A full-automatic demura parameter-adjusting compensation optimization method and system

Abstract

The application provides a full-automatic Demura parameter adjustment compensation optimization method and system, and the method comprises the following steps: step 1, collecting screen brightness data to be compensated; step 2, generating compensation data through a data protocol provided by an IC end, and writing the generated compensation data into a screen FLash; step 3, collecting brightness data of the screen after compensation; step 4, evaluating a Mura evaluation system, if the evaluation is passed, the full-automatic Demura parameter adjustment is ended, if the evaluation is not passed, step 5 is executed; step 5, full-automatic parameter adjustment system training; step 6, updating Demura system parameters; and step 7, updating the screen brightness data to be compensated, and then returning to step 2. The application has the beneficial effects that: the application realizes a closed loop of debugging, reduces Demura system debugging time, improves Demura compensation yield, and greatly reduces workshop production cost.

Description

Technical Field

[0001] This invention relates to the field of display panel technology, and in particular to a fully automatic Demura parameter tuning compensation optimization method and system. Background Technology

[0002] With the continuous development of information display technology, the market demand for display screens such as LCD, AMOLED, and MiniLED is constantly increasing, posing greater production challenges to corresponding panel suppliers. Further optimization of shipment yield and turnaround time has become a primary issue to address. The Demura workstation significantly improves the display quality of screens in the workshop and increases screen production yield. The main processes of the equipment are as follows: high-precision industrial cameras are used to collect corresponding brightness data of the screen, the Demura algorithm system calculates compensation values, and a signal transmitter (PG) is used to burn the data into the screen's Flash memory. However, due to the variety of panel types produced by panel manufacturers, such as curved screens, cutout screens, and waterdrop screens, the display quality of different screen types is limited by processes, procedures, and raw materials, resulting in varying display quality. Given the differences in actual Demura models and their IC algorithm libraries, a fast and efficient Demura switching and optimization solution is extremely important. Summary of the Invention

[0003] This invention provides a fully automated Demura parameter tuning and compensation optimization method to solve problems such as rapid product changeover, large differences, and complex debugging processes. By collecting screen brightness data before and after compensation, quantitatively evaluating the Mura in the brightness data, and solving the parameters using the gradient descent method, it realizes an automated process for Mura compensation debugging for different models and displays, improving the efficiency of changeover and debugging processes.

[0004] This invention provides a fully automated Demura parameter tuning and compensation optimization method, comprising the following steps:

[0005] Step 1: Collect the brightness data of the screen to be compensated; based on the information of the screen to be repaired, generate the corresponding positioning map and its grayscale map, collect the corresponding image, and use the Demura algorithm system to realize R, G, B sub-pixel positioning and sub-pixel brightness extraction.

[0006] Step 2: Generate compensation data using the data protocol provided by the IC and write the generated compensation data into the screen FLASH.

[0007] Step 3: Collect the brightness data of the compensated screen.

[0008] Step 4: The Mura evaluation system performs a quantitative evaluation of the brightness data in Step 3. If the evaluation passes, the fully automatic Demura parameter tuning ends; if the evaluation fails, proceed to Step 5.

[0009] Step 5: Training with the fully automated parameter tuning system.

[0010] Step 6: Update Demura system parameters.

[0011] Step 7: Update the screen brightness data to be compensated, then return to step 2.

[0012] As a further improvement of the present invention, step 1 further includes:

[0013] Step S1: Generate the corresponding positioning map and its grayscale map according to the IC algorithm module requirements and the screen information to be repaired.

[0014] Step S2: Use an industrial camera to acquire the corresponding positioning image and its grayscale image.

[0015] Step S3: Based on the Demura-related parameters, the R, G, and B sub-pixel level positioning is achieved through the Demura algorithm model. Then, based on the imaging principle and the PSF model, deconvolution operation is performed to obtain the R, G, and B sub-pixel brightness data.

[0016] As a further improvement of the present invention, in step S1, the screen information to be repaired includes R, G, B logical resolution, PG image resolution, and SPR mapping relationship; in step S3, the Demura-related parameters include resolution and screen type.

[0017] As a further improvement of the present invention, in step 2, a corresponding compensation data bin file is generated through the data protocol provided by the IC terminal, and the compensation data bin file is written into the screen FLASH through the signal transmitter PG and hardware communication protocol.

[0018] As a further improvement of the present invention, in step 4, the Mura evaluation system is evaluated by the SEMU model. The Mura evaluation system includes a Mura segmentation algorithm module, a Mura contrast algorithm module, and a visual nonlinear mapping algorithm module.

[0019] As a further improvement of the present invention, step 4 further includes:

[0020] Step 40: In the SEMU model, the relationship between the contrast of the Mura defect when it is just identifiable and the defect area is expressed as:

[0021] Cjnd=F(Sjnd)=1.97 / Sjnd 0.33 +0.72 (1)

[0022] Where Cjnd represents the contrast at JND, and Sjnd represents the area of ​​the Mura defect at the JND contrast.

[0023] According to formula (1), we can obtain:

[0024]

[0025] Where |Cx| represents the average contrast of the measured Mura defect, Semu is the grade of the Mura defect, and Sx represents the area of ​​the Mura defect;

[0026] Step 41: Based on the sample test calibration, correct the model results from Step 30 against the production and shipping standards.

[0027] As a further improvement of the present invention, step 40 further includes:

[0028] Step a, Gaussian filtering is used to reduce noise in the data, and the formula is as follows:

[0029]

[0030] Step b: Perform mean filtering on the data from step a to obtain the background model and obtain LumBg.

[0031] Step c yields |Cx| defined as follows:

[0032] |Cx|=|lum-LumBg| / Lum (4)

[0033] Where lum represents the brightness data of the corresponding pixel, and LumBg represents the brightness data of the background model.

[0034] As a further improvement of the present invention, in step 5, the evaluation result of step 4 is used as the objective function and the data processing module is used as the independent variable to establish a minimization objective optimization problem. The gradient descent method is used to iteratively solve the problem and obtain the current optimal feasible solution, which is the list of algorithm parameters corresponding to the minimum JND.

[0035] As a further improvement of the present invention, in step 5, the data processing module includes a color shift adjustment algorithm, a stripe suppression algorithm, a moiré pattern adjustment algorithm, and a moiré pattern adjustment algorithm.

[0036] The present invention provides a fully automatic Demura parameter tuning compensation optimization system, including a memory, a processor, and a computer program stored in the memory, the computer program being configured to implement the steps of the method described in the present invention when invoked by the processor.

[0037] The beneficial effects of this invention are: the fully automatic Demura parameter tuning compensation optimization method of this invention, by collecting the brightness data after compensation, establishes a Mura evaluation algorithm module system, realizes the negative feedback loop in the Demura debugging process, realizes the debugging closed loop, reduces the Demura system debugging time, improves the Demura compensation yield, and greatly reduces the workshop production cost. Attached Figure Description

[0038] Figure 1 This is a flowchart of the fully automated Demura parameter tuning and compensation optimization method of the present invention;

[0039] Figure 2 This is a schematic diagram of the screen brightness data to be compensated according to the present invention;

[0040] Figure 3 This is a schematic diagram of the screen brightness data after compensation according to the present invention;

[0041] Figure 4 This is a diagram showing the equipment setup structure of the fully automated Demura parameter tuning and compensation optimization method of this invention;

[0042] Figure 5 This is the convergence graph of the fully automatic Demura parameter tuning and compensation optimization method of this invention.

[0043] Attached diagram labels: 1-Illumination system, 2-CCD camera, 3-Image acquisition card, 4-Industrial computer, 5-Control mechanism, 6-Target being detected Detailed Implementation

[0044] like Figure 4 As shown, the device of the present invention is constructed from 1 a lighting system, 2 a CCD camera, 3 an image acquisition card, 4 an industrial computer, and 5 a control mechanism, wherein:

[0045] 1. Illumination System: Ambient light applied to the target being detected, improving the detection accuracy of the algorithm.

[0046] 2. CCD camera: A data acquisition device used to capture images of the target being detected.

[0047] 3. Image acquisition card: Transmits the data acquired by the camera to the computer.

[0048] 4. Industrial computers: run software and algorithms.

[0049] 5. Control mechanism: Automated equipment.

[0050] like Figure 1 As shown, this invention discloses a fully automated Demura parameter tuning and compensation optimization method, comprising the following steps:

[0051] Step 1: Collect brightness data of the screen to be compensated; based on the screen information (such as resolution, SPR, conversion, etc.), generate the corresponding positioning map and its grayscale map. Use an industrial camera (preferably VieWorks 151M) to acquire the corresponding images, and use the Demura algorithm system to achieve R, G, B sub-pixel positioning and sub-pixel brightness extraction. (Note: SPR: Sub-Pixel rendering)

[0052] The brightness data of the screen to be compensated collected in this invention includes, but is not limited to, flexible screens, punch-hole screens, curved screens, waterfall screens, AMOLED screens, LCD screens, and MINILED screens.

[0053] Step 1 also includes:

[0054] Step S1: Based on the IC algorithm module requirements and the screen information to be repaired (such as R, G, B logical resolutions and their PG mapping resolution, SPR mapping relationship, etc.), generate the corresponding positioning map and its grayscale map. Common grayscale sequences to be captured are shown below:

[0055]

[0056] The G1647FP104 product model uses Real mode for optical data acquisition, which does not require SPR mapping, as shown below:

[0057]

[0058] Step S2: Use a high-resolution industrial camera (preferably Vieworks 151M) to acquire the corresponding positioning map and its grayscale image;

[0059] Step S3: Based on Demura (defect compensation, filtering) related parameters, such as resolution and screen type, the Demura algorithm model is used to achieve R, G, and B sub-pixel level positioning. Then, considering the imaging principle, a series of operations such as deconvolution are performed based on the PSF (point spread function) model to obtain R, G, and B sub-pixel brightness data.

[0060] Step 2: Generate compensation data using the data protocol provided by the IC and write the generated compensation data into the screen FLASH.

[0061] The corresponding compensation data bin file is generated based on the data protocol provided by the IC, and then burned into the screen via the signal transmitter PG. For different data definition protocols provided by various IC manufacturers such as Synaptics and Yunyinggu, the brightness data is converted according to the protocol to obtain the compensation data for the screen. Finally, the compensation data is written into the screen FLASH via the signal transmitter PG and hardware communication protocol.

[0062] Step 3: Acquire the brightness data of the compensated screen. After the screen display defects are improved through programming, the brightness data is obtained by secondary sampling using an industrial camera and the Demura algorithm. The acquisition of compensated brightness data includes a negative feedback mechanism in the debugging process, enabling automation and efficiency in the debugging process.

[0063] Step 4 involves evaluating the Mura evaluation system using the SEMU model proposed by SEMI (Semiconductor Equipment and Materials International). This evaluation includes algorithm modules for Mura segmentation, Mura contrast, and visual nonlinear mapping. If the evaluation passes, the fully automated Demura parameter tuning ends; otherwise, step 5 is executed. The evaluation method references the SEMU standard (SEMI has established a set of measurement and quantitative evaluation standards for display Mura defects).

[0064] Step 4 also includes:

[0065] Step 40: In the SEMU model, the relationship between the contrast of the Mura defect under the condition of just noticeable difference (JND) and the defect area can be expressed as:

[0066] Cjnd=F(Sjnd)=1.97 / Sjnd 0.33 +0.72 (1)

[0067] The symbols in the formula are explained as follows:

[0068]

[0069] JND is a commonly used concept in biology. It can be defined as the smallest difference in brightness that humans can perceive under a certain sensor input. It can also be understood as threshold difference or differential threshold.

[0070] According to formula (1), we can obtain:

[0071]

[0072]

[0073] Where |Cx| is calculated as follows:

[0074] Step a, Gaussian filtering is used to reduce noise in the data, and the formula is as follows:

[0075]

[0076] This is a two-dimensional Gaussian function formula used for low-pass filtering; Let σ represent the amplitude, σ represent the standard deviation, and x and y represent the coordinates. Here, the center coordinates are defined as (0,0).

[0077] Step b: Apply mean filtering to the data from step a to obtain the background model, resulting in LumBg; Step c: Obtain |Cx| as defined below:

[0078] |Cx|=|lum-LumBg| / Lum (4)

[0079]

[0080] Step 41: Based on extensive sample testing and calibration, correct the model results from Step 30 against the production and shipping standards. The shipping standard is generally JND < 2.1. The relationship between Mura and JND is as follows:

[0081]

[0082] Step 5: Training with the fully automated parameter tuning system.

[0083] Using the evaluation results from step four as the objective function, and parameters from data processing modules such as color shift adjustment algorithms, stripe suppression algorithms, moiré pattern adjustment algorithms, and data reconstruction algorithms as independent variables, a minimization objective optimization problem is established. Gradient descent is used for iterative solution, and the current optimal feasible solution is the list of algorithm parameters corresponding to the minimum JND. The solution process is briefly described below:

[0084] According to Taylor's formula:

[0085]

[0086] Wherein, f(x) represents the Mura quantization evaluation result. The independent variables x are the color shift adjustment algorithm, S / G direction adjustment algorithm, and moiré pattern adjustment algorithm in the Demura algorithm model. Some parameters are shown below:

[0087]

[0088] By adopting an evaluation result-oriented approach and establishing a minimum objective optimization problem, the complexity of the debugging work for workshop production staff is reduced.

[0089] Step 6: Update Demura system parameters.

[0090] Step 7: Update the screen brightness data to be compensated, then return to step 2.

[0091] The present invention also discloses a fully automatic Demura parameter tuning compensation optimization system, including a memory, a processor, and a computer program stored in the memory, the computer program being configured to implement the steps of the method described in the present invention when invoked by the processor.

[0092] The beneficial effects of this invention are: the fully automatic Demura parameter tuning compensation optimization method of this invention, by collecting the brightness data after compensation, establishes a Mura evaluation algorithm module system, realizes the negative feedback loop in the Demura debugging process, realizes the debugging closed loop, reduces the Demura system debugging time, improves the Demura compensation yield, and greatly reduces the workshop production cost.

[0093] The above description, in conjunction with specific preferred embodiments, provides a further detailed explanation of the present invention. It should not be construed that the specific implementation of the present invention is limited to these descriptions. For those skilled in the art, various simple deductions or substitutions can be made without departing from the concept of the present invention, and all such modifications and substitutions should be considered within the scope of protection of the present invention.

Claims

1. A full-automatic Demura parameter-adjusting compensation optimization method, characterized in that, Comprising the following steps: Step 1, collecting the screen brightness data to be compensated; generating corresponding positioning maps and gray scale maps according to the screen information to be repaired, collecting corresponding images, and realizing R, G, and B sub-pixel positioning and sub-pixel brightness extraction through a Demura algorithm system; Step 2, generating compensation data through a data protocol provided by an IC end, and writing the generated compensation data into a screen FLash; Step 3, collecting the brightness data of the screen after compensation; Step 4, quantitatively evaluating the brightness data in step 3 by a Mura evaluation system, and if the evaluation is passed, ending the automatic Demura parameter adjustment, and if the evaluation is not passed, executing step 5; Step 5, training the automatic parameter adjustment system; Step 6, updating the Demura system parameters; Step 7, updating the screen brightness data to be compensated, and then returning to step 2; In the step 1, further comprising: Step S1, generating corresponding positioning maps and gray scale maps according to the IC algorithm module requirements and the screen information to be repaired; Step S2, collecting the corresponding positioning maps and gray scale maps by using an industrial camera; Step S3, realizing R, G, and B sub-pixel positioning through a Demura algorithm model according to Demura related parameters, and then obtaining R, G, and B sub-pixel brightness data by performing inverse convolution operation according to a PSF model through imaging principle.

2. The full-automatic Demura parameter-adjusting compensation optimization method according to claim 1, characterized in that: In the step S1, the screen information to be repaired includes R, G, and B logical resolution, PG mapping resolution, and SPR mapping relationship; in the step S3, the Demura related parameters include resolution and screen type.

3. The full-automatic Demura parameter-adjusting compensation optimization method according to claim 1, characterized in that, In the step 2, generating corresponding compensation data bin files through a data protocol provided by an IC end, and writing the compensation data bin files into a screen FLash through a signal transmitter PG and a hardware communication protocol.

4. The full-automatic Demura parameter-adjusting compensation optimization method according to claim 1, characterized in that: In the step 4, evaluating the Mura evaluation system by a SEMU model, and the Mura evaluation system includes a Mura segmentation algorithm module, a Mura contrast algorithm module, and a visual non-linear mapping algorithm module.

5. The full-automatic Demura parameter-adjusting compensation optimization method according to claim 4, characterized in that, In the step 4, further comprising: Step 40, in the SEMU model, expressing the relationship between the contrast of Mura defects at just identifiable conditions and the area of the defects as: Cjnd = F(Sjnd) = 1.97 / Sjnd 0.33 +0.72 (1) Wherein, Cjnd represents the contrast at JND, and Sjnd represents the area of the Mura defects at JND contrast; According to formula (1), the following can be obtained: Wherein, |Cx| represents the average contrast of the measured Mura defects, Semu is the grade of the Mura defects, and Sx represents the area of the Mura defects; Step 41, correcting the model results of step 30 and the production shipment standard according to sample test calibration.

6. The full-automatic Demura parameter-adjusting compensation optimization method according to claim 5, characterized in that, In the step 40, further comprising: Step a, adopting Gaussian filtering to reduce noise of the data, and the formula is as follows: Step b, performing mean filtering on the data of step a to obtain a background model and obtain LumBg; Step c, obtaining |Cx| is defined as follows: |Cx| = |lum-LumBg| / Lum (4) Wherein, lum represents the brightness data of the corresponding pixel point, and LumBg represents the brightness data of the background model.

7. The full-automatic Demura parameter-adjusting compensation optimization method according to claim 6, characterized in that, In the step 5, a minimization target optimization problem is established with the evaluation result of the step 4 as a target function and the data processing module as an independent variable, a gradient descent method is used for iterative solution, and a current optimal feasible solution is obtained, which is a parameter list corresponding to the minimum JND.

8. The full-automatic Demura parameter-adjusting compensation optimization method according to claim 7, characterized in that, In the step 5, the data processing module includes a color offset debugging algorithm, a stripe suppression algorithm, a moire debugging algorithm, a data reconstruction algorithm and the like.

9. A full-automatic Demura parameter-adjusting compensation optimization system, characterized in that, A computer program product comprising a memory, a processor and a computer program stored on the memory, the computer program being configured to implement the steps of the method of any one of claims 1-8 when called by the processor.

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