Lens film layer optimization method, device and medium based on optical simulation

Abstract

The application discloses a lens film layer optimization method and device based on optical simulation, and a medium, comprising: acquiring the base physical parameters and environmental light parameters of a visual optical lens, and establishing a visual optical digital twin simulation model; in the visual optical digital twin simulation model, a candidate film layer sequence set is configured, the candidate film layer sequence set contains a multilayer film structure combined by different medium materials and thickness parameters; light ray tracing simulation is performed on the candidate film layer sequence set, and an optical transmission and reflection simulation data set containing transmission light intensity numerical values and reflection light intensity numerical values is acquired; based on preset target optical parameters, the comprehensive evaluation eigenvalues of the optical transmission and reflection simulation data set are calculated, and the candidate film layer sequence set is iteratively optimized based on this, so that a target film layer sequence is determined; the target film layer sequence is analyzed into single-layer material identification and thickness numerical values, a process control instruction is generated by combining a preset film plating machine process parameter library, and a physical output is executed.

Description

Technical Field

[0001] This invention relates to the field of optical lens simulation design and thin film coating technology, and in particular to a method, equipment and medium for optimizing lens film based on optical simulation. Background Technology

[0002] With the increasing market demand for high-end resin lenses and optical lenses, the industry's requirements for lens transmittance, reflective impurities, and visual comfort for human eyes are constantly rising. Multilayer dielectric films are the core means to improve the optical performance of lenses.

[0003] Current mainstream lens coating optimization methods fall into two categories: manual physical trial coating and traditional computer-aided fixed-step simulation exhaustive search. Manual trial coating relies on the experience of R&D personnel to manually set the combination of coating materials and thicknesses, requiring multiple vapor deposition samples followed by actual spectral colorimetry measurements. This method suffers from drawbacks such as high material consumption, long development cycles, high dependence on personal experience in optimization results, and inability to quantify and match human visual perception. While existing computer optical simulation optimization schemes can reduce the number of physical samples, they still have many insurmountable technical shortcomings. Traditional simulations only build a single vertically incident light source model and do not simulate the multi-field macular visual sensitivity characteristics of the human eye, resulting in simulation-obtained reflectance colorimetry that differs from the actual colorimetry. The actual visual effect of wearing lenses deviates significantly. The optimization algorithm uses a fixed thickness step size throughout the process. If the step size is too large, the optimal coating parameters will be missed; if the step size is too small, the simulation calculation workload will increase exponentially, making it impossible to balance optimization accuracy and computational efficiency. At the same time, the existing evaluation system only relies on transmittance or reflectance as the evaluation standard, lacking a comprehensive quantitative evaluation mechanism that weights three-dimensional indicators of transmittance, reflectance, and human eye reflectance chromaticity. Optimized lenses are prone to problems such as reflective glare and obvious color variations. Furthermore, the simulation optimization module and the coating machine are independent of each other, and the optimal coating parameters need to be manually converted into equipment process instructions, which is prone to parameter errors and makes it difficult to achieve automated integration from design to production. The existing technology as a whole has the following shortcomings: low matching degree between simulation scenarios and real human eye observation scenarios, inability to balance optimization efficiency and accuracy, single dimension of optical performance evaluation, and disconnect between simulation and coating production links. It cannot meet the current demand for high-precision, high-efficiency, and automated coating optimization production of high-end optometry lenses.

[0004] Therefore, how to solve the shortcomings of existing simulation scenarios with low matching degree with real human eye observation scenarios, inability to balance optimization efficiency and accuracy, single dimension of optical performance evaluation, and disconnect between simulation and coating production links, which cannot meet the current demand for high-precision, high-efficiency, and automated coating optimization production of high-end optometry lenses, is an urgent technical problem to be solved in this field. Summary of the Invention

[0005] The purpose of this invention is to address the shortcomings of existing simulation scenarios, such as low matching degree between simulation scenarios and real human observation scenarios, inability to balance optimization efficiency and accuracy, single dimension of optical performance evaluation, and disconnect between simulation and coating production links. These shortcomings make it impossible to meet the current demand for high-precision, high-efficiency, and automated coating optimization production of high-end optometry lenses.

[0006] To solve the above-mentioned technical problems, the present invention provides the following technical solution:

[0007] A lens coating optimization method based on optical simulation includes:

[0008] S1: Obtain the substrate physical parameters and ambient lighting parameters of the optometry lens, and establish an optometry digital twin simulation model;

[0009] S2: In the optical digital twin simulation model, a candidate film layer sequence set is configured, which includes a multilayer thin film structure composed of different media materials and thickness parameters;

[0010] S3: Perform ray tracing simulation on the candidate film sequence set to obtain an optical transmission and reflection simulation dataset containing transmitted light intensity values ​​and reflected light intensity values;

[0011] S4: Based on the preset target optical parameters, calculate the comprehensive evaluation feature value of the optical transmission and reflection simulation dataset, and iteratively optimize the candidate film sequence set accordingly to determine the target film sequence;

[0012] S5: The target film sequence is parsed into single-layer material identifiers and thickness values, and process control instructions are generated and executed by combining them with the preset coating machine process parameter library.

[0013] Preferably, the step of acquiring the substrate physical parameters and ambient lighting parameters of the optometry lens and establishing an optometry digital twin simulation model includes:

[0014] The surface curvature data, substrate center refractive index, and geometric center thickness of the optometry lens are collected as the physical parameters of the substrate.

[0015] Data including discrete wavelength sets, corresponding spectral power distributions, and environmental incident angle space vectors are collected and used as the ambient illumination parameters.

[0016] In a three-dimensional coordinate system, a spatial mesh is generated based on the surface curvature data to construct a lens geometric topology model containing multiple micro-facet units, and the refractive index data of the substrate center is associated and stored in the corresponding micro-facet unit attributes.

[0017] A virtual light source emitting plane is constructed on the light incident side of the lens geometric topology model, and a virtual retinal receiving surface is constructed on the light transmission side. These are then integrated to generate the opto-optical digital twin simulation model.

[0018] Preferably, configuring a candidate film layer sequence set in the opto-optical digital twin simulation model includes:

[0019] Read the preset coating material property database and extract the refractive index data and extinction coefficient data of the candidate materials;

[0020] Set a threshold for the total number of film layers to be optimized, and set an initial thickness search range and thickness deviation for each layer to generate a candidate set of single-layer thicknesses for each layer;

[0021] The parameters of the extracted candidate materials are combined with the parameters in the single-layer thickness candidate set by Cartesian product hierarchical combination to generate the candidate film layer sequence set containing multiple sets of physical stacking orders;

[0022] The candidate film sequence sets are respectively associated and stored in the additional attribute records of each micro-facet unit in the lens geometric topology model.

[0023] Preferably, the step of performing ray tracing simulation on the candidate film layer sequence set to obtain an optical transmission and reflection simulation dataset containing transmitted light intensity values ​​and reflected light intensity values ​​includes:

[0024] Multiple discrete incident rays carrying initial light intensity and initial phase values ​​are generated on the virtual light source emission plane;

[0025] Calculate the spatial intersection point between the discrete incident ray and the micro-faceted unit, and at the interface of the film layer, combine the refractive index data of the media on both sides, the local normal vector and the incident ray vector to calculate the refraction direction vector, the reflection direction vector, and the reflection amplitude and transmission amplitude at the interface.

[0026] By combining the current film thickness, refractive index data and refraction direction vector, the optical path difference of light propagating inside the film is calculated, and the phase offset is obtained by mapping.

[0027] Based on the calculated reflection amplitude, transmission amplitude, and phase shift, the intensity and phase values ​​of the split reflected and refracted ray branches are updated.

[0028] Preferably, obtaining the optical transmission and reflection simulation dataset, which includes transmitted light intensity values ​​and reflected light intensity values, further includes:

[0029] During the tracking simulation, when the light intensity attenuation of the light branch is lower than the preset minimum light intensity threshold, or when the number of interface reflections reaches the maximum number threshold, the propagation calculation of the light branch is terminated.

[0030] The final set of transmitted light rays is collected at the virtual retina receiving surface, and the final set of reflected light rays is collected in the half-space on one side of the virtual light source emitting plane.

[0031] At each spatial receiving point, scalar calculations of the interference superposition of light rays of the same wavelength are performed to obtain the values ​​of interference transmitted light intensity and interference reflected light intensity at the corresponding discrete wavelengths and incident angles, which are then aggregated into the optical transmission and reflection simulation dataset.

[0032] Preferably, calculating the comprehensive evaluation feature value of the optical transmission and reflection simulation dataset based on preset target optical parameters includes:

[0033] The interference transmitted light intensity values ​​and the interference reflected light intensity values ​​in the optical transmission and reflection simulation dataset are respectively converted into simulated transmittance sequences and simulated reflectance sequences corresponding to the current candidate film layer sequences;

[0034] By comparing the discrete scalar differences between the simulated transmittance sequence, the simulated reflectance sequence and the preset target transmittance sequence and target reflectance sequence at the corresponding wavelength positions, the transmittance deviation sequence and the reflectance deviation sequence are obtained.

[0035] The simulated reflectance sequence is input into a preset color matching function mapping table for colorimetric projection mapping to obtain simulated reflectance chromaticity coordinate pairs, and the spatial geometric distance between the simulated reflectance chromaticity coordinate pairs and the preset target reflectance chromaticity coordinate pairs is calculated.

[0036] By combining preset weighted evaluation factors for each band, the transmittance deviation sequence, the reflectance deviation sequence, and the spatial geometric distance are weighted and summed to generate the comprehensive evaluation feature value that characterizes the degree of deviation of the overall optical performance of the current candidate film sequence.

[0037] Preferably, the step of iteratively optimizing the candidate membrane sequence set to determine the target membrane sequence includes:

[0038] The current candidate membrane sequence set is sorted in ascending order based on the comprehensive evaluation feature value, and the sequences with a ranking higher than a preset threshold are extracted as the seed sequence set for the current iteration.

[0039] Obtain the thickness step size from the previous iteration, and generate the refined search step size for the current round using a preset attenuation ratio coefficient;

[0040] Based on the current thickness values ​​of each thin film in the seed sequence set, discrete sampling is performed within the range of the refined search step size to generate a local neighborhood combination set as a new round of candidate film sequence set;

[0041] Repeat the ray tracing simulation and comprehensive evaluation feature value calculation steps for the new round of candidate film sequence sets;

[0042] The difference between the minimum comprehensive evaluation feature values ​​output by adjacent iterations is compared. When the difference is less than the preset convergence threshold or the iteration reaches the set maximum limit, the iteration screening ends and the sequence ranked first is determined as the target membrane sequence.

[0043] Preferably, the step of parsing the target film sequence into single-layer material identifiers and thickness values, generating process control instructions based on a preset coating machine process parameter library, and executing physical output includes:

[0044] The deposition source location code and basic film formation rate constant corresponding to the single-layer material identifier are retrieved from the preset coating machine process parameter library;

[0045] Divide the obtained single-layer thickness value by the corresponding basic film formation rate constant to calculate the single-layer evaporation residence time required to achieve the design thickness.

[0046] Extract the preset mechanical action delay time of the baffle in the coating equipment, add it to the single-layer vapor deposition dwell time, and obtain the total actual control time of a single layer;

[0047] According to the underlying communication protocol format of the external physical coating equipment, the total actual control time of the single layer, the deposition source location code and the process environment control data are encapsulated in protocol frames to generate the process control instructions;

[0048] The process control commands are sent to the external physical coating equipment and executed via the device communication interface.

[0049] A computer device includes a memory and a processor, the memory storing a computer program, the processor executing the computer program to implement the steps of a lens coating optimization method based on optical simulation.

[0050] A computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of a lens coating optimization method based on optical simulation.

[0051] The beneficial effects of this invention are as follows: First, the lens coating optimization method and equipment based on optical simulation provided by this invention rely on the opto-optical digital twin simulation model to complete the discrete mesh modeling of the lens freeform surface, multi-band ray tracing and thin film admittance matrix interferometry solution. It constructs a comprehensive evaluation functional by jointly considering multiple objectives such as transmission spectral error, CIELAB chromatic aberration and coating thickness, and uses an adaptive step-size iterative optimization mechanism to screen the globally optimal coating sequence. Unlike traditional fixed coating systems and single-index optimization design schemes, this invention can accurately take into account multiple optical requirements such as lens light transmission performance, color distortion suppression, and spectral blocking in specific bands, significantly shortening the number of coating iteration convergence rounds, effectively improving the lens optical imaging quality and spectral control accuracy, and reducing the lens coating rework rate.

[0052] Secondly, the lens coating optimization method and equipment based on optical simulation provided by this invention uses GPU to perform parallel batch ray tracing simulations across the entire visible light band. At the same time, it performs adaptive triangulation and mesh discretization on the collected lens surface data and adjusts the mesh density accuracy according to the optical field of view partition of the lens. Combined with the Sellmeier dispersion equation, it accurately characterizes the broadband refractive index variation law of the coating medium, and fully restores the real physical process of light wave interference under multi-angle oblique incidence and multi-layer thin film on curved lenses. The simulation results have a high degree of matching with the spectrum of the actual coating product, eliminating the need for multiple physical prototyping trials and errors, and significantly reducing material loss and R&D cycle in the optical film system design stage.

[0053] Third, the lens coating optimization method and equipment based on optical simulation provided by this invention can automatically parse the target coating sequence obtained by iterative optimization into a PLC process control data package that the coating machine can recognize, calculate the evaporation time of each dielectric layer, compensate for the delay of the mechanical baffle, and directly send it to the physical coating machine to complete the automated processing; at the same time, it introduces the closed-loop logic of the measured spectrum feedback correction digital twin model to continuously correct the substrate optical parameters and surface shape errors, and open up the full-link automated process of "simulation design-algorithm optimization-physical evaporation-measurement correction", realize the seamless connection from optical design to industrial coating, and improve the process consistency of large-scale lens production.

[0054] Fourth, the lens coating optimization equipment based on optical simulation provided by this invention is divided into independent functional hardware modules such as digital twin modeling, parallel ray tracing, iterative optimization, process output, and communication interaction. Each module collaborates through a high-speed PCIE bus. It is equipped with a material dispersion database, industrial communication interface, and encrypted time-series log storage mechanism. It supports general simulation optimization of multiple types of optical lenses and various coating targets, and can also encrypt and store core process coating parameters, taking into account the equipment's versatility, production and maintenance convenience, and the confidentiality and security of the enterprise's core process data. Attached Figure Description

[0055] Figure 1A schematic diagram of a lens coating optimization method based on optical simulation provided by the present invention;

[0056] Figure 2 This is a flowchart illustrating the process of establishing an opto-optical digital twin simulation model in a lens coating optimization method based on optical simulation provided by the present invention. Detailed Implementation

[0057] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments.

[0058] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to specific embodiments. It should be understood that the specific embodiments described herein are for illustrative purposes only and are not intended to limit the invention.

[0059] like Figure 1 As shown, this embodiment provides a lens coating optimization method based on optical simulation. This method is executed by a computer device or cloud server equipped with a high-performance parallel GPU processing unit. This method starts from the underlying laws of optical physics and colorimetry theory, and provides support for each step in the claims through a rigorous mathematical model. Specifically, the method includes the following core technical steps:

[0060] S1: Obtain the substrate physical parameters and ambient lighting parameters of the optometry lens, and establish an optometry digital twin simulation model;

[0061] Reference Figure 2 The establishment of a digital twin simulation model is the process of transforming the physical entity of the lens into a discrete mathematical model, providing geometric and physical boundary conditions for subsequent ray tracing.

[0062] 1. Surface curvature acquisition and adaptive triangular topology mesh modeling

[0063] The system first acquires continuous surface shape data of the front and rear surfaces of the optical lens. In precision optics, freeform or aspherical surfaces are typically characterized by orthogonal expansion using Zernike polynomials. (Using polar coordinates...) Indicates the sagitta of the lens surface This can be expanded as follows:

[0064]

[0065] in, Zernike coefficient, It is a set of continuous basis polynomials orthogonal on the unit circle, where K is the order of expansion, preferably 8 to 15 in engineering.

[0066] To perform ray intersection calculations in the computer, the system employs an adaptive Delaunay triangulation algorithm to perform the aforementioned continuous... The curved space is discretized into a geometric topological mesh model containing a large number of micro-facet units; the mesh density distinction rules are as follows: the maximum side length of the triangular unit in the effective field of view of the lens is ≤0.5mm, and the maximum side length of the unit in the non-field of view area at the edge of the lens is ≤2mm; when the deviation of the average reflectance calculation of the entire lens before and after the densification is less than 0.1%, the mesh is determined to be converged and the subdivision is stopped.

[0067] For any micro-facet element in this mesh model Extract the three-dimensional coordinates of its three vertices. and By constructing edge vectors The system employs vector cross product to rigorously calculate the local geometric unit normal vector of the microfacet element. :

[0068]

[0069] Expanding it into scalar form in Cartesian coordinates, we get:

[0070]

[0071] The system will calculate the local unit normal vector. Together with the refractive index data of the substrate center in this region The data is associated with the micro-facet and stored in its attribute dictionary.

[0072] Furthermore, when constructing a complete three-dimensional geometric topology model of the lens, the absolute physical distance of light propagation inside the lens cannot be determined solely by the curvature data of the front and rear surfaces. Therefore, the system simultaneously acquires the geometric center thickness of the optical lens as the core benchmark for spatial modeling. In specific implementation, the acquired geometric center thickness is used as the Z-axis (optical axis) spatial translation vector. In the three-dimensional coordinate system, the origin of the front surface mesh and the origin of the rear surface mesh of the lens, after discrete mesh generation, are spatially aligned and anchored.

[0073] By combining the thickness at the geometric center with solid modeling, the true physical optical path boundary of light propagation within the substrate can be accurately defined. This step effectively avoids the calculation deviation of the refraction path of off-axis rays (especially large-angle oblique incident rays in the lens edge region) caused by the use of the "ideal thin lens approximation" in traditional computer simulations. The resulting 3D topological model is completely faithful to the real spatial structure of the physical lens, providing extremely rigorous geometric topological support for subsequent oblique incident multi-beam interference calculations and significantly improving the fidelity of digital twin simulations.

[0074] 2. Ambient lighting parameters and virtual array construction

[0075] The system imports the relative spectral power distribution function of specific light sources in the 400-700nm visible light band, based on the International Commission on Illumination (CIE) standard. A virtual light source emitting plane is arranged parallel to the front of the geometric topology model, with a fixed distance of 500mm between the plane and the front surface of the lens; a virtual receiving surface matching the curvature of the human eye retina (curvature radius 12mm) is constructed behind the lens, effectively covering ±30° oblique incident light rays, fully simulating the multi-angle incident conditions of daily vision, thus completing the construction of the complete opto-optical digital twin simulation model.

[0076] S2: Configure a candidate film layer sequence set in the aforementioned opto-optical digital twin simulation model.

[0077] Thin film optimization is a typical high-dimensional combinatorial optimization problem. The system needs to pre-generate a large-scale sample space of candidate film system combinations as the initial search space; all micro-facets of the entire lens share the same set of candidate film layer sequences. A unified iterative optimization of the globally optimal membrane system.

[0078] 1. Material dispersion and database matching

[0079] The system reads a preset database of coating material properties, and selectable target materials include optical media such as SiO2, TiO2, and MgF2. Within the broad spectral range of 400–700 nm, the refractive index of the material is not a constant. The system strictly employs the Sellmeier dispersion equation to describe the variation of the material's refractive index with wavelength.

[0080]

[0081] in, and The dispersion constant is fitted to an experiment targeting a specific physical target. Simultaneously, the extinction coefficient of the material is obtained. Together they constitute the complex refractive index of the medium. .

[0082] 2. The combination of walking distance and Descartes

[0083] The total number of membrane layers to be optimized is set to... The preferred floor height for the project is 3 to 12 stories. (This is the first...) Thin film setting physical thickness search range Initial thickness is far from the walking length Engineering specifications: ,initial Minimum thickness limit .

[0084] The system extracts material identifiers and the aforementioned thickness candidate set, performing hierarchical cross-combination. Each candidate membrane sequence structure can be represented by a mathematical set. The rigorous expression is:

[0085]

[0086] in, Representing the Candidate membrane sequences, For the first Complex refractive index of the layered medium, For the first The physical thickness of the layer medium. The system assigns this sequence set to the additional attribute fields of each microfacet unit.

[0087] S3: Perform ray tracing simulation on the candidate film sequence to obtain an optical transmission and reflection simulation dataset.

[0088] This invention employs a parallel computing architecture based on the coupling of three-dimensional ray tracing and optical admittance feature matrix. It uses GPUs to perform batch synchronous simulations of all discrete wavelengths from 400 to 700 nm, thus solving the problem of non-uniform incident angle caused by the curved surface of optical lenses.

[0089] 1. Calculation of ray intersection and deflection of reflection and refraction vectors

[0090] Let the ray parameter equation of the discrete incident ray be: ,in As the light source, This is a unit vector representing the direction of the light ray. To accurately track the energy attenuation and interference phenomena of light waves in multilayer media, when generating each discrete incident ray on the virtual light source emission plane, the system, in addition to assigning it geometric spatial coordinates and a direction vector, also initializes and binds it with a physical state vector containing initial light intensity and initial phase values. Specifically, the system assigns an initial light intensity to the ray based on the spectral power distribution function of the corresponding wavelength in the current ambient lighting parameters (e.g., setting it to an absolute energy value or a normalized scalar of 1.0), while strictly calibrating the initial phase value of the ray leaving the virtual light source plane to 0 (or the same reference phase at the same wavelength); at the intersection point, the incident direction of the ray is defined as... Given the local unit normal vector of the microfacet Angle of incidence It can be obtained through the inner product:

[0091]

[0092] Based on Snell's law in vector form, the direction vector of the refracted ray entering the first layer of medium for:

[0093]

[0094] Reflected ray direction vector for:

[0095]

[0096] By rigorously quantifying and assigning initial light intensity and initial phase values ​​to discrete light rays, an absolute physical calculation benchmark is established for the propagation of light waves in thin-film systems. The setting of the initial light intensity provides a basis for comparison when executing the energy attenuation truncation algorithm (terminating tracking when the intensity falls below a preset threshold), effectively avoiding the waste of computing power caused by invalid calculations. The unified setting of the initial phase ensures that at subsequent film layer interfaces, whether it is a reflected light ray branch or a refracted light ray branch, there is a traceable optical path difference record. This allows for the accurate calculation of the final amplitude of constructive interference (anti-reflection) or destructive interference (anti-reflection) of light waves, ensuring the physical correctness of the macroscopic spectral transmittance and reflectance solutions.

[0097] 2. Tangential continuity of electromagnetic field and solution of characteristic admittance matrix

[0098] The theory of thin-film optical characteristic admittance is introduced to handle complex optical wave interference between interfaces. For wavelengths of... The incident light wave is decomposed into s-polarization and p-polarization and calculated independently. The natural light is finally obtained by superimposing the average values ​​of the two polarization simulation results. For the first... For a layered medium, the effective optical admittance is defined as:

[0099]

[0100]

[0101] Light passes through the first Spatial phase delay accumulated during layered dielectric The calculation is as follows:

[0102]

[0103] Based on the continuity condition of the tangential components of the electric and magnetic fields at the interface in Maxwell's equations, the first... Feature transfer matrix of thin film Represented as:

[0104]

[0105] For a complete film system consisting of L thin films, its global characteristic matrix is... The product of the individual matrices:

[0106]

[0107] The admittance of the basis is obtained through this matrix. By mapping onto the outer surface, the equivalent optical input admittance of the entire film system can be obtained. :

[0108]

[0109] 3. Calculation of transmittance and reflection coefficients and superposition of light wave interference

[0110] Using equivalent optical input admittance Y and ambient dielectric admittance Calculate the complex amplitude reflection coefficient and complex amplitude transmission coefficient :

[0111]

[0112] The reflected light intensity and the transmitted light intensity are respectively:

[0113]

[0114] In the specific implementation of ray tracing simulation on the candidate film layer sequence set, since infinitely many multi-beam interference and reflection occur within the multilayer thin film, a forced truncation mechanism is introduced to balance computational power and simulation accuracy. Specifically, an intensity attenuation record marker and a recursion depth marker are set for each branch of the split reflected and refracted light rays.

[0115] On a physical level, light travels through a thickness of... , refractive index When the thin film is applied, the resulting phase shift Through formula Perform the mapping calculation. For wavelength, Let be the angle of refraction. After each energy split at the interface, the intensity attenuation is calculated. When the intensity attenuation of this ray branch is lower than a preset minimum intensity threshold (e.g., of the initial intensity), the intensity decreases. If the number of consecutive reflections at the micro-facet interface reaches a maximum threshold (e.g., 50 times), the system will determine that the remaining energy of that ray branch contributes to the total global interference intensity within the system's noise floor range, thereby immediately terminating the propagation calculation of that ray branch and releasing the memory stack. Finally, at the virtual retina receiving surface and the virtual light source emitting plane, scalar approximate interferometric superposition is performed on the complex amplitudes of all untrunculated ray branches of the same wavelength to obtain the values ​​of the transmitted and reflected light intensities.

[0116] The above calculations, performed on the virtual retina receiving surface and the half-space containing the virtual light source, apply a complex amplitude scalar approximation superposition for light waves of the same frequency, ultimately aggregating them into an optical transmission and reflection simulation dataset. .

[0117] S4: Calculate the comprehensive evaluation feature values ​​of the optical transmission and reflection simulation dataset, and perform iterative optimization to determine the target film sequence.

[0118] 1. Colorimetric projection mapping and spatial geometric distance calculation

[0119] Extracting the simulated reflectance sequence from the simulation dataset Introducing CIE standard color matching functions. Perform the Riemann sum-integral approximation on M discrete wavelength nodes to calculate the tristimulus values. , , :

[0120]

[0121]

[0122]

[0123] in The normalization constant is the wavelength relative to the walk length. The preferred wavelength for engineering is 5nm. The tristimulus values ​​are converted to the CIELAB color space using a standard piecewise conversion function. for:

[0124]

[0125] Obtain simulated reflection chromaticity coordinate pairs :

[0126]

[0127]

[0128] , , This is the standard white light tristimulus reference value. Its relationship with the preset target chromaticity coordinates is then calculated. The three-dimensional Euclidean distance between them (total chromatic difference) ):

[0129]

[0130] In the comprehensive evaluation of eigenvalue calculation, colorimetric projection mapping is crucial to ensuring the visual aesthetics of the lenses. The system combines a simulated reflectance sequence with the spectral power distribution of the light source, inputs it into a preset CIE 1931 standard color matching function, and obtains the tristimulus values ​​through discrete Riemann integral approximation calculation. , , Subsequently, it is nonlinearly converted into simulated reflectance chromaticity coordinate pairs in the CIELAB uniform color space. The three-dimensional geometric Euclidean distance between them, i.e., the color difference value The system employs a weighted summation algorithm to sum the spectral mean square error of transmittance and reflectance with the color difference value. Multiply by the respective band weighting factors (e.g., 0.7 for optical performance and 0.3 for chromaticity) to generate a comprehensive evaluation feature value. The smaller this value, the closer the current film sequence is to the ideal design goal in terms of optical performance and anti-reflective color distortion visual performance.

[0131] 2. Comprehensive evaluation of eigenvalue construction and adaptive step-size iteration

[0132] Calculate the mean square error of transmittance and reflectance (Taking reflectivity as an example):

[0133]

[0134] in Given the target's ideal reflectance spectrum, construct a target evaluation functional (comprehensive evaluation eigenvalues) characterizing the overall optical deviation. :

[0135]

[0136] Among them, the weight allocation factor engineering calibration optimization The system is based on The candidate sequences are sorted in ascending order of numerical values, and an adaptive step size reduction strategy is used for iterative optimization.

[0137] Specifically, the system is based on After sorting the current candidate membrane sequence set in ascending order of numerical values, the candidate sequences with a ranking higher than a preset threshold (e.g., the top 5%) are extracted as the seed sequence set for the current iteration. The refined search step size for the current iteration is generated using a decay ratio coefficient. Then, the system uses the current thickness values ​​of each thin film in the seed sequence set as a reference point, within the positive and negative ranges. Discrete sampling is performed within a refined search step size to generate a local neighborhood combination set containing various thickness fine-tuning combinations. This local neighborhood combination set will be used as a new candidate film layer sequence set and re-input into the ray tracing model to repeat the interferometric simulation and evaluation functional solution steps;

[0138] Let the thickness discrete search step size in the m-th round be... Then the first The wheel refinement step size is updated as follows:

[0139]

[0140] Attenuation ratio Values ​​range from 0.7 to 0.9; a double iteration termination constraint is added:

[0141] Convergence threshold constraint: Evaluation of functional differences between adjacent iterations Convergence threshold ;

[0142] Maximum number of iterations constraint: Set an upper limit for iterations If the sequence fails to converge after reaching the upper limit, the current optimal sequence is directly selected.

[0143] The system exits the iteration when any termination condition is met, and the current iteration is set to... The sequence with the smallest value is identified as the target membrane sequence.

[0144] The aforementioned mechanism of "seed sequence extraction" combined with "local neighborhood refinement sampling" achieves a perfect balance between "macro-global exploration" and "micro-local mining" within the vast solution space of membrane system parameters. Extracting high-ranking seed sequences ensures that the optimization direction always converges towards regions with better optical performance, avoiding blind exhaustive search. Meanwhile, local neighborhood recombination based on adaptive step size reduction acts like a "microscope," performing fine-tuning near excellent membrane system architectures. This effectively prevents the problem of traditional fixed large step size algorithms easily "skipping and missing" the true optimal physical thickness nodes. While ensuring that the final output membrane system parameters have extremely high optical accuracy, the computational convergence cycle is shortened by more than 50%.

[0145] S5: Parse the target film sequence into single-layer material identifiers and thickness values, generate process control commands, and execute physical outputs.

[0146] The target film sequence is resolved into discrete physical processing parameters. For the first... A thin film with a target design thickness of [thickness value missing]. The system retrieves the coating machine process parameter database to obtain the hard core reference film formation rate constant of the evaporation source corresponding to the material. The required residence time for this single layer of vapor deposition. Based on the linear depositional relationship of physical vapor deposition:

[0147]

[0148] Introducing the inherent time constant of the mechanical response delay of the light-shielding baffle in the coating machine (Engineering values ​​range from 0.5s to 2s), calculate the total actual control time for a single layer. :

[0149]

[0150] In the output stage of converting the digital model into physical processing instructions, after parsing the target film layer sequence, the system retrieves the deposition source location code (e.g., from the local or cloud-synchronized coating machine process parameter library) for each layer of material. The material corresponds to the No. 2 electron gun crucible) and the fundamental film formation rate constant, hard-calibrated under a specific vacuum degree (e.g., ).

[0151] When calculating the single-layer evaporation time, in addition to dividing the physical design thickness by the basic film formation rate constant, the system also extracts the inherent motion delay of the mechanical actuators of the physical equipment, namely the mechanical motion delay time of the baffle (typically ranging from 0.5 to 1.5 seconds). The theoretical evaporation time is added to the mechanical delay time to obtain the total actual control time for a single layer that the equipment's logic controller needs to control. Finally, based on the underlying industrial communication protocol (such as Modbus RTU or TCP / IP) used by the external physical coating equipment, the system assembles process environment control data, including deposition source location encoding, total actual control time, equipment ID, and control register read / write instructions, into a protocol data frame containing a frame header, opcode, data segment, and cyclic redundancy check (CRC). This process control instruction is directly sent to the coating equipment via RS-485 or industrial Ethernet communication interfaces, driving the electron gun, baffle, and other physical hardware to perform actions, realizing an automated technology closed loop from digital virtual simulation to physical processing.

[0152] The system encodes the physical evaporation source location and the total time consumed. Parameters such as process vacuum level are assembled into binary protocol data packets containing frame headers, opcodes, data segments, and check codes according to the underlying communication protocol format of programmable logic controllers (PLCs); and sent to external physical coating machines for processing via industrial communication bus.

[0153] Digital twin closed-loop correction supplement

[0154] After the coating machine completes the vapor deposition, the spectrometer measures the transmission and reflection curves of the finished lens and transmits the data back to the system. The system automatically updates the substrate refractive index and substrate surface shape Zernike coefficient in the digital twin model, completing the complete digital twin closed loop of "simulation optimization - physical coating - actual measurement correction".

[0155] Example 1: Complete Engineering Demonstration of a 1.67 High-Refractive Index Resin Blue Light Blocking Lens

[0156] 1. Basic parameter calibration

[0157] Substrate: 1.67 high-refractive-index resin substrate; Sellmeier dispersion coefficient was measured and fitted using a spectrophotometer.

[0158] Film parameters: Total number of layers L=5, candidate target materials SiO2, TiO2, MgF2;

[0159] General hyperparameters: , The maximum number of iterations is 100, and the wavelength step size is 5nm.

[0160] Target constraints: Average visible light transmittance ≥ 96%, total color difference Blue light band reflectivity ≥80%.

[0161] 2. Results of the full-process simulation

[0162] S1: Acquire the free-form surface shape of the lens, fit it with 10th-order Zernike, generate an optical mesh through adaptive triangulation, and build a virtual simulation space for the CIED65 light source.

[0163] S2: Generate a 5-layer film system combined sample space with a thickness range of 10~300nm and an initial step size of 5nm;

[0164] S3: The GPU performs parallel ray tracing and feature matrix interferometry calculations across the entire 400~700nm wavelength range, and outputs a transmittance and reflectance dataset;

[0165] S4: Calculate the mean square error of the spectrum and the CIELAB color difference. After 72 rounds of adaptive step-size iteration, the convergence threshold is met, and the optimal 5-layer film system is locked.

[0166] S5: Analyze the material and thickness of each layer, calculate the evaporation time and baffle delay, generate a PLC data packet and send it to the coating machine to complete the processing.

[0167] Comparison of effects (compared to traditional fixed 4-layer blue light blocking film system)

[0168] Average transmittance of visible light 96.3% 91.5% +4.8% Total color difference ΔE across the entire spectrum 1.21 4.73 -74.4% Simulation rounds required for iterative convergence 72 rounds 156 rounds -53.8% average reflectance in the blue light band 82.6% 70.1% +12.5%

[0169] Example 2: Lens Coating Optimization Equipment Based on Optical Simulation

[0170] This embodiment provides a lens coating optimization device based on optical simulation, used to execute the lens coating optimization methods described in S1 to S5. The device comprises six hardware logic units: a digital twin modeling module, a candidate coating configuration module, a parallel ray tracing simulation module, an iterative optimization evaluation module, a process instruction output module, and a communication interaction module. These modules are interconnected via a high-speed PCIe bus. The device is equipped with a multi-GPU parallel computing cluster and an industrial PLC communication interface. The specific hardware configuration and execution logic of each module are as follows:

[0171] Digital twin modeling module: includes surface shape acquisition sub-unit, mesh generation and calculation sub-unit, and lighting simulation construction sub-unit; the hardware is equipped with a high-precision optical surface shape detector data access interface, multi-channel parallel GPU stream processor, and memory cache array.

[0172] Execution logic: Receive measured surface shape data of the optometry lens substrate, fit the surface sagitta using Zernike polynomials; call the adaptive Delaunay triangulation core operator to complete surface discretization, calculate the micro-element normal vector and bind the substrate refractive index; load CIE standard light source spectral parameters, construct the virtual light source emission plane and the human eye retina receiving surface, and output the complete lens digital twin simulation model to shared memory.

[0173] Candidate film configuration module: includes a coating material database storage subunit and a combination traversal calculation subunit; hardware is equipped with a high-speed solid-state drive to store various target material Sellmeier dispersion coefficient and extinction coefficient databases, and a multi-threaded CPU calculation core.

[0174] Execution logic: Read the dispersive optical parameters of the preset coating medium, receive the total number of film layers, thickness range, and initial dispersion step size input by the user, perform Cartesian cross combination on the material identifier and thickness range, generate a set of all candidate film layer sequences, and bind the sequence set to the attribute fields of each micro-facet of the digital twin model.

[0175] Parallel ray tracing simulation module: includes multi-GPU parallel ray intersection sub-unit, thin film admittance matrix solving sub-unit, and polarization intensity superposition sub-unit; the hardware is equipped with multiple high-performance graphics processors and a large-capacity memory array, supporting batch parallel computation of 400~700nm visible light multi-wavelengths.

[0176] Execution logic: Read the digital twin model and candidate film layer sequence, and solve the intersection point of curved light rays and the reflection and refraction vectors based on the vector Snell's law; calculate the characteristic transmission matrix of the thin film layer by layer, and derive the equivalent optical admittance of the film system; solve the transmission and reflection complex amplitudes under s and p polarization respectively, and superimpose them to obtain the natural light transmission and reflection intensity dataset, which is cached to high-speed shared storage.

[0177] Iterative optimization evaluation module: includes a colorimetric integral calculation subunit, a multi-objective evaluation functional calculation subunit, and an adaptive step-size iterative control subunit; the hardware is equipped with a multi-core CPU arithmetic unit and a parameter threshold register.

[0178] Execution logic: Read the transmission and reflection simulation dataset, solve for the CIE tristimulus values ​​using Riemann integrals, convert to the CIELAB color space to calculate the total color difference ΔE; calculate the mean square error of the transmission and reflection spectra respectively, and construct a weighted comprehensive optical evaluation functional; sort the candidate films in ascending order according to the functional values, adaptively reduce the thickness search step size according to the attenuation coefficient, and monitor the dual termination conditions of the iteration convergence threshold and the maximum number of iterations, and output the globally optimal target film sequence.

[0179] Process instruction output module: includes film layer process analysis subunit, evaporation timing calculation subunit, and industrial protocol packaging subunit; hardware supporting real-time computing controller and industrial protocol encoding chip.

[0180] Execution logic: Deconstruct the single-layer material identifier and physical thickness of the target film sequence, retrieve the film formation rate constant of the coating machine, and calculate the total evaporation time for each layer by combining the mechanical baffle delay time constant; encapsulate parameters such as evaporation source code, evaporation time, and process vacuum degree into binary data packets with check codes according to the PLC industrial communication protocol.

[0181] Communication interaction module: includes a high-speed industrial bus communication subunit and a measured data feedback correction subunit; the hardware is equipped with an RS485 / Ethernet industrial communication interface and a spectrometer data receiving interface.

[0182] Execution logic: The process data package is sent to the external physical coating machine; after the coating is completed, the measured transmission and reflection spectra of the finished lens are received from the spectrometer, and the substrate refractive index and Zernike coefficient of the surface shape in the digital twin modeling module are automatically written back and updated to complete the full digital twin closed loop of simulation-processing-actual measurement correction.

[0183] The computing hardware of the device is an industrial server equipped with a multi-GPU parallel cluster. Simulation data is transmitted between modules via a PCIE 4.0 high-speed bus. The device has three types of expansion interfaces reserved externally: optical inspection equipment, coating PLC machine, and cloud storage. The device has an independent power supply module and computing power scheduling firmware built in, which ensures real-time parallel computing for large-scale ray tracing simulation and high-dimensional film layer combination iteration.

[0184] Example 3: Computer-readable storage medium

[0185] A computer-readable storage medium storing a computer program, which, when executed by a processor or a GPU parallel computing unit, implements a lens coating optimization method based on optical simulation for all steps S1 to S5 mentioned above.

[0186] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related computing hardware (such as a server entity including a CPU, GPU, memory, solid-state drive, and network interface card). The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the above method embodiments. Furthermore, any references to memory, storage, databases, or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory.

[0187] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A method for optimizing lens coatings based on optical simulation, characterized in that, include: S1: Obtain the substrate physical parameters and ambient lighting parameters of the optometry lens, and establish an optometry digital twin simulation model; S2: In the optical digital twin simulation model, a candidate film layer sequence set is configured, which includes a multilayer thin film structure composed of different media materials and thickness parameters; S3: Perform ray tracing simulation on the candidate film sequence set to obtain an optical transmission and reflection simulation dataset containing transmitted light intensity values ​​and reflected light intensity values; S4: Based on the preset target optical parameters, calculate the comprehensive evaluation feature value of the optical transmission and reflection simulation dataset, and iteratively optimize the candidate film sequence set accordingly to determine the target film sequence; S5: The target film sequence is parsed into single-layer material identifiers and thickness values, and process control instructions are generated and executed by combining them with the preset coating machine process parameter library.

2. The lens coating optimization method based on optical simulation according to claim 1, characterized in that, The process of acquiring the substrate physical parameters and ambient lighting parameters of the optometry lens and establishing an optometry digital twin simulation model includes: The surface curvature data, substrate center refractive index, and geometric center thickness of the optometry lens are collected as the physical parameters of the substrate. Data including discrete wavelength sets, corresponding spectral power distributions, and environmental incident angle space vectors are collected and used as the ambient illumination parameters. In a three-dimensional coordinate system, a spatial mesh is generated based on the surface curvature data to construct a lens geometric topology model containing multiple micro-facet units, and the refractive index data of the substrate center is associated and stored in the corresponding micro-facet unit attributes. A virtual light source emitting plane is constructed on the light incident side of the lens geometric topology model, and a virtual retinal receiving surface is constructed on the light transmission side. These are then integrated to generate the opto-optical digital twin simulation model.

3. The lens coating optimization method based on optical simulation according to claim 1, characterized in that, The configuration of a candidate film layer sequence set in the opto-optical digital twin simulation model includes: Read the preset coating material property database and extract the refractive index data and extinction coefficient data of the candidate materials; Set a threshold for the total number of film layers to be optimized, and set an initial thickness search range and thickness deviation for each layer to generate a candidate set of single-layer thicknesses for each layer; The parameters of the extracted candidate materials are combined with the parameters in the single-layer thickness candidate set by Cartesian product hierarchical combination to generate the candidate film layer sequence set containing multiple sets of physical stacking orders; The candidate film sequence sets are respectively associated and stored in the additional attribute records of each micro-facet unit in the lens geometric topology model.

4. The lens coating optimization method based on optical simulation according to claim 1, characterized in that, The step of performing ray tracing simulation on the candidate film sequence set to obtain an optical transmission and reflection simulation dataset containing transmitted light intensity values ​​and reflected light intensity values ​​includes: Multiple discrete incident rays carrying initial light intensity and initial phase values ​​are generated on the virtual light source emission plane; Calculate the spatial intersection point between the discrete incident ray and the micro-faceted unit, and at the interface of the film layer, combine the refractive index data of the media on both sides, the local normal vector and the incident ray vector to calculate the refraction direction vector, the reflection direction vector, and the reflection amplitude and transmission amplitude at the interface. By combining the current film thickness, refractive index data and refraction direction vector, the optical path difference of light propagating inside the film is calculated, and the phase offset is obtained by mapping. Based on the calculated reflection amplitude, transmission amplitude, and phase shift, the intensity and phase values ​​of the split reflected and refracted ray branches are updated.

5. The lens coating optimization method based on optical simulation according to claim 1, characterized in that, The process of obtaining an optical transmission and reflection simulation dataset containing transmitted light intensity values ​​and reflected light intensity values ​​also includes: During the tracking simulation, when the light intensity attenuation of the light branch is lower than the preset minimum light intensity threshold, or when the number of interface reflections reaches the maximum number threshold, the propagation calculation of the light branch is terminated. The final set of transmitted light rays is collected at the virtual retina receiving surface, and the final set of reflected light rays is collected in the half-space on one side of the virtual light source emitting plane. At each spatial receiving point, scalar calculations of the interference superposition of light rays of the same wavelength are performed to obtain the values ​​of interference transmitted light intensity and interference reflected light intensity at the corresponding discrete wavelengths and incident angles, which are then aggregated into the optical transmission and reflection simulation dataset.

6. The lens coating optimization method based on optical simulation according to claim 1, characterized in that, The calculation of the comprehensive evaluation feature value of the optical transmission and reflection simulation dataset based on preset target optical parameters includes: The interference transmitted light intensity values ​​and the interference reflected light intensity values ​​in the optical transmission and reflection simulation dataset are respectively converted into simulated transmittance sequences and simulated reflectance sequences corresponding to the current candidate film layer sequences; By comparing the discrete scalar differences between the simulated transmittance sequence, the simulated reflectance sequence and the preset target transmittance sequence and target reflectance sequence at the corresponding wavelength positions, the transmittance deviation sequence and the reflectance deviation sequence are obtained. The simulated reflectance sequence is input into a preset color matching function mapping table for colorimetric projection mapping to obtain simulated reflectance chromaticity coordinate pairs, and the spatial geometric distance between the simulated reflectance chromaticity coordinate pairs and the preset target reflectance chromaticity coordinate pairs is calculated. By combining preset weighted evaluation factors for each band, the transmittance deviation sequence, the reflectance deviation sequence, and the spatial geometric distance are weighted and summed to generate the comprehensive evaluation feature value that characterizes the degree of deviation of the overall optical performance of the current candidate film sequence.

7. The lens coating optimization method based on optical simulation according to claim 6, characterized in that, The step of iteratively optimizing the candidate membrane sequence set to determine the target membrane sequence includes: The current candidate membrane sequence set is sorted in ascending order based on the comprehensive evaluation feature value, and the sequences with a ranking higher than a preset threshold are extracted as the seed sequence set for the current iteration. Obtain the thickness step size from the previous iteration, and generate the refined search step size for the current round using a preset attenuation ratio coefficient; Based on the current thickness values ​​of each thin film in the seed sequence set, discrete sampling is performed within the range of the refined search step size to generate a local neighborhood combination set as a new round of candidate film sequence set; Repeat the ray tracing simulation and comprehensive evaluation feature value calculation steps for the new round of candidate film sequence sets; The difference between the minimum comprehensive evaluation feature values ​​output by adjacent iterations is compared. When the difference is less than the preset convergence threshold or the iteration reaches the set maximum limit, the iteration screening ends and the sequence ranked first is determined as the target membrane sequence.

8. The lens coating optimization method based on optical simulation according to claim 1, characterized in that, The process of parsing the target film sequence into single-layer material identifiers and thickness values, combining them with a preset coating machine process parameter library to generate process control instructions, and executing physical outputs includes: The deposition source location code and basic film formation rate constant corresponding to the single-layer material identifier are retrieved from the preset coating machine process parameter library; Divide the obtained single-layer thickness value by the corresponding basic film formation rate constant to calculate the single-layer evaporation residence time required to achieve the design thickness. Extract the preset mechanical action delay time of the baffle in the coating equipment, add it to the single-layer vapor deposition dwell time, and obtain the total actual control time of a single layer; According to the underlying communication protocol format of the external physical coating equipment, the total actual control time of the single layer, the deposition source location code and the process environment control data are encapsulated in protocol frames to generate the process control instructions; The process control commands are sent to the external physical coating equipment and executed via the device communication interface.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the lens coating optimization method based on optical simulation as described in any one of claims 1 to 8.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the lens coating optimization method based on optical simulation as described in any one of claims 1 to 8.

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