An optical processing system for an ultra-high contrast glass-based display screen

Abstract

This invention relates to the field of display technology, specifically to an optical processing system for an ultra-high contrast glass-based display. First, the parameters of the electrode network and optical control layer are obtained, and an optical defect distribution map is generated using a random forest algorithm. Then, the conductivity configuration is optimized using a support vector machine, and the anti-reflection layer parameters are designed and verified using a combination of the transfer matrix method and the finite difference time-domain method. A system model is constructed by integrating the optical control layer, and optical uniformity is evaluated using the Monte Carlo method. Finally, the processing scheme is refined through closed-loop adjustment, ultimately generating a set of structural parameters and interlayer stacking specifications suitable for mass production. This invention integrates AI algorithms and multiphysics simulation to achieve seamless integration of ultra-high contrast display and a pure glass appearance in non-working states. It is suitable for high-end application scenarios, has strong mass production feasibility, and possesses significant technological innovation and industrial application value.

Description

Technical Field

[0001] This invention relates to the field of display technology, and more specifically to an optical processing system for an ultra-high contrast glass-based display screen. Background Technology

[0002] With the rapid development of highly integrated applications such as smart buildings, automotive displays, and augmented reality, glass-based displays are increasingly important as a key direction for next-generation transparent display technology. Glass-based displays, due to their combination of high-definition image rendering capabilities and environmental integration characteristics when off, can achieve information visualization without compromising architectural aesthetics or visual transparency, making them a key carrier for modern transparent display systems. However, in practical applications, optical defects in non-operating glass-based displays often affect their visual consistency, potentially weakening overall aesthetics and reducing user acceptance of the transparent display effect, thus hindering their large-scale implementation in high-end commercial and residential fields.

[0003] Currently, most mainstream transparent display solutions employ a structure that adds an independent light-emitting layer or projection film layer to the glass surface. While these solutions can achieve basic image output in display mode, they often exhibit optical phenomena such as slight reflections from electrode lines, iridescent interference caused by the thin film, or a hazy appearance due to decreased overall transmittance in non-display mode. These effects result in a visually perceptible difference between the glass surface and ordinary float glass when the device is off, affecting its invisibility performance. Furthermore, to achieve high-contrast displays, a sophisticated electrode network and optical control layer are typically integrated onto the glass substrate. These functional layers themselves introduce light scattering, reflection, and interference effects. On one hand, the electrodes need sufficient conductivity to support high brightness and fast response, which usually requires a certain thickness or linewidth density. On the other hand, while increasing electrode size helps improve electrical performance, it may enhance optical visibility in the visible light band. To suppress such reflections, anti-reflective films are often introduced, but the superposition of multiple optical films may induce new interference chromatic aberrations, further affecting optical uniformity in non-operating states.

[0004] The optical performance of glass-based displays is closely related to the parameters of their internal multilayer structure. The geometric arrangement of the electrode network, the refractive index of the material, and the thickness and density of the anti-reflective layer collectively determine the balance between bright-state contrast and dark-state transparency. Existing design methods are mostly based on empirical rules or local optimization strategies, with less emphasis on collaborative modeling from the perspective of overall optical-electrical coupling. Especially in non-operating states, the complex propagation paths of incident light passing through the interfaces of the multilayer media make it difficult for traditional optical evaluation methods to accurately predict overall transmission behavior. Furthermore, minute deviations in the electrodes and control layers under different manufacturing conditions can manifest as significant visual unevenness on a macroscopic scale, placing higher demands on high-consistency manufacturing. The interrelationship of these factors makes achieving an optical appearance highly consistent with ordinary glass while maintaining ultra-high contrast display performance a key challenge in the current technological landscape.

[0005] Especially under conditions of high ambient light or wide viewing angles, even weak scattering or interference effects can be significantly amplified, leading to brightness or color deviations in localized areas. Simultaneously, the design of the anti-reflective structure must balance wide-band and wide-angle suppression effects, while the electrode network layout is limited by the integration density of the driving circuit. This multi-objective constraint-driven collaborative optimization problem further increases the complexity of the system design. Therefore, how to systematically coordinate the electrode conductivity, anti-reflective layer structure, and optical control layer matching relationship through a combination of data-driven and multiphysics simulation methods, while ensuring high brightness contrast and making the transmitted light distribution in the non-operating state highly consistent with that of ordinary glass, has become a core problem that urgently needs to be solved in the optical processing system of ultra-high contrast glass-based displays. Summary of the Invention

[0006] The purpose of this invention is to address the aforementioned shortcomings in the prior art by providing an optical processing system for an ultra-high contrast glass-based display screen.

[0007] The objective of this invention is achieved through the following technical solution: an optical processing system for an ultra-high contrast glass-based display screen, comprising the following steps: S1. Obtain electrode network data and optical control layer parameters of the glass-based display screen, extract light scattering and reflection interference features from them, classify these features using the random forest algorithm to determine the degree of optical influence in the non-working state, and obtain a preliminary optical defect distribution map. S2. Based on the preliminary optical defect distribution map, simulate and adjust the conductivity-related parameters, and use the support vector machine algorithm to predict the change in high-brightness display performance after adjustment. If the predicted performance exceeds the preset threshold, the adjustment scheme is retained; otherwise, the parameters are iteratively optimized to determine the optimized conductivity configuration. S3. Obtain the design input of the anti-reflection layer from the optimized conductivity configuration, calculate the suppression effect of layer thickness and density on reflection interference through optical simulation model, determine whether the suppression effect reaches the uniform standard, and obtain the preliminary structural description of the anti-reflection layer. S4. Based on the preliminary structural description of the anti-reflection layer, integrate the electrode network data to perform multi-layer optical simulation, and use the finite difference time domain method to analyze the light scattering distribution. If the analysis results show that the scattering is lower than the preset threshold, the structure is confirmed to be effective, and the final anti-reflection layer parameters are determined. S5. From the final anti-reflection layer parameters, obtain the integration requirements of the optical control layer, adjust the matching degree between the control layer and the electrode network through an iterative optimization algorithm, determine whether the matching degree improves the contrast under high brightness display, and obtain the integrated optical processing system model. S6. Based on the integrated optical processing system model, simulate the incident light response in the non-working state, use the Monte Carlo method to trace the light path to evaluate the transmission uniformity, and output the verification data if the evaluation result meets the consistency of pure glass to determine the optical uniformity index of the system. S7. Obtain feedback data from the optical uniformity index of the system to further refine the conductivity and anti-reflection layer configuration. Iterate the above simulation process through a closed-loop adjustment mechanism to determine whether the overall performance achieves seamless integration and obtain an optimized optical processing scheme for the glass-based display screen. S8. Based on the optimized optical processing scheme for the glass-based display screen, and combined with the manufacturing process constraints and the material optical constant database, generate a set of structural parameters and interlayer stacking sequence specifications suitable for mass production.

[0008] The beneficial effects of this invention are: I. This invention achieves seamless integration of ultra-high contrast in high-brightness display mode and visual consistency of pure glass in non-working mode through collaborative modeling and parameter optimization of electrode network, anti-reflection layer and optical control layer. It completely solves the optical defects such as electrode reflection, interference color difference and obvious fogging in traditional solutions, and significantly improves the visual experience and environmental integration of glass-based display screen.

[0009] Second, this invention introduces a random forest algorithm to achieve precise location and classification of optical defects, a support vector machine algorithm to achieve rapid optimization and prediction of conductivity parameters, and an iterative optimization algorithm to achieve efficient matching between the control layer and the electrode network. Compared with traditional empirical design or local optimization strategies, this significantly improves the accuracy and efficiency of parameter design. Simultaneously, through feature extraction and data-driven modeling, the risk of optical performance fluctuations caused by multi-layered structural coupling is effectively avoided, resulting in small transmission fluctuations across the entire field of view and ensuring performance consistency during mass production.

[0010] Third, this invention integrates multiphysics simulation techniques such as rigorous coupled-wave analysis, transmission matrix method, finite-difference time-domain method, and Monte Carlo ray tracing to systematically analyze complex optical behaviors such as light scattering, reflection interference, and polarization modulation, suppressing adverse effects such as electrode edge diffraction, multilayer film interference, and interface scattering at the source. The resulting antireflective layer achieves a small standard deviation of reflectivity fluctuation across the entire visible light spectrum and a wide incident angle range, with a low scattering ratio, and optical uniformity indicators comprehensively superior to industry standards.

[0011] Fourth, this invention uses a closed-loop adjustment mechanism to dynamically refine the conductivity configuration and anti-reflection layer parameters, ensuring that the overall performance continuously approaches the optimal solution. At the same time, the optimized scheme is transformed into a complete process package that includes photolithography mask rules, sputtering deposition parameters, liquid crystal injection pressure, interlayer adhesion requirements, etc. It clarifies key mass production indicators such as the physical thickness tolerance of each functional layer and the matching criteria for the coefficient of thermal expansion, effectively ensuring compatibility with existing display panel manufacturing equipment, reducing technology transfer costs and mass production risks, and providing a solid guarantee for large-scale application.

[0012] Fifth, the optical processing system of this invention is suitable for high-end scenarios with extremely high requirements for visual effects and environmental integration, such as intelligent building curtain walls, vehicle-mounted transparent displays, and augmented reality devices, breaking through the application limitations of traditional glass-based displays in high-end commercial and home decoration fields. Its technical solution not only enriches the realization path of transparent display technology but also promotes the development of glass-based displays towards the integration of invisible and high-definition displays. Attached Figure Description

[0013] The invention will be further illustrated with reference to the accompanying drawings, but the embodiments in the drawings do not constitute any limitation on the invention. For those skilled in the art, other drawings can be obtained based on the following drawings without any creative effort.

[0014] Figure 1 This is a flowchart of the method of the present invention. Detailed Implementation

[0015] The present invention will be further described in conjunction with the following embodiments.

[0016] Depend on Figure 1 As can be seen, the optical processing system for an ultra-high contrast glass-based display screen described in this embodiment is characterized by including the following steps: S1. Obtain electrode network data and optical control layer parameters of the glass-based display screen, extract light scattering and reflection interference features from them, classify these features using the random forest algorithm to determine the degree of optical influence in the non-working state, and obtain a preliminary optical defect distribution map. S2. Based on the preliminary optical defect distribution map, simulate and adjust the conductivity-related parameters, and use the support vector machine algorithm to predict the change in high-brightness display performance after adjustment. If the predicted performance exceeds the preset threshold, the adjustment scheme is retained; otherwise, the parameters are iteratively optimized to determine the optimized conductivity configuration. S3. Obtain the design input of the anti-reflection layer from the optimized conductivity configuration, calculate the suppression effect of layer thickness and density on reflection interference through optical simulation model, determine whether the suppression effect reaches the uniform standard, and obtain the preliminary structural description of the anti-reflection layer. S4. Based on the preliminary structural description of the anti-reflection layer, integrate the electrode network data to perform multi-layer optical simulation, and use the finite difference time domain method to analyze the light scattering distribution. If the analysis results show that the scattering is lower than the preset threshold, the structure is confirmed to be effective, and the final anti-reflection layer parameters are determined. S5. From the final anti-reflection layer parameters, obtain the integration requirements of the optical control layer, adjust the matching degree between the control layer and the electrode network through an iterative optimization algorithm, determine whether the matching degree improves the contrast under high brightness display, and obtain the integrated optical processing system model. S6. Based on the integrated optical processing system model, simulate the incident light response in the non-working state, use the Monte Carlo method to trace the light path to evaluate the transmission uniformity, and output the verification data if the evaluation result meets the consistency of pure glass to determine the optical uniformity index of the system. S7. Obtain feedback data from the optical uniformity index of the system to further refine the conductivity and anti-reflection layer configuration. Iterate the above simulation process through a closed-loop adjustment mechanism to determine whether the overall performance achieves seamless integration and obtain an optimized optical processing scheme for the glass-based display screen. S8. Based on the optimized optical processing scheme for the glass-based display screen, and combined with the manufacturing process constraints and the material optical constant database, generate a set of structural parameters and interlayer stacking sequence specifications suitable for mass production.

[0017] This embodiment describes an optical processing system for an ultra-high contrast glass-based display screen. The preliminary optical defect distribution map includes scattering hotspot localization, interference fringe intensity mapping, and local transmittance deviation quantification. The optimized conductivity configuration includes a combination of electrode linewidth, spacing, and material conductivity parameters. The preliminary structural description of the anti-reflective layer includes refractive index gradient distribution, film thickness sequence, and interface roughness control range. The final anti-reflective layer parameters include the thickness of each sublayer, material composition ratio, and deposition angle. The integrated optical processing system model includes the electrode-control layer coupling phase matching relationship, polarization modulation response function, and viewing angle-dependent attenuation coefficient. The system's optical uniformity indices include full-field-of-view transmission fluctuation amplitude, color coordinate offset, and haze value. The optimized glass-based display screen optical processing scheme includes electrode topology reconstruction rules, anti-reflective film system design templates, and control layer microstructure arrangement strategies. The structural parameter set and interlayer stacking sequence specifications include the physical thickness tolerance of each functional layer, interface adhesion requirements, and thermal expansion coefficient matching criteria.

[0018] The optical processing system for an ultra-high contrast glass-based display screen described in this embodiment acquires electrode network data and optical control layer parameters of the glass-based display screen, extracts light scattering and reflection interference features from them, and classifies these features using a random forest algorithm to determine the degree of optical influence in the non-working state, thereby obtaining a preliminary optical defect distribution map. The specific steps are as follows: Obtain geometric data of the electrode network already deployed on the glass substrate, including line width, line spacing, intersection density and material refractive index, and simultaneously collect the thickness, dielectric constant and surface morphology parameters of the optical control layer to construct an initial model of the multilayer dielectric structure; Based on the initial model, the reflectivity spectrum and transmission phase distribution at each incident angle in the 400-700nm visible light band were calculated using the rigorous coupled-wave analysis method. The concentrated area of ​​scattered energy caused by diffraction at the electrode edge (i.e., the location of scattering hotspots) and the position of the periodic reflection peaks formed by multilayer film interference (i.e., the intensity mapping of interference fringes) were extracted. The spatial coordinates of the concentrated scattering energy region and the wavelength-angle correspondence of the reflection peak are encoded into a feature vector and input into a pre-trained random forest classifier. The classifier makes a judgment based on the visible and invisible defect labels marked in historical samples. Output the optical impact level corresponding to each pixel location and present it in the form of a grayscale image to generate a preliminary optical defect distribution map (i.e., local transmittance deviation quantization).

[0019] The optical processing system for an ultra-high contrast glass-based display screen described in this embodiment involves simulating and adjusting conductivity-related parameters based on the preliminary optical defect distribution map, using a support vector machine algorithm to predict changes in high-brightness display performance after adjustment, and retaining the adjustment scheme if the predicted performance exceeds a preset threshold; otherwise, iteratively optimizing the parameters to determine the optimized conductivity configuration. The specific steps are as follows: Based on the location of the high-impact region in the optical defect distribution map, the linewidth of the corresponding electrode is reduced or the spacing between adjacent electrodes is increased, while keeping the number of conductive paths per unit area constant, to generate a candidate set of conductive parameters. The candidate set of conductive parameters is substituted into the electro-optical coupling simulation platform to calculate the response time and luminous uniformity of the pixel unit under the maximum driving current, and the contrast maintenance rate is extracted as a high-brightness display performance index. The contrast maintenance rate and electrode geometric parameters are combined to form training samples, which are then input into a radial basis function support vector machine regression model to predict the performance output under unsimulated parameter combinations. The contrast retention rate threshold was set to 92%. Parameter combinations with predicted values ​​higher than this threshold were screened, and the one with the smallest total electrode projection area was selected as the optimized conductivity configuration.

[0020] The optical processing system for an ultra-high contrast glass-based display screen described in this embodiment obtains the design input for the anti-reflection layer from the optimized conductivity configuration, calculates the suppression effect of layer thickness and density on reflection interference through an optical simulation model, and determines whether the suppression effect reaches a uniform standard to obtain a preliminary structural description of the anti-reflection layer. The specific steps are as follows: Based on the electrode surface morphology corresponding to the optimized conductivity configuration, the highest spatial frequency component that the anti-reflection layer needs to cover (i.e., interface roughness control) is set, and the required minimum film thickness resolution is determined. A three-layer graded refractive index film model (i.e., refractive index gradient distribution) was established using the transfer matrix method. The variables included the thickness of the bottom high refractive index material, the porosity of the middle transition layer, and the density of the top low refractive index material (i.e., film thickness sequence). Scan all combinations of variables within a 550nm center wavelength and ±30° incident angle range, calculate the standard deviation of average reflectance fluctuation, and determine that the uniformity standard is met when the standard deviation is less than 0.8%. Record the combinations of membrane parameters that meet the conditions, sort them according to process feasibility, and output the top three groups as preliminary structural descriptions of the anti-reflective layer.

[0021] The optical processing system for an ultra-high contrast glass-based display screen described in this embodiment involves, based on the preliminary structural description of the anti-reflection layer, integrating electrode network data for multi-layer optical simulation, and using the finite difference time-domain method to analyze the light scattering distribution. If the analysis results show that the scattering is below a preset threshold, the structure is confirmed to be effective. The specific steps for determining the final anti-reflection layer parameters are as follows: The preliminary structural description of the anti-reflection layer is superimposed with the three-dimensional model of the electrode network to construct a complete optical structure including interface roughness. In the FDTD solver, a plane wave source is set up with a wavelength coverage of 400-700 nm and an incident angle step of 5° to calculate the far-field scattering intensity distribution. The proportion of scattered light intensity to total transmitted light intensity at all angles is statistically analyzed. If this proportion is less than 0.5% across the entire wavelength band, the structure is considered valid. The set of parameters with the lowest material cost and the widest deposition process window is selected from the effective structure and determined as the final anti-reflection layer parameters (i.e., the thickness of each sublayer, the proportion of material components, and the deposition angle).

[0022] The optical processing system for an ultra-high contrast glass-based display screen described in this embodiment obtains the integration requirements of the optical control layer from the final anti-reflection layer parameters, adjusts the matching degree between the control layer and the electrode network through an iterative optimization algorithm, and determines whether the matching degree improves the contrast under high brightness display, thus obtaining the integrated optical processing system model. The specific steps are as follows: Based on the top-layer flatness requirements defined by the final anti-reflection layer parameters, the maximum allowable thickness gradient of the optical control layer is set. A genetic algorithm was used to jointly optimize the liquid crystal orientation angle, phase retardation, and driving voltage-transmittance curve of the control layer. The objective function was the difference between maximizing the transmittance in the bright state and minimizing the light leakage in the dark state. Each generation of individuals was subjected to electromagnetic field coupling simulation with a fixed electrode network, and contrast values ​​were extracted. The iteration terminates when the contrast improvement of the best individual across five consecutive generations is less than 0.3%, and the combination of the current control layer parameters and electrode layout is output to form an integrated optical processing system model.

[0023] The optical processing system for an ultra-high contrast glass-based display screen described in this embodiment simulates the incident light response in a non-operating state based on the integrated optical processing system model. The Monte Carlo method is used to trace the light path to evaluate transmission uniformity. If the evaluation result meets the requirements of pure glass, verification data is output. The specific steps for determining the optical uniformity index of the system are as follows: Import the integrated optical processing system model into the Monte Carlo ray tracing engine, set millions of random incident rays with wavelengths conforming to the CIE standard illuminator D65 distribution; Record the outgoing direction, phase delay, and energy attenuation of each ray after it passes through the system, and statistically analyze the spatial distribution variance of the transmitted light (i.e., the transmission fluctuation amplitude across the entire field of view) and the chromaticity coordinate dispersion (i.e., the chromaticity coordinate offset). The variance and dispersion are compared with the reference values ​​of 3mm thick float glass. If the deviations of both are less than the threshold that the human eye can distinguish (ΔE<1.5, transmission fluctuation<1.2%), then the verification data is output. Based on the verification data, the haze value, the full field-of-view transmission uniformity index, and the polarization independence coefficient are calculated to determine the optical uniformity index of the system.

[0024] The optical processing system for an ultra-high contrast glass-based display screen described in this embodiment obtains feedback data from the system's optical uniformity index to further refine conductivity and anti-reflection layer configuration. It iterates the above simulation process through a closed-loop adjustment mechanism to determine whether the overall performance achieves seamless integration, resulting in an optimized optical processing scheme for the glass-based display screen. The specific steps are as follows: Extract sub-items that exceed the tolerance in the optical uniformity index and back-map them to the electrode line edge steepness (i.e., electrode topology reconstruction rule) or the anti-reflection layer interface roughness parameter (i.e., anti-reflection film system design template). Apply a ±15% perturbation to the out-of-tolerance parameters, re-execute processes S2 to S6, and generate a new set of performance data; Pareto front analysis was used to screen for the solution that achieves the optimal balance between contrast and transparency. The loop stops when the overall performance difference between two consecutive iterations is less than 0.7%, and the current solution is output as the optimized optical processing solution for the glass-based display screen.

[0025] The optical processing system for an ultra-high contrast glass-based display screen described in this embodiment, based on the optimized optical processing scheme for the glass-based display screen and combined with manufacturing process constraints and a material optical constant database, specifically generates a set of structural parameters and interlayer stacking sequence specifications suitable for mass production as follows: The electrode linewidth, the thickness of each sublayer of the antireflection layer, and the phase delay of the control layer in the optimization scheme are converted into photolithography mask design rules, sputtering deposition time parameters, and liquid crystal injection pressure settings. The refractive index-wavelength curves at the corresponding process temperature in the material database are used to correct the dispersion difference between the simulation model and the actual material. The maximum residual stress limit of each functional layer is set according to the thermal expansion coefficient of the glass substrate (i.e., thermal expansion coefficient matching criterion), and the coating thickness of the interlayer adhesion promoter is adjusted (i.e., interface adhesion requirement). The output includes a complete process package containing tolerance zones, deposition sequence, annealing curves, and online inspection points, forming a set of structural parameters and interlayer stacking sequence specifications.

[0026] This embodiment provides an optical processing system for an ultra-high contrast glass-based display screen, the overall process of which is as follows: Figure 1 As shown, the system achieves coordinated optimization of the optical performance of the glass-based display screen in both non-operating and high-brightness display states through eight sequentially linked and partially feedback-coupled modular steps.

[0027] First, in the S1 preliminary optical defect distribution map generation module, the geometric data of the electrode network already deployed on the glass substrate is acquired, including an electrode linewidth of 3.2 μm, a line spacing of 8.5 μm, an intersection density of 142 per square millimeter, and the refractive index of the indium tin oxide (ITO) material used is 1.92. Simultaneously, the physical parameters of the optical control layer are collected, including a thickness of 4.8 μm, a dielectric constant of 3.7, and a surface roughness RMS value of 0.18 nm. This data is then imported into multilayer dielectric structure modeling software to construct a three-dimensional initial model containing the glass substrate, electrode layer, and control layer. Based on this model, the rigorous coupled-wave analysis (RCWA) method is used to perform a full-angle scan within the visible light band of 400-700 nm with an incident angle step of 2° to calculate the reflectivity spectrum and transmission phase distribution. Two key features were extracted from the analysis results: first, the concentrated area of ​​scattered energy caused by diffraction at the electrode edge, whose spatial coordinates were recorded as (x, y) pixel positions; second, the periodic interference peaks formed by multiple reflections between the glass, electrode, and control layer, whose wavelength-angle correspondence was encoded as (λ, θ) feature pairs. These two feature vectors were input into a pre-trained random forest classifier. This classifier judges based on manually labeled "visible defects" and "invisible defects" from historical samples, outputting the optical impact level (0-5) for each pixel position, and generating a preliminary optical defect distribution map in 8-bit grayscale image form, where a grayscale value of 255 represents the region with the highest impact level.

[0028] After entering the S2 conductivity configuration optimization module, based on the optical defect distribution map output by S1, high-influence regions with grayscale values ​​greater than 180 are identified. Within these regions, an electrode geometry parameter adjustment strategy is implemented: the original electrode linewidth is reduced from 3.2 μm to 2.6 μm, and the spacing between adjacent electrodes is increased from 8.5 μm to 9.3 μm. Simultaneously, the number of conductive paths per unit area is kept constant by adding localized circuitry, thereby generating a set of candidate conductivity parameters. This parameter set is then imported into the COMSOL Multiphysics electro-optic coupling simulation platform, with the maximum driving current set to 15 mA / cm². 2The response time and luminous uniformity of each pixel unit were calculated, and the contrast maintenance rate was extracted as a performance indicator for high-brightness displays. The contrast maintenance rate values ​​were combined with corresponding electrode geometric parameters (line width, spacing, and crosspoint density) to form training samples, which were then input into a support vector machine regression model using a radial basis function (RBF kernel) to predict the performance output under parameter combinations not actually simulated. A contrast maintenance rate threshold of 92% was set, and all parameter combinations with predicted values ​​higher than this threshold were filtered out. The group with the smallest total electrode projected area was selected, i.e., a line width of 2.7 μm, a spacing of 9.1 μm, and a crosspoint density of 138 / mm. 2 As an optimized conductivity configuration.

[0029] Subsequently, in the preliminary structural design module of the S3 anti-reflection layer, based on the electrode surface morphology data corresponding to the optimized conductivity configuration output from S2, the spatial frequency components were analyzed using Fourier transform, and the highest effective frequency was determined to be 1.8 μm. -1 Based on this, the minimum film thickness resolution required for the antireflective layer was set to 50 nm. A three-layer graded refractive index film model was established using the transfer matrix method (TMM). The bottom layer was TiO2 (n=2.35), the middle transition layer was a SiO2-TiO2 mixed porous structure (porosity adjustable), and the top layer was MgF2 (n=1.38). Variables included the bottom layer thickness (80-120 nm), the middle layer porosity (30%-60%), and the top layer density (corresponding to a refractive index of 1.35-1.40). Within a center wavelength of 550 nm and an incident angle of ±30°, parameter scans were performed with a thickness step of 5 nm and a porosity step of 5%, and the standard deviation of the average reflectance fluctuation under each combination was calculated. When the standard deviation was less than 0.8%, the uniformity standard was considered met. Finally, three sets of parameter combinations that meet the conditions are recorded: combination A (bottom layer 105nm, porosity 48%, top layer density 1.37), combination B (bottom layer 98nm, porosity 52%, top layer density 1.36), and combination C (bottom layer 112nm, porosity 45%, top layer density 1.38). After sorting them according to the sputtering deposition process window width, the first three sets are output as the preliminary structural description of the anti-reflection layer.

[0030] In the S4 antireflective layer parameter verification module, the three preliminary structural descriptions output from S3 were Boolean superposition operations performed with the optimized 3D electrode network model from S2 to construct a complete optical structural model in SolidWorks, including interface roughness (RMS = 0.22 nm). This model was imported into the LumericalFDTD solver, with a plane wave source covering the 400-700 nm band and an incident angle ranging from 0° to 60° in 5° increments, to calculate the far-field scattering intensity distribution. The proportion of scattered light intensity to total transmitted light intensity was statistically analyzed for each structural group at all wavelengths and angles. The results showed that combination B had a scattering proportion below 0.5% across the entire band (the highest value was 0.47%), combination A had 0.53%, and combination C had 0.58%. Therefore, only combination B met the preset threshold. Further comparison of material costs and deposition process windows showed that combination B required 7% less TiO2 than combination A, and the porosity of the intermediate layer was within the stable control range of magnetron sputtering (45%-55%). Therefore, combination B was selected as the final anti-reflection layer parameters, specifically including: a TiO2 thickness of 98 nm at the bottom layer, a SiO2-TiO2 mixture porosity of 52% at the middle layer, and a MgF2 density at the top layer corresponding to a refractive index of 1.36.

[0031] After entering the S5 optical control layer integration modeling module, based on the anti-reflection layer top-layer flatness requirement (surface undulation ≤ 0.3nm) determined in S4, the maximum allowable thickness gradient of the optical control layer is set to ±0.05μm / 100μm. The NSGA-II genetic algorithm is used to jointly optimize the key parameters of the control layer, including the initial orientation angle of liquid crystal molecules (0°-90°), phase retardation (Δn*d = 0.2-0.5μm), and the slope of the driving voltage-transmittance curve (0.8-1.2% / V). The objective function is defined as the difference between maximizing the bright-state transmittance (V=5V) and minimizing the dark-state leakage (V=0V). Fifty individuals are generated per generation, and each individual is subjected to electromagnetic field coupling simulation with a fixed electrode network (from S2) in AnsysHFSS to extract contrast values. During iteration, the iteration terminates when the contrast improvement of the best individual for five consecutive generations is less than 0.3% (e.g., contrast ratio of 12500:1 in generation 48 and 12532:1 in generation 53). The current optimal control layer parameters are output: orientation angle 82°, phase delay 0.43μm, voltage-transmittance slope 1.05% / V, and bound to the electrode layout data to form an integrated optical processing system model. This model explicitly includes the coupling phase matching relationship between the electrode and the control layer (phase difference tolerance ±5°), polarization modulation response function (Jones matrix form), and viewing angle dependent attenuation coefficient (contrast attenuation does not exceed 18% at a viewing angle of ±60°).

[0032] In the S6 optical uniformity verification module, the integrated model output from S5 was imported into the TracePro Monte Carlo ray tracing engine. The number of incident rays was set to 1,200,000, with wavelengths conforming to the CIED65 standard illuminator spectral power distribution. The incident directions were uniformly and randomly sampled within a hemispherical space. The outgoing direction vector, phase delay, and energy attenuation value of each ray after passing through the system were recorded. The spatial distribution variance of transmitted light on the exit surface (expressed as standard deviation) and the CIE1931 chromaticity coordinate dispersion (expressed as the area enclosed by an ellipse for 95% of the data points) were statistically analyzed. These two indicators were compared with the measured values ​​of a 3mm thick float glass reference sample: the reference transmission fluctuation standard deviation was 0.98%, and the area of ​​the chromaticity coordinate dispersion ellipse was 0.00018. The measured transmission fluctuation standard deviation of this system was 1.12%, and the area of ​​the chromaticity coordinate dispersion ellipse was 0.00021, corresponding to a color difference ΔE = 1.32. Since ΔE < 1.5 and transmission fluctuation < 1.2%, it was determined to meet the requirements for pure glass consistency. Based on this, the haze value was calculated to be 0.17%, the full field-of-view transmission uniformity index was 0.986, and the polarization independence coefficient (standard deviation of s / p polarization transmittance ratio) was 0.008, thus determining the optical uniformity index of the system.

[0033] Subsequently, in the S7 closed-loop feedback refining module, the optical uniformity index output from S6 was analyzed. It was found that while the haze value met the standard, it was close to the upper limit (0.17% vs. 0.2% limit), which was then mapped back to the anti-reflection layer interface roughness parameter. A ±15% perturbation was applied to this parameter, adjusting the RMS value from 0.22nm to 0.187nm and 0.253nm. The S2-S6 process was then repeated, generating two new sets of performance data. Simultaneously, considering that the transmission uniformity index was slightly lower than the ideal value (0.986 vs. target 0.99), the electrode line edge steepness parameter (originally 78°) was perturbed to 75° and 81°, and S2-S6 was executed again. A total of four new schemes were obtained. Using Pareto front analysis, non-dominated solutions were selected between two objectives: contrast ratio (>12000:1) and transparency (haze <0.18%). The optimal solution was chosen based on the following overall performance: electrode edge steepness of 81° and anti-reflective layer interface RMS = 0.187nm. Comparing this solution with the previous solution, the overall performance difference was 0.63%, which is less than the 0.7% stopping threshold. Therefore, the iteration was terminated, and the current solution was output as the optimized optical processing scheme for the glass-based display screen. This scheme includes electrode topology reconstruction rules (edge ​​passivation radius of 0.15μm), anti-reflective film system design template (fixed three-layer structure parameters), and microstructure arrangement strategy for the control layer (liquid crystal domain size controlled at 8μm × 8μm).

[0034] Finally, in the S8 mass production parameter output module, the optimized scheme output by S7 is converted into manufacturing process parameters: the electrode linewidth of 2.7μm is converted into the minimum linewidth CD=2.75μm in the photolithography mask design rules (including process deviation compensation); the thicknesses of each sublayer of the anti-reflection layer (98nm, 110nm, 85nm) are converted into deposition time parameters for DC magnetron sputtering (TiO2 layer: 98 seconds, mixed layer: 110 seconds, MgF2 layer: 85 seconds, calibrated based on a deposition rate of 0.98Å / s); the phase retardation of the control layer of 0.43μm is converted into the liquid crystal filling pressure setting value of 1.8kPa. The material optical constants database (containing the n / k values ​​of SchottBOROFLOAT33 glass, ITO, TiO2, SiO2, and MgF2 in the 200-1000nm band) is called to correct the difference between the material dispersion in the simulation model and the actual batch material, and the film thickness parameter is corrected to ±2nm. Based on the thermal expansion coefficient of the glass substrate (3.25×10⁻⁶), the difference between the material dispersion in the simulation model and the actual batch material is corrected. -6 The maximum residual stress limit for each functional layer is set to 80 MPa ( / ℃), and the coating thickness of the silane coupling agent (APTES) adhesion promoter is adjusted to 8 nm accordingly. The final output is a complete process package containing the following: physical thickness tolerance of each functional layer (±3%), interlayer stacking order (glass-ITO electrode-TiO2-mixed layer-MgF2-alignment layer-liquid crystal-upper substrate), annealing profile (180℃ / 30min / N2 atmosphere), online inspection points (electrode linewidth AOI inspection, anti-reflection layer reflectivity spectrum sampling inspection, haze final inspection), forming a set of structural parameters and interlayer stacking order specifications, which can be directly used to guide the setting of production line equipment parameters and quality control.

[0035] To enable those skilled in the art to fully understand and implement this invention, the specific implementation principles of this invention are further supplemented below with a specific application scenario.

[0036] In the actual deployment of intelligent building curtain wall integrated transparent display systems, the glass-based display screen needs to present a visual appearance completely consistent with ordinary float glass when closed during the day, while providing high-contrast image output when turned on at night. To achieve this goal, the S1 preliminary optical defect distribution map generation module is first used, based on the ITO electrode network (3.2μm linewidth, 8.5μm line spacing, and 142 crosspoints / mm) already deployed on a 3.2mm thick SchottBOROFLOAT33 glass substrate. 2A three-dimensional optical model containing three dielectric interfaces was constructed, consisting of a glass-ITO-liquid crystal layer and a 4.8 μm thick liquid crystal control layer above it (RMS roughness 0.18 nm). Rigorous coupled-wave analysis (RCWA) was used to scan the 400-700 nm band with a 2° incident angle step size to identify scattering hotspots caused by electrode edge diffraction (such as energy concentration at coordinates (1250, 840)) and a 550 nm / 28° periodic reflection peak formed by interference between the glass, ITO, and liquid crystal interfaces. These features were then processed by a random forest classifier to generate an optical defect distribution map corresponding to high-influence areas with a grayscale value of 255, accurately locating optical anomalies that might be visible to the naked eye when not in operation.

[0037] Subsequently, in the S2 conductivity configuration optimization module, for regions with grayscale values ​​>180, the electrode linewidth was reduced from 3.2 μm to 2.6 μm and the spacing increased to 9.3 μm. Simultaneously, localized circuitry was used to maintain a constant conductivity density per unit area, thereby reducing geometric occlusion and edge diffraction intensity of the electrodes in the visible light band. This strategy was validated in COMSOL Multiphysics at 15 mA / cm². 2 Under the driving current, the pixel response time still meets the requirement of <8ms, and the contrast retention rate reaches 93.1%. The full-parameter spatial performance was predicted using a support vector machine regression model, and a line width of 2.7μm, a spacing of 9.1μm, and an intersection density of 138 points / mm were ultimately selected. 2 The combination satisfies the electrical performance threshold (contrast retention ≥ 92%) while minimizing the electrode projection area, thereby reducing optical visibility in non-working state from the source.

[0038] Entering the preliminary structural design module of the S3 anti-reflection layer, based on Fourier spectrum analysis of the optimized electrode surface morphology, its highest effective spatial frequency was determined to be 1.8 μm. -1 Therefore, the antireflective layer is required to have a film thickness resolution of ≤50nm to effectively cover high-frequency components. A three-layer graded refractive index structure of TiO2 (n=2.35), porous SiO2-TiO2 hybrid layer, and MgF2 (n=1.38) was constructed using the transfer matrix method (TMM). Parameters were scanned within a range of 550nm±30°, and three candidate schemes with an average reflectance fluctuation standard deviation of <0.8% were selected. Combination B (bottom layer 98nm, porosity 52%, top layer refractive index 1.36) was preferentially retained due to its combination of low reflectance fluctuation and wide process window.

[0039] In the S4 antireflection layer parameter verification module, the composite B was superimposed with the electrode model using Boolean superposition. After introducing a 0.22nm RMS interface roughness, far-field scattering simulations were performed across the entire wavelength range (400-700nm) and at large angles (0°-60°) using LumericalFDTD. The results show that the peak scattering proportion of composite B is only 0.47%, significantly lower than the 0.5% threshold, indicating that it can effectively suppress secondary scattering caused by the electrode microstructure. This effect stems from the quasi-continuous refractive index transition (from 2.35-1.72-1.36) formed by the 52% porosity of the intermediate layer, which significantly weakens Fresnel reflections at each interface. At the same time, the porous structure reduces interference chromatic aberration caused by optical path difference.

[0040] In the S5 optical control layer integrated modeling module, based on the requirement that the top surface flatness of the anti-reflective layer be ≤0.3nm, the thickness gradient of the liquid crystal layer is limited to ±0.05μm / 100μm to avoid introducing additional phase distortion due to surface undulations. By jointly optimizing the liquid crystal orientation angle (82°), phase retardation (0.43μm), and voltage-transmittance slope (1.05% / V) using the NSGA-II algorithm, a bright-state transmittance of 86.3% is achieved under 5V driving, while dark-state light leakage is only 0.0069% at 0V, resulting in an ultra-high contrast ratio of 12500:1. This performance relies on the phase matching relationship between the electrode and the liquid crystal layer (with a tolerance of ±5°), ensuring a uniform electric field distribution to drive the liquid crystal molecules to rotate and avoiding localized light leakage.

[0041] In the S6 optical uniformity verification module, TracePro Monte Carlo traced 1.2 million rays from a D65 light source, and the statistical standard deviation of the spatial fluctuation of transmitted light was 1.12%, with a CIE1931 color coordinate discrete ellipse area of ​​0.00021, corresponding to ΔE=1.32. This result is close to the 3mm float glass reference (ΔE≈0), indicating that the human eye cannot distinguish it from ordinary glass when the system is not in operation. The haze value of 0.17% is achieved due to the effective suppression of forward scattering by the anti-reflective layer and the reduction of diffraction sidelobes by controlling the electrode edge steepness (78°).

[0042] In the S7 closed-loop feedback refining module, to address the issue of haze approaching its upper limit, the RMS of the antireflective layer interface was reduced from 0.22 nm to 0.187 nm, and the electrode edge steepness was increased to 81° (passivation radius 0.15 μm), and the S2-S6 process was re-executed. The new solution reduced haze to 0.15%, improved the transmission uniformity index to 0.989, and maintained a contrast ratio of 12520:1. Pareto analysis confirmed that this solution was a non-dominated solution, with an overall performance improvement of 0.63%, meeting the termination criteria.

[0043] Finally, in the S8 mass production parameter output module, the electrode linewidth of 2.7 μm was converted to a photolithography mask CD of 2.75 μm to compensate for process deviations; the thicknesses of each sublayer of the antireflective layer (98 nm TiO2, 110 nm mixed layer, 85 nm MgF2) were converted to sputtering time at a deposition rate of 0.98 Å / s; the liquid crystal phase retardation of 0.43 μm corresponded to a potting pressure of 1.8 kPa. Dispersion differences were corrected using a materials database, and the glass thermal expansion coefficient (3.25 × 10⁻⁶) was used as a guideline. -6 ( / ℃) Set an 8nm APTES adhesion layer to control residual stress <80MPa, and finally output a complete process package including layer sequence, tolerance, annealing curve and online detection points to ensure that the production line can stably reproduce the optical-electrical synergistic optimization effect.

[0044] Finally, 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 the scope of protection of the present invention. 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 essence and scope of the technical solutions of the present invention.

Claims

1. An optical processing system for an ultra-high contrast glass-based display screen, characterized in that: Includes the following steps: S1. Obtain electrode network data and optical control layer parameters of the glass-based display screen, extract light scattering and reflection interference features from them, classify these features using a random forest algorithm to determine the degree of optical influence in the non-working state, and obtain a preliminary optical defect distribution map; the preliminary optical defect distribution map includes scattering hotspot localization, interference fringe intensity mapping, and local transmittance deviation quantification. S2. Based on the preliminary optical defect distribution map, simulate and adjust the conductivity-related parameters. Use a support vector machine algorithm to predict the change in high-brightness display performance after adjustment. If the predicted performance exceeds a preset threshold, retain the adjustment scheme; otherwise, iteratively optimize the parameters to determine the optimized conductivity configuration. The optimized conductivity configuration includes a combination of electrode linewidth, spacing, and material conductivity parameters. S3. From the optimized conductivity configuration, obtain the design input for the anti-reflection layer, calculate the suppression effect of layer thickness and density on reflection interference through an optical simulation model, determine whether the suppression effect reaches the uniform standard, and obtain a preliminary structural description of the anti-reflection layer; the preliminary structural description of the anti-reflection layer includes refractive index gradient distribution, film thickness sequence and interface roughness control range; S4. Based on the preliminary structural description of the anti-reflection layer, multi-layer optical simulation is performed by integrating electrode network data. The light scattering distribution is analyzed using the finite difference time-domain method. If the analysis results show that the scattering is lower than a preset threshold, the structure is confirmed to be effective, and the final anti-reflection layer parameters are determined. The final anti-reflection layer parameters include the thickness of each sublayer, the proportion of material components, and the deposition angle. S5. From the final anti-reflection layer parameters, obtain the integration requirements of the optical control layer, adjust the matching degree between the control layer and the electrode network through an iterative optimization algorithm, determine whether the matching degree improves the contrast under high brightness display, and obtain the integrated optical processing system model; the integrated optical processing system model includes the electrode-control layer coupling phase matching relationship, polarization modulation response function and viewing angle dependent attenuation coefficient; S6. Based on the integrated optical processing system model, simulate the incident light response in the non-working state, and use the Monte Carlo method to trace the light path to evaluate the transmission uniformity. If the evaluation result meets the consistency of pure glass, output the verification data and determine the optical uniformity index of the system. The optical uniformity index of the system includes the transmission fluctuation amplitude of the full field of view, the color coordinate offset, and the haze value. S7. Obtain feedback data from the optical uniformity index of the system to further refine the conductivity and anti-reflection layer configuration. Iteratively execute steps S2 to S6 through a closed-loop adjustment mechanism to determine whether the overall performance achieves seamless integration, and obtain an optimized optical processing scheme for the glass-based display screen. The optimized optical processing scheme for the glass-based display screen includes electrode topology reconstruction rules, anti-reflection film system design templates, and microstructure arrangement strategy for the control layer. S8. Based on the optimized optical processing scheme for the glass-based display screen, and combined with the manufacturing process constraints and the material optical constant database, generate a set of structural parameters and interlayer stacking sequence specifications suitable for mass production; the set of structural parameters and interlayer stacking sequence specifications include the physical thickness tolerance of each functional layer, interface adhesion requirements, and thermal expansion coefficient matching criteria.

2. The optical processing system for an ultra-high contrast glass-based display screen according to claim 1, characterized in that: The specific steps for obtaining electrode network data and optical control layer parameters of a glass-based display screen, extracting light scattering and reflection interference features, classifying these features using a random forest algorithm to determine the degree of optical influence in the non-operating state, and obtaining a preliminary optical defect distribution map are as follows: Obtain geometric data of the electrode network already deployed on the glass substrate, including line width, line spacing, intersection density and material refractive index, and simultaneously collect the thickness, dielectric constant and surface morphology parameters of the optical control layer to construct an initial model of the multilayer dielectric structure; Based on the initial model, the reflectivity spectrum and transmission phase distribution at each incident angle in the visible light band are calculated using the rigorous coupled-wave analysis method. The concentrated area of ​​scattered energy caused by diffraction at the electrode edge and the position of the periodic reflection peak formed by multilayer film interference are extracted. The spatial coordinates of the concentrated scattering energy region and the wavelength-angle correspondence of the reflection peak are encoded into a feature vector and input into a pre-trained random forest classifier. The classifier makes a judgment based on the visible and invisible defect labels marked in historical samples. Output the optical impact level corresponding to each pixel location and present it in grayscale image form to generate a preliminary optical defect distribution map.

3. The optical processing system for an ultra-high contrast glass-based display screen according to claim 1, characterized in that: Based on the preliminary optical defect distribution map, the conductivity-related parameters are simulated and adjusted. A support vector machine algorithm is used to predict the change in high-brightness display performance after adjustment. If the predicted performance exceeds a preset threshold, the adjustment scheme is retained; otherwise, the parameters are iteratively optimized. The specific steps for determining the optimized conductivity configuration are as follows: Based on the location of the high-impact region in the optical defect distribution map, the linewidth of the corresponding electrode is reduced or the spacing between adjacent electrodes is increased, while keeping the number of conductive paths per unit area constant, to generate a candidate set of conductive parameters. The candidate set of conductive parameters is substituted into the electro-optical coupling simulation platform to calculate the response time and luminous uniformity of the pixel unit under the maximum driving current, and the contrast maintenance rate is extracted as a high-brightness display performance index. The contrast maintenance rate and electrode geometric parameters are combined to form training samples, which are then input into a radial basis function support vector machine regression model to predict the performance output under unsimulated parameter combinations. Set a preset contrast maintenance threshold, filter parameter combinations with predicted values ​​higher than the threshold, and select the one with the smallest total electrode projection area as the optimized conductivity configuration.

4. The optical processing system for an ultra-high contrast glass-based display screen according to claim 1, characterized in that: The steps for obtaining the design input of the anti-reflection layer from the optimized conductivity configuration, calculating the effect of layer thickness and density on the suppression of reflection interference through an optical simulation model, and determining whether the suppression effect reaches the uniform standard to obtain the preliminary structural description of the anti-reflection layer are as follows: Based on the optimized conductivity configuration and the corresponding electrode surface morphology, the highest spatial frequency component that the anti-reflection layer needs to cover is set, and the required minimum film thickness resolution is determined. A three-layer graded refractive index film model was established using the transfer matrix method, with variables including the thickness of the bottom high refractive index material, the porosity of the middle transition layer, and the density of the top low refractive index material. Scan each variable combination within the preset center wavelength and preset incident angle range, calculate the average reflectance fluctuation standard deviation, and determine that the reflectance fluctuation threshold corresponding to the preset uniformity standard is met when the standard deviation is less than the preset uniformity standard. Record the combinations of membrane parameters that meet the conditions, sort them according to process feasibility, and output the top three groups as preliminary structural descriptions of the anti-reflective layer.

5. The optical processing system for an ultra-high contrast glass-based display screen according to claim 1, characterized in that: Based on the preliminary structural description of the anti-reflection layer, multi-layer optical simulation is performed by integrating electrode network data. The light scattering distribution is analyzed using the finite difference time-domain method. If the analysis results show that the scattering is below a preset threshold, the structure is confirmed to be effective. The specific steps for determining the final anti-reflection layer parameters are as follows: The preliminary structural description of the anti-reflection layer is superimposed with the three-dimensional model of the electrode network to construct a complete optical structure including interface roughness. Using the finite difference time-domain method, a plane wave source is set up with wavelengths covering the visible light band, and the far-field scattering intensity distribution is calculated within a preset incident angle range; The proportion of scattered light intensity to total transmitted light intensity at all angles is statistically analyzed. If this proportion is lower than the preset scattering threshold across the entire wavelength band, the structure is deemed valid. The set of parameters with the lowest material cost and the widest deposition process window is selected from the effective structures and determined as the final anti-reflective layer parameters.

6. The optical processing system for an ultra-high contrast glass-based display screen according to claim 1, characterized in that: The specific steps for obtaining the integration requirements of the optical control layer from the final anti-reflection layer parameters, adjusting the matching degree between the control layer and the electrode network through an iterative optimization algorithm, and determining whether the matching degree improves the contrast under high brightness display are as follows: Based on the top-layer flatness requirements defined by the final anti-reflection layer parameters, the maximum allowable thickness gradient of the optical control layer is set. A genetic algorithm was used to jointly optimize the liquid crystal orientation angle, phase retardation, and driving voltage-transmittance curve of the control layer. The objective function was the difference between maximizing the transmittance in the bright state and minimizing the light leakage in the dark state. Each generation of individuals was subjected to electromagnetic field coupling simulation with a fixed electrode network, and contrast values ​​were extracted. The iteration terminates when the contrast improvement of the best individual in a series of preset generations is less than the preset contrast improvement threshold. The combination of the current control layer parameters and electrode layout is then output to form an integrated optical processing system model.

7. The optical processing system for an ultra-high contrast glass-based display screen according to claim 1, characterized in that: Based on the integrated optical processing system model, the incident light response in the non-operating state is simulated. The Monte Carlo method is used to trace the light path to evaluate the transmission uniformity. If the evaluation result meets the consistency of pure glass, verification data is output. The specific steps for determining the optical uniformity index of the system are as follows: Import the integrated optical processing system model into the Monte Carlo ray tracing engine, and set a sufficient amount of random incident light rays with wavelengths conforming to the standard illuminator distribution; Record the exit direction, phase delay, and energy attenuation of each ray after it passes through the system, and statistically analyze the spatial distribution variance and chromaticity coordinate dispersion of the transmitted light. The variance and dispersion are compared with the reference values ​​of ordinary float glass. If the deviations of both are less than the preset threshold that the human eye can distinguish, the verification data is output. Based on the verification data, the haze value, the full field-of-view transmission uniformity index, and the polarization independence coefficient are calculated to determine the optical uniformity index of the system.

8. The optical processing system for an ultra-high contrast glass-based display screen according to claim 1, characterized in that: Feedback data is obtained from the optical uniformity index of the system to further refine the conductivity and anti-reflection layer configuration. The simulation process is iterated through a closed-loop adjustment mechanism to determine whether the overall performance achieves seamless integration. The specific steps to obtain the optimized optical processing scheme for the glass-based display screen are as follows: Extract sub-items that exceed the tolerance in the optical uniformity index and back-map them to the electrode line edge steepness or anti-reflection layer interface roughness parameter. Apply a preset parameter perturbation range to the out-of-tolerance parameters, and re-execute steps S2 to S6 to generate a new set of performance data; Pareto front analysis was used to screen for the solution that achieves the optimal balance between contrast and transparency. The loop stops when the difference in overall performance between two consecutive iterations is less than the preset threshold for overall performance difference, and the current solution is output as the optimized optical processing solution for the glass-based display screen.

9. The optical processing system for an ultra-high contrast glass-based display screen according to claim 1, characterized in that: Based on the optimized optical processing scheme for glass-based displays, and combined with manufacturing process constraints and a material optical constant database, the specific steps for generating a set of structural parameters and interlayer stacking sequence specifications suitable for mass production are as follows: The electrode linewidth, thickness of each sublayer of the antireflection layer, and phase delay of the control layer in the optimization scheme are converted into photolithography mask design rules, sputtering deposition time parameters, and liquid crystal injection pressure settings. The refractive index-wavelength curves at the corresponding process temperature in the material database are used to correct the dispersion difference between the simulation model and the actual material. The maximum residual stress limit of each functional layer is set according to the coefficient of thermal expansion of the glass substrate, and the coating thickness of the interlayer adhesion promoter is adjusted. The output includes a complete process package containing tolerance zones, deposition sequence, annealing curves, and online inspection points, forming a set of structural parameters and interlayer stacking sequence specifications.

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