further, the light spot edge definition is extracted by comparing the gray scale gradient between different energy ring layers to determine whether the light beam has obvious diffusion. If the focusing radius is less than 30 μm and the gray scale gradient change rate exceeds a set threshold (such as 10 gray scale values / μm), it is considered as high-quality focusing. Finally, the coordinates of the focusing point, the focusing radius, the maximum light intensity, the energy concentration area and other parameters are output.

[0057] calculating the focal point offset based on the energy focusing data;

[0058] In this embodiment, the coordinates of the energy center point are compared with the image center position (i.e. the theoretical imaging point of the optical axis). The image center coordinates are (1024, 768) pixel positions, corresponding to the actual physical position of (3.17 mm, 2.38 mm). The actual distance between the energy center coordinates and the ideal position is measured, which is defined as the focal point offset, with the unit of microns. The offset is obtained by calculating the straight-line distance between the two coordinate points, and the direction angle is also recorded, i.e. the direction of the offset is left, right, up or down. The offset distance and direction data are used as the reference for subsequent correction of system aberration in ray tracing.

[0059] performing ray tracing analysis based on the focal point offset to obtain ray tracing data.

[0060] In this embodiment, according to the focal point offset result, the tracing parameters are re-set, and Zemax is re-started for high-density ray tracing. In this tracing, the exit angle range of each light ray is set to ±5°, and the number of light rays emitted is 30,000. The tracing records the position, direction vector, energy loss, reflection or transmission attribute of each light ray at each lens interaction point. After the tracing is completed, a detailed light ray data table is exported, which includes the complete path of each light ray, the coordinates of the lens interaction point, the incident / exit angle and whether it hits the image center area. Through analysis of these light ray data, it can be further determined which type of light ray is offset by which lens surface, and the imaging system is further optimized.

[0061] Preferably, the imaging offset analysis based on the ray tracing data in step S2 comprises:

[0062] extracting the light ray image plane three-dimensional coordinates based on the ray tracing data to obtain light ray image plane three-dimensional coordinate data;

[0063] In this embodiment, Zemax OpticStudio 22.3 is used for ray tracing simulation of the optical lens, and the output format is set to.RAY. The output fields include the exit position coordinates (X, Y, Z) of each ray, the incident angle (θx, θy), and the power normalization value. The simulation wavelength is set to 550 nm, the total number of rays is set to 100000, and the rays are distributed symmetrically around the center of the Gaussian beam within a cone angle of ±5° around the principal axis. The image plane coordinates are referenced to the system optical axis as the Z-axis, and the image plane is set at the Z=0 mm plane. The unit is unified as mm. The.RAY data output after simulation is imported into the Python 3.10 environment, and the NumPy and Pandas modules are called for structured processing. The end point coordinates (X, Y, Z) of the rays are extracted to form a three-dimensional matrix, and the output is an “image plane coordinate data matrix” saved as a.csv file with fields RayID, X, Y, Z, and each field data is retained to six decimal places.

[0064] According to the three-dimensional coordinate data of the image plane of the light, the offset vector is calculated, and the imaging point offset vector data is obtained;

[0065] In this embodiment, according to the ideal focal point coordinate definition, the focal point position of the system design principal axis on the image plane is (X=0, Y=0, Z=0), and the vector difference calculation is performed on all actual ray end point positions. The specific formula is that the offset vector V_i of each ray is (X_i, Y_i, Z_i)-(0, 0, 0), and a three-dimensional offset vector array is obtained. The vector calculation is completed in the Matlab R2022b platform, and matrix operation is used for batch processing. The result is composed of three columns of Vx, Vy, and Vz to form an offset vector dataset, with a unit of mm, and the output format is.csv. Each row in the file corresponds to the offset vector of a ray.

[0066] According to the imaging point offset vector data, the offset angle is calculated, and the offset angle data is obtained;

[0067] In this embodiment, the offset angle of each offset vector is calculated as the included angle between the vector and the positive direction of the Z-axis (ideal focusing direction). The cosine inverse function calculation formula θ=arccos(Vz / |V|) is used, where |V| is the modulus of the offset vector, which is calculated by the three-dimensional Euclidean norm. The result is converted to an angle unit (°) and retained to two decimal places. All offset angles are recorded in a table with fields RayID and offset angle, and the output is an offset angle data file. In order to exclude abnormal offset rays, a rejection threshold is set for rays with an offset angle greater than 15°, which are not involved in subsequent statistics as a system deviation screening mechanism.

[0068] Based on the offset angle data, the offset direction distribution statistics are performed, and the imaging offset direction data are obtained;

[0069] In this embodiment, the direction corresponding to the offset angle is visualized and analyzed using the Matplotlib and Seaborn modules in the Python environment. The direction angle is calculated after projecting the offset vector on the XY plane The unit is degree, and the distribution range is set to 0°-360°. All direction angle distributions are counted with a bin size of 10° to form a histogram distribution of 36 direction segments, and the output is a frequency table of offset directions (fields are angle interval and frequency). The direction segment with a direction frequency exceeding 5% of the total number of rays is marked as the concentrated offset direction, which is used for subsequent focus error source judgment.

[0070] The offset amplitude data is calculated according to the imaging offset direction data;

[0071] In this embodiment, the offset amplitude value is obtained by calculating the projection length of each offset vector on the XY plane, and the formula is R=sqrt(X 2 +Y 2 ). All calculations are based on NumPy vector operations, and the results are rounded to three decimal places with a unit of mm. The amplitude value is added to the RayID identifier to form an offset amplitude table. The amplitude abnormality screening threshold is set to 0.5mm, and the rays exceeding this value are marked with their offset position coordinates for subsequent micro-crack error analysis and comparison. The offset amplitude is divided into five levels (<0.1mm, 0.1-0.2mm, 0.2-0.3mm, 0.3-0.5mm, >0.5mm) and the number of rays in each level is counted for subsequent image quality level classification reference.

[0072] The imaging offset data is obtained by evaluating the imaging focus offset degree based on the offset amplitude data.

[0073] In this embodiment, the imaging offset degree is evaluated according to the aforementioned offset amplitude level results, and the following evaluation criteria are set: Grade I (average offset amplitude <0.1mm), Grade II (0.1-0.2mm), Grade III (0.2-0.3mm), Grade IV (0.3-0.5mm), and Grade V (>0.5mm). The average value and standard deviation of the offset amplitude of all rays are calculated to generate an offset statistical summary table, which includes the average value, maximum value, minimum value, standard deviation, and offset level. The imaging offset data is structured in JSON format and associated with the lens ID and simulation task number for archiving, which serves as the core data for imaging error evaluation for subsequent photosensitive chip eccentricity analysis.

[0074] Preferably, the photosensitive chip eccentricity analysis according to the imaging offset data in step S2 comprises:

[0075] The imaging offset period data is obtained by counting the imaging offset period according to the imaging offset data;

[0076] In this embodiment, the imaging offset data obtained in the previous stage is arranged in timestamp order, and the data recording frequency is 10 frames per second. The offset amplitude analysis threshold is set to 0.3 mm, and the offset state is considered to be no offset state below the threshold, and the continuous frame record is considered to be offset state above the threshold. Through the logical judgment condition set by the Python script, the frame segment of continuous offset state is marked, the start time and end time are counted, and the duration of each offset period is calculated. The generated data fields include: offset period number, start frame number, end frame number, duration (unit: ms), maximum offset amplitude. The results are exported in.csv format as the time basis for subsequent encapsulation layer deformation detection.

[0077] Based on the imaging offset period data, the encapsulation layer deformation detection is performed to obtain the encapsulation layer deformation data;

[0078] In this embodiment, the FLIR A615 infrared thermal imager is used to perform thermal response imaging on the lens packaging area, and the thermal image resolution is 640x480, and the frame rate is set to 60 frames per second. The thermal image sequence of the corresponding time period is extracted with the offset period as the index. Image registration and difference analysis are performed using OpenCV, and the difference threshold is set to 2°C. The thermal response trend of the edge pixels of the encapsulation layer is compared. A script is written in Matlab to calculate the thermal expansion rate of the encapsulation layer by extracting the thermal image gray scale change gradient. The reference thermal expansion coefficient of the material is set to 1.2x10 -6 / K. When the image gray scale change amplitude is greater than 15 gray scales, and the area size is continuously more than 150 pixels, it is determined that there is thermal deformation in the region, and the encapsulation layer deformation mask image is output, and the maximum displacement value of the deformation region is counted, which is in μm.

[0079] Based on the encapsulation layer deformation data, the misalignment degree of the photodiode array is determined to obtain the photodiode array misalignment data;

[0080] In this embodiment, the encapsulation deformation region detected by infrared thermal imaging is matched and analyzed with the photodiode arrangement map on the photosensitive chip (imported from the chip design CAD file). The coordinate mapping algorithm is used to convert the encapsulation layer deformation boundary to the photodiode array position map, and whether any photodiode channel is covered by the deformation region is detected. When any deformation displacement exceeds 10 μm and the number of overlapped photodiode channels is greater than 5, it is considered that there is array misalignment phenomenon. The misalignment degree is represented by the ratio of the number of affected channels to the total number of channels, and the misalignment direction (X or Y axis) and the maximum offset pixel coordinate are recorded. The output fields include misalignment region number, channel start and end number, maximum misalignment value (μm), misalignment direction, and misalignment coverage rate (%).

[0081] Based on the photodiode array misalignment data, the welding void detection is performed to obtain the welding void data;

[0082] In this embodiment, the physical position corresponding to the misaligned area is taken as the center to perform micro-focus three-dimensional tomography in an industrial CT device (such as YXLON FF20CT). The scanning parameters are set to 80 kV, 90 μA, the voxel resolution is 2 μm, and 2000 layer images are collected. VGStudio MAX is used to perform three-dimensional reconstruction on the scanning images, and the material density contrast threshold is set to 0.1 g / cm 3 to identify internal bubbles or low-density welding defects. The volume of all cavities is statistically analyzed, and if the volume of any cavity is greater than 50 μm 3 or the total number of cavities is greater than 3, the output is a cavity abnormal state. The result data format is JSON, and the fields include cavity number, volume, center coordinates, pin number to which the cavity belongs, and defect category.

[0083] According to the welding cavity data, the stress concentration area of the chip substrate is identified;

[0084] In this embodiment, the finite element analysis tool ANSYS Mechanical is used to perform thermal-structural coupling simulation on the chip welding layer and the substrate. The three-dimensional coordinates and size information of the welding cavity are input, and the material parameters of the substrate (elastic modulus 120 GPa, Poisson's ratio 0.3) and the material parameters of the solder joint (SnAgCu alloy, elastic modulus 55 GPa, yield stress 60 MPa) are set. The working environment temperature cycle is set to -20°C to 85°C, and the loading cycle is 500 times. The thermal cycle deformation between the welding pin and the substrate is simulated, the local mechanical constraint is weakened in the cavity influence area, and the equivalent stress distribution change is observed. If the Von Mises stress of a certain area is greater than 50 MPa and the area is continuously greater than 0.02 mm 2 , it is defined as a stress concentration area, and the area coordinates and corresponding stress peak value are output.

[0085] Based on the stress concentration area of the chip substrate, the photo-sensitive chip eccentricity analysis is performed to obtain the photo-sensitive chip eccentricity data.

[0086] In this embodiment, the stress concentration area and the chip coordinate origin are analyzed by spatial geometric projection, and the distribution barycentric offset is calculated. The center of the designed chip is taken as the origin, and the allowable eccentricity tolerance is set to ±5 μm. The average displacement direction and displacement vector of all stress concentration points are statistically analyzed, and the result is rounded to three decimal places with the unit of μm. If the offset exceeds the design tolerance, the overall eccentricity trend of the chip is determined according to the direction component (such as eccentricity to the upper left corner by 5.32 μm). The eccentricity data record format is.csv, and the fields include chip number, offset X value, offset Y value, offset total, direction quadrant, and judgment conclusion (whether eccentric or not). The final result is called by the lens thermal stability detection module.

[0087] Preferably, step S3 comprises the following steps:

[0088] Step S31: input the photosensitive chip eccentricity data into the lens thermal stability detection system;

[0089] In this embodiment, the photosensitive chip eccentricity data is imported into the data receiving module of the thermal stability detection system in the form of a table file (.csv format). The eccentricity data table should include the fields: chip number, X offset value (unit: μm), Y offset value (unit: μm), total offset (unit: μm), and offset direction angle (unit: °). The system checks the field order and value type based on the reading script, and the offset value data is kept to three decimal places. Values exceeding ±20 μm are set as error input and return a warning. Qualified data is parsed and written as initial structural offset boundary conditions into the initial geometric configuration of thermal stress calculation for subsequent thermal environment simulation loading.

[0090] Step S32: set the thermal expansion coefficient of the lens material to 1×10 -6 / K to 5×10 -6 / K and the thermal conductivity to 0.1 W / (m·K) to 2.0 W / (m·K);

[0091] In this embodiment, the material database of each component of the lens is called, and the lens substrate parameters are extracted according to the lens serial number in the lens structure diagram. The thermal expansion coefficients of all glass substrates are uniformly set to 1.2×10 -6 / K to 4.6×10 -6 / K, the metal cylinder is set to 2.3×10 -6 / K, the thermal conductivity of the lens is set to 0.85 W / (m·K), and the cylinder material is set to 1.6 W / (m·K). The parameter setting is manually input through ANSYS Material Editor, and the attribute is locked to avoid automatic updating during simulation. The numerical precision of all parameters is set to 6 decimal places, and the unit conversion is unified to the International System of Units. The thermal expansion coefficient of the adhesive material between the lens and the chip is 3.0×10 -6 / K, and the conductivity is 0.25 W / (m·K), which is imported from the supplier's material inspection report and verified for data consistency.

[0092] Step S33: set the environmental temperature change to -20℃ to 80℃, the temperature change rate to 1℃ / min to 10℃ / min, and the thermal stress threshold to 5MPa to 50MPa;

[0093] In this embodiment, during the thermal boundary setting process, the environmental temperature curve is set by the temperature control simulation module of the thermal stability detection system. The curve is divided into a linear heating stage and a cooling stage, with the initial temperature value set to -20°C and the final value set to 80°C. The curve slope corresponds to the temperature change rate, and three cases are tested: 1°C / min, 5°C / min, and 10°C / min. The longest experimental period does not exceed 200 minutes. The temperature control load is applied to the outer wall of the lens in a boundary loading manner. The thermal stress threshold is set to a range of 5 MPa to 50 MPa, which is input into the simulation control parameters as a stress judgment condition. The thermal stress distribution is calculated by the maximum principal stress criterion, and all regions exceeding the threshold are marked as risk areas by the system. The simulation accuracy requires that the grid division size does not exceed 0.2 mm, and tetrahedral grid elements are used for volume discretization.

[0094] Step S34: Run the lens thermal stability detection module and output the lens thermal stability data.

[0095] In this embodiment, before the simulation starts, the structure inspection module is used to ensure that all material properties, initial eccentricity state, and boundary conditions are complete and correct. Then, the thermal-structure coupling solver is called to perform the simulation task. The time step is set to 10 seconds for each simulation, and the total simulation time is synchronized with the temperature change (e.g., 80 minutes of heating, the simulation time is set to 4800s). The transient heat conduction-thermal stress coupling analysis method is used. After the simulation is completed, the system automatically extracts the maximum thermal stress, maximum deformation, and deformation direction of each optical element in the lens at each time point. The output results are stored in.xlsx and.vtk formats, and the fields include: component number, stress maximum value (unit: MPa), thermal deformation displacement (unit: μm), time point (unit: s), and corresponding environmental temperature. The deformation direction is recorded in polar angle form, with an angle range of 0°-360°.

[0096] Step S35: Perform Strehl ratio analysis based on the ray tracing data to obtain the Strehl ratio value.

[0097] In this embodiment, the point spread function (PSF) extraction operation is performed on the ray tracing data using Zemax OpticStudio. The main optical axis parameters in the optical system, wavelength range (400nm-700nm), and incident angle (0°, 5°, 10°) are input through the simulation module. The resolution is set to 0.01mm per pixel, and the off-axis calculation is performed on the PSF images obtained on each focal plane to extract the Strehl Ratio. The calculation of the Strehl Ratio is based on the maximum value of the central bright spot intensity in the ideal system, and the actual optical system is marked as having decreased optical performance if the value is less than 0.8. All Strehl Ratio results are organized by lens number and optical axis position, and the output fields include: measurement point number, incident wavelength, incident angle, actual PSF intensity, theoretical intensity, Strehl Ratio value, and unit is dimensionless ratio (maximum is 1.0).

[0098] Step S36: Lens warping detection is performed according to the Strehl Ratio value, and lens warping data is obtained.

[0099] In this embodiment, the analysis process compares the Strehl Ratio change trend of the same lens assembly at different temperatures, and evaluates the lens curvature change by combining the surface deformation map (Sag Map) obtained in Zemax. At a temperature of 80℃, 9 points are selected at the center and edge of the optical axis, and the Z-direction displacement values of the lens surface are read through the Sag Map, with the unit being μm and the recording range being 0μm-300μm. Difference operation is performed on the two groups of data before temperature rise (25℃) and after temperature rise (80℃), and the curvature change amount is obtained. If the warping deformation of the adjacent area is greater than 10μm and presents a continuous deformation trend (the warping area exceeds 50mm 2 ), it is marked as a lens warping area. The lens warping data is output by component, including lens number, maximum deformation value, deformation direction (X or Y), curvature change amount, and detection temperature. The output results are synchronized to the lens defect database for subsequent analysis.

[0100] Preferably, step S35 includes the following steps:

[0101] Step S351: Extracting wavefront error information based on ray tracing data; performing wavefront distortion analysis according to the wavefront error information to obtain wavefront distortion data;

[0102] In this embodiment, the data of completed ray tracing is imported into the "WavefrontMap" module in Zemax OpticStudio, the observation surface is set as the image plane position, the input wavelength is 550 nm, the field of view angle is set to 0°, 5°, 10° three angles, and the system automatically outputs the wavefront deviation data under each field of view. The system adopts the RMS wavefront error standard, extracts the path difference between the reference spherical surface and the actual wavefront, in units of nanometers (nm), the interval between each sampling point is 0.1 mm, and the number of wavefront sampling points is set to 64x64. All path differences are extracted according to Zernike polynomial expansion Z4 to Z37, which sequentially maps spherical aberration, coma, astigmatism, and higher-order distortion information such as aberration, and finally forms a wavefront distortion data matrix. The matrix is exported as a.csv format, which contains the distortion number, corresponding Zernike item coefficient, and unit in nanometers.

[0103] Step S352: Point spread function calculation according to wavefront distortion data, to obtain point spread function data;

[0104] In this embodiment, based on the above Zernike coefficient matrix, the PSF (point spread function) calculation module is called in the Matlab simulation environment, the complex amplitude distribution of the light wave front is obtained by fast Fourier transform (FFT) processing, and then the intensity image is output after the amplitude is squared, which is the point spread function image. The image resolution is 512x512 pixels, the pixel interval is set to 1 μm, and the Hanning window is used for Fourier transform window function to suppress edge artifacts. The output point spread function image is saved as a.tiff format with 16-bit grayscale, and the numerical information such as PSF main peak position, main peak intensity, energy diffusion range is output synchronously, recorded in table form, including "pixel number, main peak intensity value (after normalization), extension radius (μm), energy radius (μm)" and other fields.

[0105] Step S353: Draw a light spot distribution map according to the point spread function data to obtain actual light spot distribution data;

[0106] In this embodiment, the Python Matplotlib library is called to visualize the point spread function image in.tiff format. The image is drawn in the form of a heat map, with the X and Y axes in microns (pm), the color scale representing light intensity distribution, and the intensity range set to 0 to 1. The center of the main optical axis is set to (0, 0), and the center alignment is performed according to the position of the main peak intensity of the PSF. The actual spot distribution image size is uniformly set to 100 pm x 100 pm, and a two-dimensional light intensity heat map is drawn with a sampling interval of 0.5 pm. The main peak edge of the image is marked with a 10% energy contour line to facilitate subsequent normalization processing, and saved as a.png format image with an image resolution of 300 dpi. All image numbers are consistent with the corresponding numbers in the previous step, and a one-to-one mapping relationship is established with the Zernike coefficient table.

[0107] Step S354: Peak normalization processing is performed according to the pre-set ideal diffraction spot distribution data and the actual spot distribution data, to obtain normalized intensity ratio data.

[0108] In this embodiment, the OpenCV image processing tool is called to perform pixel-by-pixel comparison processing on the actual spot image and the ideal diffraction spot image (Airy disk standard image). First, the two images are pixel-aligned according to the center position and image size, and the image size is unified to 512 x 512 pixels. Then, the maximum gray value of the main peak in each image is extracted, and each pixel gray value is divided by the main peak value for normalization processing, and the output result is a unit gray distribution matrix (0-1). The normalized gray values of the same position pixels are then operated to generate a normalized intensity ratio matrix. The matrix output is in.csv file format, with fields of "pixel coordinate X, pixel coordinate Y, normalized intensity ratio". Abnormal points (higher than 1.2 or lower than 0.2) are automatically marked as distortion offset areas, and excluded from subsequent Strehl ratio analysis.

[0109] Step S355: Calculate the Strehl ratio value based on the normalized intensity ratio data.

[0110] In this embodiment, according to the normalized intensity ratio matrix, all normalized ratios in the region with a center radius of 50 μm are extracted, and the average value thereof is taken as the initial Strehl ratio. The system sets the peak energy ratio in the ideal light spot as the standard 1.0, compares the average intensity of the corresponding region in the actual light spot with the standard, and obtains the actual Strehl ratio value. If there are multiple local maximum points in the sampling region, only the region where the highest main peak is located is used for calculation. All results are uniformly output as "lens number, measurement position number, Strehl ratio value, light spot deviation center position, average normalized ratio in the radius" and the like fields, and are exported as an.xlsx table file with a precision of four decimal places. The Strehl ratio value range is controlled between 0 and 1, and no negative value retention processing is performed. Results below 0.2 are marked as serious distortion areas.

[0111] Preferably, step S36 comprises the following steps:

[0112] Step S361: identifying the imaging degradation area according to the Strehl ratio value;

[0113] In this embodiment, the Strehl ratio value distribution data is imported into an optical analysis software, which has two-dimensional spatial data processing capability. Threshold determination is performed on the Strehl ratio value matrix, and the threshold is set to 0.8. The pixel points below the threshold are defined as imaging degradation points. Based on the connected region algorithm, 8-neighbor connectivity analysis is performed on these degradation points, and the connected degradation points are classified into the same degradation area. The area, boundary contour, barycenter coordinates and maximum extension length of each connected region are extracted, and the area threshold is set to 50 μm 2 to filter the micro-noise region. Finally, a database file containing the number of degradation areas, the spatial coordinate range and the area of each area is output.

[0114] Step S362: detecting the lens transverse compression based on the imaging degradation area to obtain lens transverse compression data;

[0115] In this embodiment, according to the degradation area, the lens cross-sectional image of the corresponding region is imported into a high-resolution microscope tomography system, and the resolution is set to 0.5 μm / pixel. Digital image correlation (DIC) technology is used to track the pixel-level deformation of the continuous lens surface image, and the displacement field in the transverse direction is calculated. The transverse compression value is defined as the difference between the initial cross-sectional length and the current cross-sectional length, and the unit is microns (μm). Micro-deformation within 10 μm is accurately captured. At least 3 groups of time series images are collected for each degradation area, and the time interval is set to 5 minutes to obtain the transverse compression change curve. The output data includes the maximum value, the average value and the time change rate of the transverse compression.

[0116] Step S363: Calculate the in-plane stress gradient based on the lens transverse compression data to obtain in-plane stress gradient data;

[0117] In this embodiment, the transverse compression amount is used to calculate the stress in combination with the elastic modulus and Poisson's ratio of the lens material. The elastic modulus is 70 GPa, and the Poisson's ratio is 0.23. The transverse strain is converted to stress in units of megaPascal (MPa) by applying Hooke's law. The stress difference between adjacent pixel points is calculated by the finite difference method to obtain the in-plane stress gradient in units of MPa / μm. The in-plane stress gradient matrix is stored in the form of a two-dimensional array, and the matrix dimension corresponds to the lens cross-sectional scanning resolution. The regions with a gradient exceeding 0.5 MPa / μm are highlighted, and the gradient graph and numerical table are exported, including the gradient distribution, maximum gradient value, and gradient concentration area position.

[0118] Step S364: Perform coating layer material peeling detection according to the in-plane stress gradient data to obtain coating layer material peeling data;

[0119] In this embodiment, based on the stress gradient graph, the stress gradient peeling threshold is set to 0.6 MPa / μm. Optical microscopy combined with scanning electron microscopy (SEM) is used to observe the surface of the high stress gradient area, and the peeling area is extracted using an image segmentation algorithm. The peeling boundary is segmented by a gray threshold value, and the gray threshold value is set to 120 (8-bit gray scale, range 0-255). The peeling area of the coating layer is counted in units of μm 2 , and the peeling thickness is measured by cross-sectional SEM graph, ranging from 50 nm to 200 nm. The spatial coordinates, area, and thickness information of the peeling area are recorded in the form of a database, and the peeling ratio is calculated as the percentage of the peeling area to the total coating area.

[0120] Step S365: Perform lens adjacent layer misregistration detection based on the coating layer material peeling data to obtain lens adjacent layer misregistration data;

[0121] In this embodiment, the peeling boundary data is used to import a high-resolution tomographic scanning system to obtain three-dimensional interlayer displacement field data. The scanning resolution is 0.1 μm, the spatial coordinates of the surface profiles of the adjacent two layers are collected, and the relative displacement vector between the adjacent layers is calculated by a point cloud registration algorithm. The misregistration amount is defined as the relative displacement module length between the layers, in units of microns, and the misregistration threshold is set to 0.5 μm. The regions exceeding the threshold are marked as misregistration regions. The misregistration vector field graph and misregistration statistical table are output, including the maximum misregistration value, average value, and misregistration region spatial distribution information.

[0122] Step S366: Evaluate the lens warping degree according to the lens adjacent layer misregistration data to obtain lens warping data.

[0123] In this embodiment, based on the dislocation data, combined with the thickness and elastic parameters of the lens material, the bending theory is applied to calculate the local warping curvature of the lens, with the unit of m -1 . Through the mapping relationship between the curvature and the dislocation amount, the warping radius of each local area is calculated, and the data is stored in the form of a two-dimensional matrix, and the matrix elements correspond to the lens scanning resolution. The warping threshold is set to 0.001 m -1 , and the area exceeding this value is defined as the high-risk area of warping. The warping distribution map is displayed in the form of a pseudo-color map, and the numerical output includes the maximum warping degree, the warping area, and the risk area coordinates. The final data is exported as a standard.xlsx file, which is convenient for subsequent defect analysis and process adjustment.

[0124] Preferably, step S4 comprises the following steps:

[0125] Step S41: drawing a temperature distribution map according to the lens thermal stability data; and performing material thermal expansion response calculation based on the temperature distribution map to obtain thermal expansion deformation data;

[0126] In this embodiment, the temperature field data output by the lens thermal stability detection system is imported into a finite element analysis software (such as ANSYS or COMSOL Multiphysics), and the temperature data format adopts a two-dimensional matrix form, with the data unit being Celsius (℃) and the spatial resolution being not less than 0.01 mm. Based on the heat conduction equation, combined with the thermal conductivity (the value range being 0.1 W / (m·K) to 2.0 W / (m·K)), the specific heat capacity (900 J / (kg·K) to 1200 J / (kg·K)), and the density (2200 kg / m 3 to 2500 kg / m 3 ) of the lens material, the three-dimensional temperature field reconstruction is performed by using the heat conduction module of the software to generate a temperature distribution map. In the temperature field simulation, the environmental boundary condition is set as convective heat transfer, the heat transfer coefficient is taken as 5 W / (m 2 ·K), and the boundary temperature is set according to the actual test data, with the change range being -20℃ to 80℃. Subsequently, combined with the thermal expansion coefficient (1×10 -6 / K to 5×10 -6 / K), the linear expansion deformation amount corresponding to each temperature point is calculated by using the thermal expansion theory, with the unit being microns (μm). By integrating the thermal expansion deformation of each point, the overall thermal expansion deformation field is formed, and the three-dimensional thermal expansion deformation data is output, with the spatial accuracy being 0.01 mm.

[0127] Step S42: performing warping deformation aggravation analysis on the lens warping data according to the thermal expansion deformation data to obtain warping deformation aggravation data;

[0128] In this embodiment, the thermal expansion deformation data is superimposed with the lens warping data obtained in the previous step. Using numerical superposition method, the two sets of deformation fields are accumulated point by point in the same spatial coordinate system, with units of microns (pm) and consistent spatial resolution of 0.01 mm. The high-precision surface fitting algorithm is used to calculate the curvature of the superimposed deformation data, and the warping radius change is output. The warping intensification is defined as the absolute value of the reduction of the warping radius before and after heating, with units of m -1 . The warping intensification threshold is set to 0.0005 m -1 , and the area exceeding the threshold is marked as the warping deformation intensification area. Through the three-dimensional visualization software, the warping intensification area is displayed in the form of a color map, and the intensification amplitude distribution matrix is output, with a resolution of 0.01 mm.

[0129] Step S43: Based on the warping deformation intensification data, material stress relaxation evolution is carried out to obtain material stress relaxation data;

[0130] In this embodiment, the warping deformation intensification data is used as the load input, combined with the elastic modulus (70 GPa to 75 GPa), Poisson's ratio (0.22 to 0.25) and creep relaxation modulus parameters of the lens material, and the finite element mechanics analysis tool is used for stress relaxation simulation. The stress relaxation process simulation adopts a multi-step time integration method with a time step of 1 hour, and the total simulation time is set to 168 hours (7 days), and the temperature environment is kept constant at room temperature 25°C. The stress relaxation change with time is calculated by the material constitutive model (using Maxwell model or standard linear solid model), with units of MPa. The stress relaxation distribution data is output, with a two-dimensional matrix data structure, a spatial resolution of 0.01 mm and a time resolution of 1 hour.

[0131] Especially important is that step S43 includes the following steps:

[0132] Step S431: Based on the warping deformation intensification data, molecular chain rearrangement detection is carried out to obtain molecular chain rearrangement data;

[0133] In this embodiment, when analyzing the warping deformation of the lens material, the coupling region of the thermal stress field and the displacement field is extracted as the analysis region, the molecular dynamics simulation software LAMMPS is used to construct the molecular scale simulation structure of the region, the structure should contain not less than 100,000 polymer chain segments, and the stress tensor change information from the warping deformation aggravation data is input in the simulation initial condition. Then, the boundary condition is set as a constrained layered structure and an equivalent shear stress of 0-30 MPa is loaded, the loading duration is 10 nanoseconds, and the temperature is controlled at 298 K using the NVT ensemble. The particle displacement trajectory data is output by simulation, the number of chain segment rearrangement events (defined as the behavior of the end displacement exceeding 1 nm and the chain vector direction mutation) per unit time is extracted, and the spatial distribution position is recorded, so as to obtain the molecular chain rearrangement data. The output format is a rearrangement event distribution diagram in the form of a three-dimensional point cloud, each point contains chain number, starting coordinates, ending coordinates, sliding direction vector and other information.

[0134] Step S432: Calculate the rearrangement density according to the molecular chain rearrangement data to obtain the molecular chain rearrangement density data;

[0135] In this embodiment, on the basis of the obtained three-dimensional point cloud data, the entire simulation region is voxelized by 5 nm 3 as a unit. The number of rearrangements of chain segments in each voxel is counted, and the chain segment rearrangement frequency per unit volume is obtained by dividing the volume of the voxel, which is defined as the rearrangement density, with the unit of events / nm 3 . A density field mapping program written in Python is used to perform three-dimensional visualization processing on the density value, and the output is a rearrangement density three-dimensional field distribution data file (.vti format). At the same time, the density threshold is set to 1x10-3 even ts / nm 3 , which is used as a criterion for determining whether the chain segment motion is active, and the threshold is determined with reference to the average statistical value of the thermal activation slip events of the polymer material.

[0136] Step S433: According to the molecular chain rearrangement density data, the molecular chain high-frequency rearrangement region is obtained;

[0137] In this embodiment, the isosurface extraction algorithm (such as the Marching Cubes algorithm) is used to extract the rearrangement density region higher than the threshold value from the rearrangement density three-dimensional field data, and form the three-dimensional boundary of the functional region. The aggregated region continuously existing above the threshold value is defined as the molecular chain high-frequency rearrangement region, and the volume, center coordinates and density average value are recorded. The identified region needs to meet the condition that the minimum continuous volume is greater than 100 nm 3 to exclude noise disturbance misjudgment. The result is output in the form of volume region number, and is linked with the original molecular trajectory data to realize dynamic observation.

[0138] Step S434: Material stress relaxation evolution based on the high-frequency rearrangement region of the molecular chain, to obtain material stress relaxation data.

[0139] In this embodiment, representative voxel units in the above high-frequency rearrangement region are selected, and the equivalent thermal force coupling field is continuously loaded in the molecular dynamics software, and the stress response change of the chain segment in the continuous loading and unloading process is monitored. The average chain segment stress data of each voxel at different loading time points (such as 0 ns, 2 ns, 4 ns, 6 ns, 8 ns, 10 ns) is curve-fitted to obtain a typical stress-time relaxation curve. The stress relaxation process is fitted by using the Fung viscoelastic model, and the viscoelastic constants (such as elastic modulus E0, relaxation time τ, viscous parameter η, etc.) are extracted. Finally, the stress relaxation parameter set corresponding to each high-frequency rearrangement region is output, that is, the material stress relaxation data, and the output format is a structured table (.csv), and the fields include voxel number, relaxation time constant, maximum stress attenuation amplitude, fitting residual, etc. All data are archived in the material database for subsequent microscopic failure analysis and lens stability evaluation.

[0140] Step S44: Microcrack detection according to the material stress relaxation data, to obtain microcrack data;

[0141] In this embodiment, the stress relaxation data is used to set the microcrack inducing stress threshold to 30 MPa. Through the threshold filtering algorithm, the region with stress exceeding the threshold is identified as the potential microcrack occurrence area. Combined with high-resolution X-ray tomography (resolution 1 μm) and ultrasonic detection technology, the spatial distribution map of the microcracks inside the lens is obtained. Three-dimensional crack volume reconstruction is performed by using image processing software to measure the microcrack length (ranging from 5 μm to 200 μm), width (1 μm to 20 μm) and crack density. The microcrack data is output in the form of three-dimensional coordinate point cloud and crack parameter table, which is convenient for subsequent defect positioning and tracking.

[0142] Especially important is that step S44 includes the following steps:

[0143] Step S441: Molecular chain sliding detection according to the material stress relaxation data, to obtain molecular chain sliding data;

[0144] In this embodiment, the material stress relaxation data is input into a high-precision material mechanics response simulation system. The system calculates the micro-molecular chain motion response of the material under different stress levels based on the input stress-time relaxation curve and the material constitutive relation model. Subsequently, a high-resolution scanning probe microscope (SPM) is used to apply a quantitative nanoscale shear force to a selected micro area on the material surface. The shear force range is controlled within 0.1 to 10 nanonewtons to ensure that the sliding behavior of the molecular chain can be triggered without damaging the material structure. The displacement sensor of the SPM records the displacement change of the molecular chain after being stressed in real time, with a measurement resolution of 0.1 nanometers and a data acquisition frequency of not less than 10 kHz to capture the rapid sliding process. The collected displacement data covers an area of not less than 100x100 microns, ensuring a comprehensive reflection of the spatial distribution of molecular chain sliding. Subsequently, vector field analysis is performed on each point displacement vector in the scanned area based on the material mechanics simulation results, and the accurate values of the sliding direction and sliding amount are extracted. All sliding vector data are spatially registered through a unified coordinate system to form a complete two-dimensional molecular chain sliding vector field. The data format uses matrix storage, and each matrix element includes two-dimensional coordinates and corresponding sliding vector information. Finally, this data set is used as the basis input for subsequent friction coefficient calculation and interface shear strength evaluation, ensuring high precision and continuity throughout the analysis process. 2

[0145] Step S442: Calculate the interface friction coefficient based on the molecular chain sliding data;

[0146] In this embodiment, the sliding displacement vector is combined with the corresponding shear force data to obtain the local interface friction coefficient by calculating the ratio of shear force to normal force based on tribology theory. During the calculation process, the shear force value is collected through a micro-mechanical loading sensor, and the normal force depends on the pressure on the material structure, with a numerical range of 0.01 to 1.0 newtons. The sliding speed is used as an auxiliary parameter, with a range limited to 0.1 to 10 microns / second, considering the influence of sliding speed on friction coefficient. Data filtering and denoising techniques are used to ensure the accuracy and stability of the friction coefficient data, and finally a two-dimensional interface friction coefficient distribution map is generated with a resolution of 1 micron.

[0147] Step S443: Evaluate the material interface shear strength based on the interface friction coefficient to obtain material interface shear strength data;

[0148] ​In this embodiment, according to the physical relationship between the friction coefficient and the shear strength, combined with the elastic modulus and plastic deformation parameters of the interface material, the finite element analysis method is used to simulate the stress state of the interface. The input parameters include the friction coefficient distribution, the interface thickness (usually 10 to 50 microns), the material elastic modulus (10 to 100 GPa), and the interface yield stress threshold (20 to 100 MPa). By simulating the stress distribution and failure mode of the interface under the action of shear load, the local shear strength value is quantitatively obtained. The calculation results are expressed in two-dimensional shear strength field, and the data format is that each micron grid point corresponds to the shear strength value (unit: MPa).

[0149] Step S444: Identify the weak area of the lens interface according to the material interface shear strength data;

[0150] In this embodiment, the interface shear strength threshold is set to 5 MPa, and the continuous area below the threshold in the shear strength distribution map is scanned. Threshold segmentation and connected component analysis are performed using image processing algorithms to determine the specific boundary and spatial range of the weak area. The boundary recognition error is controlled within ±0.5 microns. The weak area size needs to be greater than 100 square microns to be recognized as an effective weak area, avoiding local noise interference. The area information is output in the form of coordinate data and area data for subsequent defect analysis.

[0151] Step S445: Perform micro-crack detection according to the weak area of the lens interface to obtain micro-crack data.

[0152] In this embodiment, a high-resolution scanning electron microscope (SEM) combined with digital image correlation technology (DIC) is used to finely scan the surface of the weak area with a resolution of 10 nanometers. By comparing the image changes before and after stress loading, the starting point and propagation path of the micro-crack are identified. Combined with ultrasonic defect detection technology, the internal structure of the weak area is scanned at a frequency of 40 MHz to locate the internal micro-crack position and length, with a detection sensitivity of 1 micron. The micro-crack data includes crack length, width, spatial coordinates, and crack opening direction. All data are archived in a three-dimensional coordinate system for subsequent simulation analysis.

[0153] Step S45: Generate an optical lens defect report according to the aberration simulation data to perform manufacturing process improvement tasks.

[0154] In this embodiment, the aberration simulation data, warping aggravation data, stress relaxation data and micro-crack detection data obtained in the foregoing steps are converted into a unified format, ensuring that all data adopt a unified spatial coordinate system and time coordinate system, the data units remain consistent, and the spatial resolution is controlled within 0.01 mm. A multi-parameter data fusion algorithm is used to comprehensively analyze the data in the spatial and time dimensions, and the fusion algorithm includes weighted average, principal component analysis (PCA) and multi-dimensional scaling analysis (MDS) to eliminate the redundancy and noise between the data and improve the accuracy and representativeness of the fused data. Then, the spatial distribution maps of various defects are generated according to the fused data, and the defect regions are presented in the form of color gradient and contour line by using three-dimensional visualization software, and the color depth represents the severity of the defects, and the spatial range is accurate to the micron level. Statistical analysis is performed on the defects, and parameters including crack density, warping radius change, stress peak value and relaxation rate are calculated, and the statistical results are listed in the form of a table, and the parameter units include MPa, μm and dimensionless ratio. The defect severity score adopts a hierarchical scoring system, and the scoring standard is based on the international optical standard ISO10110 and the quality management specification formulated by the enterprise, and the scoring levels are divided into excellent, qualified, warning and unqualified four grades, and each grade corresponds to a specific numerical interval and judgment condition. The report further includes the influence evaluation of the defects on the optical performance, and by comparing the focal length change (unit: μm) during simulation, the imaging point spread function (PSF) radius change and the imaging clarity reduction percentage, a performance index comparison table is formed to reflect the specific influence of different defects on the imaging quality. The finally generated defect report is saved in PDF format, and the report content structure is clear, including directory, abstract, data analysis process, chart display, conclusion and suggestion. The three-dimensional defect visualization model file (in STL or OBJ format) is attached, which is convenient for interactive viewing of the spatial distribution and morphology of the defects, and an Excel format detailed parameter data table is provided, which is convenient for the manufacturing process department to carry out targeted improvement and optimization based on the data, to ensure the complete transmission of information and the convenience of use.

[0155] Preferably, the present specification also provides an optical lens simulation analysis system for performing the optical lens simulation analysis method as described above, the optical lens simulation analysis system comprising:

[0156] an optical lens model construction module, configured to acquire an optical lens structure drawing; determine an assembly relationship according to the optical lens structure drawing to obtain assembly relationship data; and construct an optical lens model according to the assembly relationship data;

[0157] a photosensitive chip eccentricity analysis module, configured to perform light ray tracing simulation according to the optical lens model to obtain light ray tracing data; perform imaging offset analysis based on the light ray tracing data to obtain imaging offset data; and perform photosensitive chip eccentricity analysis according to the imaging offset data to obtain photosensitive chip eccentricity data;

[0158] The lens warping detection module is configured to perform lens thermal stability detection according to the photosensitive chip eccentricity data, to obtain lens thermal stability data; perform Strehl ratio analysis based on the light ray tracing data, to obtain a Strehl ratio value; perform lens warping detection according to the Strehl ratio value, to obtain lens warping data;

[0159] The optical lens process improvement module is configured to perform material stress relaxation analysis on the lens warping data according to the lens thermal stability data, to obtain material stress relaxation data; perform micro-crack detection according to the material stress relaxation data, to obtain micro-crack data; input the micro-crack data into an optical lens model, and perform aberration simulation calculation, to obtain aberration simulation data; generate an optical lens defect report according to the aberration simulation data, to perform manufacturing process improvement tasks.

[0160] Therefore, from any point of view, the embodiments should be regarded as exemplary and non-limiting, the scope of the application being defined by the appended claims and not by the above description, therefore all variations falling within the meaning and scope of the equivalent elements of the application file are intended to be included in the present application.

[0161] The above description is merely one specific implementation of the application, which enables a person skilled in the art to understand or implement the application. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the application. Therefore, the application will not be limited to these embodiments shown herein, but will conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A simulation analysis method for optical lenses, characterized in that, Includes the following steps: Step S1: Obtain the optical lens structure drawing; determine the assembly relationship based on the optical lens structure drawing and obtain the assembly relationship data; Construct an optical lens model based on assembly relationship data; Step S2: Perform ray tracing simulation based on the optical lens model to obtain ray tracing data; perform imaging offset analysis based on the ray tracing data to obtain imaging offset data; perform photosensitive chip eccentricity analysis based on the imaging offset data to obtain photosensitive chip eccentricity data. Step S3: Perform lens thermal stability detection based on the image sensor eccentricity data to obtain lens thermal stability data; perform Strell ratio analysis based on ray tracing data to obtain Strell ratio values; perform lens warpage detection based on Strell ratio values ​​to obtain lens warpage data; wherein, step S3 includes the following steps: Step S31: Input the image sensor eccentricity data into the lens thermal stability detection system; Step S32: Set the coefficient of thermal expansion of the lens material to 1×10⁻ 6 / K to 5×10⁻ 6 / K and thermal conductivity from 0.1 W / (m·K) to 2.0 W / (m·K); Step S33: Set the ambient temperature change to -20°C to 80°C, the temperature change rate to 1°C / min to 10°C / min, and the thermal stress threshold to 5MPa to 50MPa; Step S34: Run the lens thermal stability detection module and output lens thermal stability data; Step S35: Perform Strell ratio analysis based on ray tracing data to obtain the Strell ratio value; wherein, step S35 includes the following steps: Step S351: Extract wavefront error information based on ray tracing data; perform wavefront distortion analysis based on wavefront error information to obtain wavefront distortion data; Step S352: Calculate the point spread function based on the wavefront distortion data to obtain the point spread function data; Step S353: Draw a spot distribution map based on the point spread function data to obtain the actual spot distribution data; Step S354: Perform peak normalization processing on the preset ideal diffraction spot distribution data and the actual spot distribution data to obtain normalized intensity ratio data; Step S355: Calculate the Strell ratio value based on the normalized intensity ratio data; Step S36: Perform lens warpage detection based on the Strellby value to obtain lens warpage data; Step S4: Perform material stress relaxation analysis on lens warpage data based on lens thermal stability data to obtain material stress relaxation data; perform microcrack detection based on material stress relaxation data to obtain microcrack data; input the microcrack data into the optical lens model and perform aberration simulation calculation to obtain aberration simulation data; generate an optical lens defect report based on the aberration simulation data to perform manufacturing process improvement tasks; wherein, step S4 includes the following steps: Step S41: Draw a temperature distribution map based on the lens thermal stability data; calculate the material thermal expansion response based on the temperature distribution map to obtain thermal expansion deformation data; Step S42: Based on the thermal expansion deformation data, perform warping deformation aggravation analysis on the lens warping data to obtain warping deformation aggravation data; Step S43: Based on the data of increased warping deformation, perform material stress relaxation evolution to obtain material stress relaxation data; Step S44: Perform microcrack detection based on material stress relaxation data to obtain microcrack data; wherein, step S44 includes the following steps: Step S441: Perform molecular chain slip detection based on material stress relaxation data to obtain molecular chain slip data; Step S442: Calculate the interfacial friction coefficient based on molecular chain sliding data; Step S443: Evaluate the interfacial shear strength of the material based on the interfacial friction coefficient to obtain the interfacial shear strength data of the material; Step S444: Identify the weak areas of the lens interface based on the material interface shear strength data; Step S445: Perform microcrack detection on the weak areas of the lens interface to obtain microcrack data; Step S45: Generate an optical lens defect report based on aberration simulation data to perform manufacturing process improvement tasks.

2. The simulation analysis method for optical lenses according to claim 1, characterized in that, Step S1 includes the following steps: Step S11: Obtain the optical lens structure drawing; Step S12: Identify the component structure boundary based on the optical lens structure drawing to obtain the component structure boundary data; Step S13: Perform component constraint analysis based on component structure boundary data to obtain component constraint relationship data; Step S14: Derive the structural connection path based on the component constraint relationship data to obtain the structural connection path data; determine the structural assembly sequence based on the structural connection path data to obtain the structural assembly sequence data; Step S15: Model the assembly relationship based on the structural assembly sequence data to obtain the assembly relationship data; Step S16: Construct an optical lens model based on the assembly relationship data.

3. The simulation analysis method for optical lenses according to claim 1, characterized in that, Step S2, which involves ray tracing simulation based on the optical lens model, includes: Extract optical lens surface attribute information from the optical lens model; Calculate the refractive index of the lens based on the surface properties of the optical lens; Light propagation is simulated based on the refractive index of the lens to obtain light propagation trajectory data; Based on the light propagation trajectory data, the imaging point locations are identified, and the image plane imaging distribution data is obtained; Beam energy concentration analysis is performed based on image plane imaging distribution data to obtain energy focusing data; Calculate the focus offset based on energy focusing data; Ray tracing data is obtained by performing ray tracing analysis based on focus offset.

4. The simulation analysis method for optical lenses according to claim 1, characterized in that, Step S2, which involves imaging migration analysis based on ray tracing data, includes: The three-dimensional coordinates of the ray image plane are extracted from the ray tracing data to obtain the three-dimensional coordinate data of the ray image plane; The offset vector is calculated based on the three-dimensional coordinate data of the light image plane to obtain the image point offset vector data; The offset angle is calculated based on the image point offset vector data to obtain the offset angle data; Based on the offset angle data, the offset direction distribution is statistically analyzed to obtain the imaging offset direction data; The offset amplitude is calculated based on the imaging offset direction data to obtain the offset amplitude data; The degree of imaging focus shift is evaluated based on the offset amplitude data, and imaging offset data is obtained.

5. The simulation analysis method for optical lenses according to claim 1, characterized in that, Step S2, which involves performing sensor eccentricity analysis based on imaging offset data, includes: Based on the imaging offset data, the imaging offset time period is statistically analyzed to obtain the imaging offset time period data; Deformation detection of the encapsulation layer is performed based on imaging offset time period data to obtain encapsulation layer deformation data; The degree of misalignment of the photodiode array is determined based on the encapsulation layer deformation data, and the misalignment data of the photodiode array is obtained. Welding void detection is performed based on misalignment data from a photodiode array to obtain welding void data. Identify stress concentration areas on the chip substrate based on welding void data; Based on the stress concentration area of ​​the chip substrate, the eccentricity analysis of the photosensitive chip was carried out to obtain the eccentricity data of the photosensitive chip.

6. The simulation analysis method for optical lenses according to claim 1, characterized in that, Step S36 includes the following steps: Step S361: Identify the image degradation area based on the Strell ratio value; Step S362: Perform lateral compression detection of the lens based on the image degradation area to obtain lateral compression data of the lens; Step S363: Calculate the in-plane stress gradient based on the lens lateral compression data to obtain the in-plane stress gradient data; Step S364: Perform coating material peeling detection based on in-plane stress gradient data to obtain coating material peeling data; Step S365: Based on the coating material peeling data, perform misalignment detection on adjacent layers of the lens to obtain misalignment data on adjacent layers of the lens; Step S366: Evaluate the degree of lens warping based on the misalignment data of adjacent layers of the lens to obtain lens warping data.

7. The simulation analysis method for optical lenses according to claim 1, characterized in that, Step S4 includes the following steps: Step S41: Draw a temperature distribution map based on the lens thermal stability data; calculate the material thermal expansion response based on the temperature distribution map to obtain thermal expansion deformation data; Step S42: Based on the thermal expansion deformation data, perform warping deformation aggravation analysis on the lens warping data to obtain warping deformation aggravation data; Step S43: Based on the data of increased warping deformation, perform material stress relaxation evolution to obtain material stress relaxation data; Step S44: Perform microcrack detection based on material stress relaxation data to obtain microcrack data; Step S45: Generate an optical lens defect report based on aberration simulation data to perform manufacturing process improvement tasks.

8. A simulation analysis system for optical lenses, characterized in that, For performing the simulation analysis method for an optical lens as described in claim 1, the simulation analysis system for the optical lens includes: The optical lens model building module is used to obtain optical lens structural drawings; determine assembly relationships based on optical lens structural drawings to obtain assembly relationship data; and build an optical lens model based on assembly relationship data. The image sensor eccentricity analysis module is used to perform ray tracing simulation based on the optical lens model to obtain ray tracing data; perform imaging offset analysis based on the ray tracing data to obtain imaging offset data; and perform image sensor eccentricity analysis based on the imaging offset data to obtain image sensor eccentricity data. The lens warpage detection module is used to detect lens thermal stability based on the image sensor eccentricity data to obtain lens thermal stability data; to perform Strell ratio analysis based on ray tracing data to obtain Strell ratio values; and to detect lens warpage based on Strell ratio values ​​to obtain lens warpage data. The optical lens process improvement module is used to perform material stress relaxation analysis on lens warpage data based on lens thermal stability data to obtain material stress relaxation data; to perform microcrack detection based on material stress relaxation data to obtain microcrack data; to input the microcrack data into the optical lens model and perform aberration simulation calculation to obtain aberration simulation data; and to generate an optical lens defect report based on the aberration simulation data to perform manufacturing process improvement tasks.

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