Pixel Addressing Method and Device for Ultra-High Pixel Density Micro-LED Microdisplay Device

Abstract

The present invention relates to a pixel addressing method and device for a micro-LED microdisplay device with ultra-high pixel density. The method includes: collecting compensation parameters for the pixel array structure parameters of the micro-LED microdisplay device to obtain pixel compensation reference values; constructing a compensation network for the threshold voltage deviation distribution characteristics to obtain a threshold voltage compensation network; performing segmented design on the power supply metal traces to obtain IR Drop compensation parameters; performing process compensation calculation through a Markov decision inference model with a double-layer compensation factor to obtain process compensation data; performing timing optimization for addressing control to obtain row and column scan signal timing parameters; performing optoelectronic characteristic tests on the row and column scan signal timing parameters, collecting display uniformity data, and obtaining target compensation configuration parameters through compensation parameter evaluation. The implementation of the present invention ensures the stability of the driving timing while reducing the number of chip pins.

Description

Technical Field

[0001] The present invention relates to the field of Micro-LED technology, and particularly to a pixel addressing method and device for a Micro-LED micro-display device with ultra-high pixel density. Background Art

[0002] With the rapid development of display technology, Micro-LED micro-display devices with ultra-high pixel density show broad application prospects in fields such as AR / VR and smart wearables due to their advantages of high brightness, high contrast, and low power consumption. However, in practical applications, due to the non-uniformity of device manufacturing processes and the parasitic effects of driving circuits, there are obvious brightness non-uniformity problems in display devices, seriously affecting the display quality.

[0003] Current mainstream pixel addressing schemes mainly adopt non-compensation or single-compensation strategies, and cannot effectively cope with the influence of complex factors such as process fluctuations and power supply network voltage drops. Especially in the ultra-high pixel density scenario, traditional compensation methods are difficult to accurately capture the spatial distribution characteristics of device characteristic deviations, and the compensation strategy lacks self-adaptability, making it difficult for the compensation effect to meet the requirements of high-quality displays. Summary of the Invention

[0004] The main object of the present invention is to provide a pixel addressing method and device for a Micro-LED micro-display device with ultra-high pixel density. The present invention ensures the stability of the driving timing while reducing the number of chip pins.

[0005] To achieve the above object, the present invention provides a pixel addressing method for a Micro-LED micro-display device with ultra-high pixel density, including the following steps:

[0006] Collect compensation parameters for the pixel array structure parameters of the Micro-LED micro-display device, establish a 2T1C driving circuit compensation model, store the threshold voltage, mobility, and parasitic capacitance parameters in the compensation parameter database, and obtain the pixel compensation reference value;

[0007] Construct a compensation network for the threshold voltage deviation distribution characteristics according to the compensation parameter database and the pixel compensation reference value to obtain a threshold voltage compensation network;

[0008] Based on the threshold voltage compensation network, perform segmented design on the power supply metal traces to obtain IR Drop compensation parameters;

[0009] Input the IR Drop compensation parameters into a Markov decision inference model with a double-layer compensation factor for process compensation calculation to obtain process compensation data;

[0010] Perform timing optimization for addressing control based on the process compensation data, and adopt a dynamic time slot allocation scheme for time-division multiplexing control to obtain the timing parameters of the row-column scanning signals;

[0011] Perform optoelectronic characteristic tests on the timing parameters of the row-column scanning signals, collect display uniformity data, and obtain target compensation configuration parameters through compensation parameter evaluation.

[0012] The present invention also provides a pixel addressing device for a super-high pixel density Micro-LED microdisplay device, including:

[0013] An acquisition module, configured to collect compensation parameters for the pixel array structure parameters of the Micro-LED microdisplay device, establish a 2T1C drive circuit compensation model, store threshold voltage, mobility, and parasitic capacitance parameters in a compensation parameter database, and obtain a pixel compensation reference value;

[0014] A construction module, configured to construct a compensation network for the threshold voltage deviation distribution characteristics according to the compensation parameter database and the pixel compensation reference value to obtain a threshold voltage compensation network;

[0015] A segmentation module, configured to perform segmented design on the power supply metal traces based on the threshold voltage compensation network to obtain IR Drop compensation parameters;

[0016] A compensation module, configured to input the IR Drop compensation parameters into a Markov decision inference model with a double-layer compensation factor for process compensation calculation to obtain process compensation data;

[0017] A control module, configured to perform timing optimization for addressing control based on the process compensation data, and adopt a dynamic time slot allocation scheme for time-division multiplexing control to obtain the timing parameters of the row-column scanning signals;

[0018] A test module, configured to perform optoelectronic characteristic tests on the timing parameters of the row-column scanning signals, collect display uniformity data, and obtain target compensation configuration parameters through compensation parameter evaluation.

[0019] The present invention also provides a pixel addressing system for a super-high pixel density Micro-LED microdisplay device, on which a computer program is stored, and when the computer program is executed by a processor, the steps of the method described in any one of the above are implemented.

[0020] In summary, the technical solution provided by the present invention realizes the accurate acquisition and quantitative analysis of the characteristic parameters of pixel units by establishing a 2T1C drive circuit compensation model and introducing a weighted calculation mechanism, ensuring the accuracy and representativeness of the compensation reference value. The differential structure compensation branch design and iterative optimization strategy are adopted to effectively reduce the influence of threshold voltage deviation, and the display uniformity error is controlled within a small range. The IR Drop compensation scheme based on the dynamic prediction-feedback structure accurately grasps the spatio-temporal distribution characteristics of the power supply network voltage drop, effectively suppressing the display distortion caused by uneven power supply. An innovative Markov decision inference model with double-layer compensation factors is proposed, and the correlation mapping between device characteristics and process parameters is established through a coupling coefficient matrix, making the compensation strategy have strong self-adaptability. The time-division multiplexing control scheme with dynamic time slot allocation optimizes the configuration of data writing and storage time, ensuring the stability of the drive timing while reducing the number of chip pins. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 is a schematic diagram of the steps of a pixel addressing method for a super-high pixel density Micro-LED microdisplay device in an embodiment of the present invention;

[0022] Figure 2 is a block diagram of the structure of a pixel addressing device for a super-high pixel density Micro-LED microdisplay device in an embodiment of the present invention.

[0023] The realization, functional features and advantages of the objectives of the present invention will be further described with reference to the embodiments and the accompanying drawings. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0024] In order to make the objectives, technical solutions and advantages of the present invention clearer, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0025] Referring to Figure 1 , this embodiment provides a pixel addressing method for a super-high pixel density Micro-LED microdisplay device, including the following steps:

[0026] S1. Collect compensation parameters for the pixel array structure parameters of the Micro-LED microdisplay device, establish a 2T1C drive circuit compensation model, store the threshold voltage, mobility and parasitic capacitance parameters in the compensation parameter database, and obtain the pixel compensation reference value;

[0027] Among them, data collection is carried out on the physical characteristics of the pixel array, including structural parameters such as the size of pixels, the pitch between pixels, and the specific arrangement of pixels. These parameters are obtained through high-precision measurement tools to ensure the accuracy and comprehensiveness of the data. Analyze the structural parameters of the pixel array to construct a compensation model for the driving circuit based on the 2T1C structure. The 2T1C driving circuit consists of a switching transistor T1, a driving transistor T2, and a storage capacitor C. The function of the switching transistor T1 is to control the on and off of the signal, the driving transistor T2 is used to adjust the driving voltage of the pixel, and the storage capacitor C is responsible for maintaining the voltage stability of the pixel unit. During the construction of this model, consider the actual physical size of the pixel unit and the electrical interconnection characteristics between pixels to establish an equivalent compensation model that can accurately reflect the actual working conditions. Measure the parameters of a single pixel unit in the 2T1C driving circuit. Measure the threshold voltage of the driving transistor by measuring the critical voltage value when the transistor is turned on. At the same time, analyze the electrical characteristics of the pixel unit to obtain the mobility parameter. Mobility is an important indicator describing the carrier movement speed of the transistor and is calculated by testing the output characteristic curve of the transistor under different voltage conditions. And perform equivalent circuit modeling on a single pixel unit and measure its parasitic capacitance parameter on this basis. Parasitic capacitance is caused by the internal structure of the pixel unit and the electrical coupling effect with adjacent units, and its size will directly affect the dynamic response speed of the pixel and the uniformity of the display. Through the accurate establishment of the equivalent circuit model, accurate parasitic capacitance parameters are obtained and provide an important basis for the compensation model. Input the threshold voltage parameter, mobility parameter, and parasitic capacitance parameter into the compensation parameter database for storage. Perform weighted calculation on the data in the compensation parameter database to fully consider the characteristic differences of pixels in different positions in the pixel array. Dynamically adjust the weight coefficient according to the pixel position to form a weighted coefficient matrix for weighted average operation of the compensation parameters. The adjustment of the weight coefficient is based on the position characteristics of the pixels. Through the weighted average operation based on the weighted coefficient matrix, the pixel compensation reference value is obtained.

[0028] S2. Construct a compensation network for the threshold voltage deviation distribution characteristics according to the compensation parameter database and the pixel compensation reference value to obtain a threshold voltage compensation network;

[0029] Specifically, perform difference analysis on the threshold voltage parameters in the compensation parameter database. By extracting the threshold voltage data of each pixel recorded in the database and combining with the reference value, calculate the deviation distribution characteristics of each pixel. Through data processing and statistical analysis, draw a threshold voltage deviation distribution map to show the spatial distribution characteristics of the voltage deviation of each pixel in the entire pixel array. Plan the compensation circuit based on the threshold voltage deviation distribution map. By analyzing the distribution law of the voltage deviation, design the layout of the differential structure compensation branch so that the distribution of the compensation branch adapts to the voltage deviation characteristics. The differential structure compensation branch adjusts the actual pixel driving voltage to the ideal state by comparing and correcting the deviation value. In the layout planning stage, consider the spatial distribution of the pixel array and the hardware implementation difficulty of the compensation circuit to ensure the feasibility and effectiveness of the design. Design the circuit of the differential structure compensation branch. Construct a compensation branch including a differential amplifier, a sample and hold circuit, and a comparator. The function of the differential amplifier is to detect and amplify the voltage deviation signal between pixels so that the subsequent circuit can accurately process these signals. The sample and hold circuit is used to capture and maintain the instantaneous voltage signal and provide stable input data for the compensation circuit. The function of the comparator is to compare the amplified signal with the reference signal and generate a compensation control signal. When designing these core circuit components, calculate the circuit device parameters according to the layout of the compensation branch, such as the capacitance value of the compensation capacitor, the size of the compensation transistor, and the resistance value of the feedback resistor. The calculation of these parameters needs to comprehensively consider the electrical characteristics of the pixel array, the compensation target, and the circuit working environment to ensure the efficient operation of the compensation circuit. Input the capacitance value of the compensation capacitor, the size of the compensation transistor, and the resistance value of the feedback resistor into the timing simulation model to analyze the dynamic response characteristics of the compensation circuit. The timing simulation model evaluates the performance of the circuit under actual working conditions, including the response speed, stability, and compensation accuracy for the deviation signal, by simulating the dynamic behavior of the compensation circuit under different input signals. Through the simulation results, verify whether the currently designed compensation circuit can meet the expected requirements. Based on the pixel compensation reference value, analyze the compensation effect of the circuit response characteristics and evaluate the actual uniformity compensation ability of the compensation circuit. By comparing the compensated voltage distribution with the target reference value, calculate the uniformity compensation error data, which reflects the actual correction effect of the compensation network in each pixel area and provides a clear direction for subsequent optimization. According to the uniformity compensation error data, iteratively optimize the design parameters of the compensation branch, such as adjusting the gain of the differential amplifier, modifying the resistance value of the feedback resistor, or optimizing the capacitance value of the compensation capacitor. Through multiple optimization iterations, gradually reduce the compensation error until the compensation network can control the voltage deviation of the pixel array within the allowable range and achieve the target uniformity. Obtain a high-precision threshold voltage compensation network.

[0030] S3. Based on the threshold voltage compensation network, perform segmented design on the power supply metal trace to obtain the IR Drop compensation parameter;

[0031] It should be noted that resistance distribution simulation is performed on the power supply metal traces based on the threshold voltage compensation network. During the simulation process, by analyzing the material properties, geometric shape, and connection relationship with the pixel array of the power supply metal traces, the equivalent resistance distribution data of the traces is calculated, thereby reflecting the resistance change conditions at different positions. Based on the equivalent resistance distribution data, a voltage drop model of the power supply network is constructed. This model calculates the voltage drop values corresponding to pixels at different positions in the pixel array by considering the current distribution and resistance characteristics in the traces. The metal traces are segmented according to the voltage drop values. According to the distribution characteristics of the voltage drop, the traces are divided into several regions with similar electrical characteristics for more precise compensation design within each segment. At the same time, the width and wiring layer of each segment of the traces are optimized. The optimization of the width comprehensively considers the resistance of the traces and manufacturing process limitations to reduce the voltage drop while minimizing the occupied area of the traces. The design of the wiring layer considers the interconnection effect and parasitic characteristics of multi-layer wiring, thereby improving the signal integrity and power supply efficiency while reducing the voltage drop. Through these design steps, a segmented layout diagram of the metal traces is generated, reflecting the specific structure of the segmented power supply network. A voltage detection circuit is designed for each segmented area in the segmented layout diagram of the metal traces to construct a real-time voltage monitoring network. The real-time voltage monitoring network embeds the detection circuit to collect the voltage signals of each segmented area in real time, facilitating the monitoring of the voltage state of the power supply network at any time. These detection circuits include high-precision voltage sensors and signal processing units. By transmitting the detected voltage signals to the central control unit, real-time monitoring of the dynamic behavior of the power supply network is achieved. Through the real-time monitoring mechanism, the details of voltage changes are effectively captured. Historical voltage drop data is collected based on the real-time voltage monitoring network, and trend analysis is performed on the historical voltage drop data to construct a voltage drop prediction model. By comprehensively analyzing the time correlation and spatial distribution characteristics in the historical data, appropriate algorithms (such as Markov models or machine learning methods) are used to predict the future voltage drop change trend. Through this prediction mechanism, potential problem areas can be identified in advance. The voltage drop prediction model is input into the prediction-feedback compensation unit to perform dynamic compensation calculations. The compensation unit combines the prediction model and real-time monitoring data to calculate the compensation voltage value required for each segmented area. The calculated compensation voltage value is implemented through a drive circuit to adjust the power supply voltage in real time and offset the impact of the voltage drop on pixel driving. At the same time, to verify the compensation effect, verification tests are performed on the compensation voltage value. By monitoring and comparing the voltage state after compensation, the actual compensation effect is evaluated. If the compensation effect does not meet the requirements, the parameters of the compensation unit are optimized to gradually improve the compensation performance, and finally the optimized IRDrop compensation parameters are obtained.

[0032] S4. Input the IR Drop compensation parameters into the Markov decision inference model with double-layer compensation factors for process compensation calculation to obtain process compensation data;

[0033] Specifically, through the measurement of device characteristic deviation parameters, key data including threshold voltage deviation, channel length deviation, and oxide layer thickness deviation are obtained. These data reflect the deviations generated by pixel units due to material characteristics or process fluctuations during the actual manufacturing process. Feature extraction is performed on the device characteristic deviation parameters. By analyzing their spatial distribution and statistical characteristics, the correlations and influencing factors between different parameters are identified, and a data clustering algorithm is used to classify the parameters to extract the first-layer compensation factors. Based on the first-layer compensation factors, a device compensation feature matrix is constructed to describe the comprehensive impact of device-level deviations on display performance. At the same time, process parameters are obtained, including data such as etching depth, doping concentration, and ion implantation energy. Through the same feature extraction method, the process parameters are analyzed to extract the second-layer compensation factors, which reflect the indirect impact of process fluctuations on device characteristics. According to the second-layer compensation factors, a process compensation feature matrix is constructed to systematically characterize the distribution characteristics and action rules of process changes in the entire pixel array. Based on the device compensation feature matrix and the process compensation feature matrix, correlation analysis and regularization processing of the double-layer compensation factors are carried out to obtain a compensation factor coupling matrix. This matrix integrates the interaction between device characteristic deviations and process parameters and reduces noise interference in the high-dimensional parameter space through regularization technology, making the compensation calculation more stable and reliable. Input the IR Drop compensation parameters and the compensation factor coupling matrix into the Markov decision inference model with double-layer compensation factors. To accurately simulate the fluctuation law of process parameters, the Markov model takes the state transition of process changes as the core and constructs a process fluctuation state transition matrix. This matrix describes the transition probabilities and evolution laws between different process states and provides a decision-making basis for the optimization of dynamic compensation. Use the dynamic programming algorithm to solve the optimal compensation strategy for the process fluctuation state transition matrix, and iteratively calculate the compensation strategy sequence based on the Bellman equation. The Bellman equation decomposes the global optimization problem and transforms it into a series of sub-problems that can be recursively solved to ensure the global optimality of the solution. Perform target interval determination and variance analysis on the compensation strategy sequence to evaluate the actual effects of different compensation strategies. Combining the analysis results, through least squares fitting calculation, compensation optimization parameters are obtained to describe the compensation magnitude required for each pixel under different process conditions. Write the compensation optimization parameters into the compensation lookup table according to the pixel position and compensation magnitude to generate process compensation data.

[0034] S5. Based on the process compensation data, perform timing optimization for addressing control and adopt a dynamic time slot allocation scheme for time-division multiplexing control to obtain the timing parameters of row and column scan signals;

[0035] Among them, perform timing feature analysis on the process compensation data, extract key parameters related to display driving, such as the frequency of the row scanning signal, data writing time, and storage time. These parameters affect the driving accuracy of pixels and the dynamic performance of the display. By combining the deviation correction information in the process compensation data, initial timing configuration data is generated. Based on the initial timing configuration data, a dynamic time slot allocation model is constructed. In this model, by setting the ratio threshold of the data writing time to the storage time, the dynamic adjustment of the display driving time slot is realized, generating time-division multiplexing reference data to ensure the optimal allocation of the driving time for each pixel between compensation and display, while reducing time slot conflicts and improving pixel addressing efficiency. When constructing this model, comprehensively consider the pixel array scale, data transmission rate, and the complexity of process compensation to ensure the practical applicability and computational efficiency of the model. Design the row and column scanning control circuit according to the time-division multiplexing reference data, and complete the synchronization design of the row strobe signal and the column data signal. Through the synchronization control design, ensure the accurate matching of the row and column signals during the scanning process, reducing the possibility of signal delay and error accumulation. Combine the physical characteristics of the row and column signals to generate a scanning timing schematic diagram, showing the distribution and coordination of the row scanning signal and the column data signal on the time axis. Optimize the time slot interval based on the scanning timing schematic diagram. By analyzing the display data characteristics (such as brightness distribution and pixel load differences), dynamically adjust the multiplexing ratio to generate a dynamic time slot allocation scheme. This scheme dynamically allocates the driving time according to the requirements of different pixel regions, improving the brightness uniformity and energy efficiency. Input the dynamic time slot allocation scheme into the timing controller for time-division multiplexing simulation, verify the actual effect of the optimized scheme through simulation, and generate multiplexing control parameters. According to the multiplexing control parameters, perform mapping design on the chip pins to achieve the efficient transmission and allocation of control signals. In this process, by optimizing the data transmission channel, reduce the transmission delay and signal loss, improving the overall driving performance. After completing the pin allocation design, perform timing matching on the data writing and compensation processes based on the allocation results. By precisely adjusting the relative relationship between the writing and compensation times, generate a synchronization control sequence. Optimize the driving timing control of the synchronization control sequence to ensure that the row and column scanning signals can remain stable and efficient under different display conditions. The optimized driving timing can meet the precise driving requirements of high pixel density Micro-LED microdisplay devices, and significantly improve the display uniformity and dynamic performance, generating the final row and column scanning signal timing parameters.

[0036] S6. Perform optoelectronic characteristic tests on the row and column scanning signal timing parameters, collect display uniformity data, and obtain target compensation configuration parameters through compensation parameter evaluation.

[0037] Specifically, drive timing loading is performed on the row and column scan signal timing parameters to establish a test platform including a drive circuit, a signal generator, and an optoelectronic test system. When building the test platform, the test system parameters are configured according to the specific drive characteristics of the Micro-LED microdisplay device to ensure that the drive signal can be accurately loaded into the Micro-LED array and achieve precise measurement of brightness response and electrical performance, obtaining the test system configuration data. Based on the test system configuration data, brightness uniformity scanning is performed on the Micro-LED microdisplay device. The brightness uniformity scanning is realized through the optoelectronic detection unit in the test system to obtain the original brightness distribution data of the Micro-LED pixel array under specific drive conditions. By performing multiple scans at different positions and different time points, the brightness change characteristics of the pixel array are captured to generate the original brightness distribution data including spatial distribution and temporal dynamics. Dual sampling processing in the spatial domain and temporal domain is performed on the original brightness distribution data. Spatial domain sampling is used to analyze whether there are regional deviations in the brightness distribution of the pixel array, while temporal domain sampling captures the stability and consistency of the brightness change over time. Through dual sampling, display uniformity data is obtained. According to the display uniformity data, an evaluation system including static uniformity indicators and dynamic response indicators is constructed to comprehensively quantify the compensation effect. The static uniformity indicators are used to evaluate the brightness consistency of the pixel array under fixed conditions, such as the brightness difference between different pixels; while the dynamic response indicators focus on the response characteristics of the display device to brightness changes during signal driving, such as response time and stability. By combining these two types of indicators, a compensation effect evaluation model is constructed to systematically reflect the advantages and disadvantages of the Micro-LED microdisplay device in terms of optoelectronic performance. The display uniformity data is input into the compensation effect evaluation model for parameter analysis. The evaluation model analyzes the brightness change characteristics of pixels at different positions and different time points to generate compensation effect quantification data, reflecting the specific compensation effect, such as the improvement amplitude of brightness uniformity, the improvement degree of contrast, and the optimization effect of response time. Multidimensional feature analysis is performed on the compensation effect quantification data. An evaluation index matrix including multiple key performance indicators such as brightness uniformity, contrast, and response time is established to obtain the compensation parameter evaluation result. Based on the compensation parameter evaluation result, parameter optimization configuration is performed. Combining the previously analyzed display uniformity data and the evaluation model, the key parameters in the compensation circuit are adjusted, including drive voltage, scan timing, and compensation coefficient, etc., to generate the optimal target compensation configuration parameters.

[0038] In one example, compensation parameter acquisition is performed on the pixel array structure parameters of the Micro-LED microdisplay device, a 2T1C drive circuit compensation model is established, and the threshold voltage, mobility, and parasitic capacitance parameters are stored in the compensation parameter database to obtain the pixel compensation reference value, including:

[0039] Collect data on pixel size, pixel pitch, and pixel arrangement to obtain pixel array structure parameters;

[0040] Perform parameter analysis on the pixel array structure parameters to construct a 2T1C drive circuit compensation model including a switching transistor T1, a driving transistor T2, and a storage capacitor C;

[0041] Measure the parameters of a single pixel unit in the 2T1C drive circuit compensation model to obtain threshold voltage parameters;

[0042] Perform electrical characteristic analysis on a single pixel unit to obtain mobility parameters, and perform equivalent circuit modeling on a single pixel unit to obtain parasitic capacitance parameters;

[0043] Input the threshold voltage parameters, mobility parameters, and parasitic capacitance parameters into the compensation parameter database for storage;

[0044] Perform weighted calculation on the data in the compensation parameter database, where the weight coefficient is dynamically adjusted according to the pixel position to obtain a weighted coefficient matrix, and perform weighted average operation on the compensation parameters based on the weighted coefficient matrix to obtain a pixel compensation reference value.

[0045] In this example, the physical structure parameters of the pixel array are obtained through a high-precision measurement device, including the size of the pixel unit , pixel pitch and the arrangement of pixels. The pixel arrangement is represented as a structure matrix , where each element represents the geometric characteristics of the pixel unit at the corresponding position. Based on these structure parameters, the actual physical layout of the pixel array is understood. Perform parameter analysis on the pixel array structure parameters to construct a 2T1C drive circuit compensation model including a switching transistor , driving transistor and storage capacitor . The current of the switching transistor in this model is expressed as:

[0046] ;

[0047] where is 's mobility, is the capacitance per unit area of the gate oxide layer, and are respectively 's width and length, is the gate-source voltage, is the threshold voltage. The current of the driving transistor is similarly expressed as:

[0048] ;

[0049] where the variables are similar to those defined in , but corresponding to the characteristic parameters of . The voltage holding ability of the storage capacitor is described as:

[0050] ;

[0051] where is the voltage on the storage capacitor, is the stored charge amount, is the storage capacitance value. To ensure the accuracy of the model, the electrical parameters of a single pixel unit in the 2T1C model are measured. The threshold voltage parameter of a single pixel unit is measured. This parameter is a key characteristic of the transistor's on state and is obtained by testing the relationship curve between the gate-source voltage and the drain current. The electrical characteristics of the pixel unit are analyzed, and the mobility is measured. The mobility describes the movement rate of carriers in the transistor, and its calculation formula is:

[0052] ;

[0053] where is the drain-source current, is the capacitance per unit area of the gate oxide layer, and are the width and length of the transistor respectively, is the gate-source voltage, is the threshold voltage. An equivalent circuit model of the pixel unit is built, and the parasitic capacitance is measured. The parasitic capacitance is obtained by analyzing the high-frequency electrical characteristics or measuring the equivalent impedance of the pixel unit using a test device. The parameters , and are input into the compensation parameter database for storage. To comprehensively analyze the compensation parameters of the entire pixel array, the data in the database is weighted, where the weight coefficient is dynamically adjusted according to the pixel position. The weight calculation formula is:

[0054] ;

[0055] where represents the set of compensation parameters for the pixel position , including , and . The weight coefficient Dynamically adjust according to factors such as the edge effect of the pixel position and the signal transmission path length. Based on the weight coefficient matrix , perform a weighted average operation on the compensation parameters to obtain the pixel compensation reference value . The formula for the weighted average is:

[0056] ;

[0057] where is the scale of the pixel array. Through the above formula calculation, the compensation reference value of the entire pixel array is obtained , which contains the analysis results of the comprehensive characteristics of all pixel units

[0058] In an example, construct a compensation network for the threshold voltage deviation distribution characteristics according to the compensation parameter database and the pixel compensation reference value, and obtain the threshold voltage compensation network, including:

[0059] Perform a difference analysis on the threshold voltage parameters in the compensation parameter database to obtain a threshold voltage deviation distribution diagram, and based on the threshold voltage deviation distribution diagram, plan the compensation circuit to obtain the layout of the differential structure compensation branch;

[0060] Design the circuit for the differential structure compensation branch, construct a compensation branch including a differential amplifier, a sample and hold circuit, and a comparator, and calculate the circuit device parameters according to the compensation branch layout to obtain the capacitance value of the compensation capacitor, the size of the compensation transistor, and the resistance value of the feedback resistor;

[0061] Input the capacitance value of the compensation capacitor, the size of the compensation transistor, and the resistance value of the feedback resistor into the timing simulation model to obtain the circuit response characteristics;

[0062] Based on the pixel compensation reference value, analyze the compensation effect of the circuit response characteristics to obtain the uniformity compensation error data, and iteratively optimize the compensation branch parameters according to the uniformity compensation error data to obtain the threshold voltage compensation network.

[0063] In this example, extract the threshold voltage parameters of each pixel unit from the compensation parameter database, defined as , where represents the position of the pixel unit in the array. By calculating the threshold voltage deviation of each pixel, a threshold voltage deviation distribution diagram is obtained. The deviation calculation formula is:

[0064] ;

[0065] where is the average threshold voltage of all pixels in the array, and the calculation formula is:

[0066] ;

[0067] Among them, and are the number of rows and columns of the pixel array respectively. Based on the threshold voltage deviation distribution map, the deviation characteristics of different regions in the pixel array are identified. For example, there is a greater threshold voltage deviation in the edge region, while the center region is relatively uniform. According to these distribution characteristics, the layout of the differential structure compensation branch is planned. The differential compensation realizes the real-time detection and correction of voltage deviation through the circuit. The layout planning divides the pixel array into multiple compensation regions according to the deviation distribution map, and a differential structure compensation branch is designed for each region to handle the threshold voltage deviation of the corresponding region. After completing the layout planning of the compensation branch, the circuit design stage of the differential structure compensation branch is carried out. The compensation branch includes a differential amplifier, a sample and hold circuit, and a comparator. The role of the differential amplifier is to amplify the threshold voltage difference between pixels, and the output voltage The formula for is:

[0068] ;

[0069] Among them is the differential mode gain of the amplifier, and are the voltages of the positive input terminal and the negative input terminal respectively. The sample and hold circuit is used to capture the instantaneous voltage signal during the pixel driving process, and its output voltage is expressed as:

[0070] ;

[0071] Among them is the sample and hold function, which determines the sampling and holding time window. The role of the comparator is to compare the signal output by the differential amplifier with the reference voltage and output a logic signal to trigger the compensation action. The output logic of the comparator is expressed as:

[0072] ;

[0073] To ensure the accuracy of the compensation circuit, the key device parameters are calculated, including the capacitance value of the compensation capacitor, the size of the compensation transistor (width and length ), and the resistance value of the feedback resistor. The calculation formula for the capacitance value of the compensation capacitor is:

[0074] ;

[0075] Among them is the compensation charge amount, is the corresponding voltage change. The size of the compensation transistor is adjusted according to its current driving ability and threshold voltage deviation, and the size parameter is calculated by the following formula:

[0076] ;

[0077] where is the compensation current, is the mobility, is the gate oxide capacitance density, is the gate-source voltage, is the threshold voltage of the transistor. The resistance value of the feedback resistor is determined by the compensation time constant, and the calculation formula is:

[0078] ;

[0079] where is the time constant, is the feedback capacitance. After completing the calculation of the circuit parameters, the capacitance value of the compensation capacitor, the size of the compensation transistor, and the resistance value of the feedback resistor are input into the timing simulation model to analyze the dynamic response characteristics of the compensation circuit. The response characteristics are described by the transfer function of the circuit as:

[0080] ;

[0081] where is the complex frequency variable in the Laplace transform. The simulation results include indicators such as the gain, response time, and stability of the circuit. Based on the pixel compensation reference value, the compensation effect of the circuit response characteristics obtained by simulation is analyzed. By calculating the uniformity compensation error data, the performance of the compensation network is evaluated. The uniformity compensation error is defined as:

[0082] ;

[0083] where represents the root mean square error, and the smaller it is, the better the compensation effect. According to the error data, the parameters of the compensation branch are iteratively optimized, such as adjusting the gain of the differential amplifier , the feedback resistance value or the compensation capacitor , so as to improve the compensation accuracy. After multiple rounds of iterative optimization, a high-precision threshold voltage compensation network is finally obtained. This network can effectively correct the non-uniformity caused by the threshold voltage deviation in the pixel array.

[0084] In one example, based on the threshold voltage compensation network, the power supply metal trace is segmented designed to obtain the IRDrop compensation parameters, including:

[0085] Based on the threshold voltage compensation network, perform a resistance distribution simulation on the power supply metal traces to obtain equivalent resistance distribution data, and construct a power supply network voltage drop model based on the equivalent resistance distribution data to obtain the voltage drop values of pixels at different positions;

[0086] Segment the metal traces according to the voltage drop values, and design the width and wiring layer of each segment of the traces to obtain the segmented layout diagram of the metal traces;

[0087] Design a voltage detection circuit for each segmented area in the segmented layout diagram of the metal traces to obtain a real-time voltage monitoring network;

[0088] Collect historical voltage drop data based on the real-time voltage monitoring network, and perform trend analysis on the historical voltage drop data to obtain a voltage drop prediction model;

[0089] Input the voltage drop prediction model into the prediction-feedback compensation unit for dynamic compensation calculation to obtain the compensation voltage value, and perform verification testing and parameter optimization on the compensation voltage value to obtain the IR Drop compensation parameters.

[0090] In this example, based on the threshold voltage compensation network, a resistance distribution simulation is performed on the power supply metal traces. By establishing the geometric model and material properties of the traces, combined with the physical properties of the scaled-down devices, the equivalent resistance distribution data of the metal traces is calculated. Assume that the power supply trace is divided into several small segments, and the resistance of each segment is denoted as , then the total resistance is expressed as:

[0091] ;

[0092] where is the resistivity of the trace material, is the length of the th segment of the trace, is its cross-sectional area. Through the simulation, the equivalent resistance value of each segment in the metal trace is obtained, and a resistance distribution data matrix of the entire power supply network is generated. Based on the equivalent resistance distribution data, a power supply network voltage drop model is constructed to calculate the voltage drop values at different pixel positions. The calculation of the voltage drop is based on Ohm's law, combined with the trace current and its equivalent resistance , and the voltage drop of each segment is expressed as:

[0093] ;

[0094] Assume that the number of nodes in the power supply network is , then through the mesh current flow relationship of the power supply network, a linear equation system of voltage drop is established:

[0095] ;

[0096] where is the node voltage drop vector, is the equivalent resistance matrix between nodes, is the node current vector. After solving this system of equations, the voltage drop value at each pixel position is obtained . According to the calculated voltage drop values, the metal traces are segmented to optimize the trace design and reduce the overall voltage drop. The principle of segmentation is based on the distribution characteristics of the voltage drop values. The areas with larger voltage drops are divided into independent segments, and the width and routing layer of each segment of the trace are designed. The width design formula is:

[0097] ;

[0098] where is the maximum allowable voltage drop. The routing layer design needs to consider the interconnect effect of multi-layer traces to balance electrical performance and manufacturing complexity. After the design is completed, a metal trace segmentation layout diagram is generated, and the specific parameters of each segmented area are based on the optimization calculation results. After the layout diagram is completed, to ensure the voltage stability of the power supply network during operation, a voltage detection circuit is designed for each segmented area, thus constructing a real-time voltage monitoring network. The voltage detection circuit embeds a voltage sensor to measure the voltage value on the segmented trace in real time. The output characteristic of the sensor is expressed as:

[0099] ;

[0100] where is the actual voltage on the trace, is the measurement noise. By filtering and signal processing the detected voltage values, the noise interference is eliminated. Based on the real-time voltage monitoring network, the historical voltage drop data of the traces are collected, and a voltage drop prediction model is established through data analysis. The voltage drop prediction model is based on the time correlation in the historical data and is fitted using the method of time series analysis. For example, the first-order autoregressive model is used to represent the dynamic characteristics of the voltage drop:

[0101] ;

[0102] where, is the regression coefficient, is the prediction error term. By fitting the historical data using the least squares method, the parameters of the prediction model are determined to achieve the prediction of the future voltage drop trend. The voltage drop prediction model is input into the prediction-feedback compensation unit to perform dynamic compensation calculations for the power supply of different segmented areas. The compensation unit dynamically compensates by comparing the predicted voltage drop value and the target voltage , calculate the value of the compensation voltage to be applied :

[0103] ;

[0104] where is the predicted voltage drop value. The compensation voltage value is applied to the corresponding trace node through a compensation circuit to offset the influence of the voltage drop. After the compensation calculation is completed, a verification test is performed on the compensation voltage value, and the compensation effect is improved through parameter optimization. The verification test measures the actual voltage drop value after compensation , and calculates its root mean square error:

[0105] ;

[0106] If the error is large, the parameters of the compensation unit need to be adjusted, such as the gain, feedback resistance value, etc. After multiple rounds of optimization, the IR Drop compensation parameters that meet the accuracy requirements are obtained. These parameters are used during the operation of the microdisplay device to effectively improve the voltage stability of the power supply network and the pixel drive uniformity, thereby enhancing the display performance.

[0107] In one example, the IR Drop compensation parameters are input into a Markov decision inference model with double-layer compensation factors for process compensation calculation to obtain process compensation data, including:

[0108] Obtain device characteristic deviation parameters, which include threshold voltage deviation, channel length deviation, and oxide layer thickness deviation, and perform feature extraction and data clustering on the device characteristic deviation parameters to obtain the first-layer compensation factor. At the same time, based on the first-layer compensation factor, construct a device compensation feature matrix;

[0109] Obtain process parameters, which include etching depth, doping concentration, and ion implantation energy, and perform feature extraction on the process parameters to obtain the second-layer compensation factor. At the same time, construct a process compensation feature matrix according to the second-layer compensation factor;

[0110] Perform correlation analysis and regularization processing on the device compensation feature matrix and the process compensation feature matrix to obtain a compensation factor coupling matrix;

[0111] Input the IR Drop compensation parameters and the compensation factor coupling matrix into the Markov decision inference model with double-layer compensation factors, construct a state transition model according to the process parameter fluctuation law, and obtain a process fluctuation state transition matrix;

[0112] Use the dynamic programming algorithm to solve the optimal compensation strategy for the process fluctuation state transition matrix, and through iterative calculation of the Bellman equation, obtain a compensation strategy sequence;

[0113] Perform target interval determination and variance analysis on the compensation strategy sequence, obtain the compensation optimization parameters through least squares fitting, and write the compensation optimization parameters into the compensation lookup table according to the pixel position and compensation magnitude to obtain the process compensation data.

[0114] In this example, for the device characteristic deviation parameters, collect key parameters including threshold voltage deviation ( ), channel length deviation ( ), and oxide layer thickness deviation ( ). These parameters respectively describe the electrical and physical property changes of the microdisplay device caused by process fluctuations during actual manufacturing. By statistically analyzing these data, which are represented in matrix form , where each row corresponds to the deviation parameters of a pixel unit. To effectively utilize these data, perform feature extraction on the device characteristic deviation parameters. The feature extraction is achieved through principal component analysis, which reduces the dimensionality of the high-dimensional deviation data and extracts the main influencing factors. For example, the goal of principal component analysis is to project the data from the original space to the new feature space , and the formula is:

[0115] ;

[0116] where is the deviation parameter matrix, and is the projection matrix, which is composed of eigenvectors. The extracted main features are defined as the first-layer compensation factors, which are used to quantify the influence of different deviations on the device performance. After obtaining the first-layer compensation factors, construct the device compensation feature matrix , and its form is:

[0117] ;

[0118] where represents the th compensation factor. Obtain the process parameters, including etching depth ( ), doping concentration ( ), and ion implantation energy ( ). These parameters reflect the precision and consistency of material processing during manufacturing, and are also represented as a matrix . The method of feature extraction for the process parameters is similar to that of the device characteristic parameters. Use the principal component analysis method to reduce the dimensionality of the original parameters and extract the second-layer compensation factors, and construct the process compensation feature matrix , and its form is:

[0119] ;

[0120] where is the process compensation factors. To comprehensively analyze the relationship between device characteristic compensation factors and process compensation factors, and are subjected to correlation analysis and regularization processing. By constructing a coupling matrix , the interaction between these two types of factors is quantified. Each element of the coupling matrix represents the correlation between the device characteristic factor and the process compensation factor , and the calculation formula is:

[0121] ;

[0122] where Cov represents covariance, and are the standard deviations of and respectively. Through regularization processing, the influence of noise is eliminated and the final compensation factor coupling matrix is obtained. The IR Drop compensation parameters and the compensation factor coupling matrix are input into the Markov decision inference model of the double-layer compensation factor to describe the dynamic evolution process of process parameter fluctuations. The Markov model uses the state transition matrix to describe the probability of a parameter transitioning from one state to another, defined as follows:

[0123] ;

[0124] where is the probability of the current state transitioning to the next state . By calculating the state transition through multiple iterations, a process fluctuation state transition matrix is generated. The dynamic programming algorithm is used to solve the optimal compensation strategy for the state transition matrix. The goal of dynamic programming is to solve the compensation path with the minimum cumulative cost through recursive optimization. Assume the cost function is , and its recurrence relation is described by the Bellman equation:

[0125] ;

[0126] where is the immediate reward for taking action , and is the discount factor, which is used to balance the current reward and future rewards. Through the iterative calculation of the Bellman equation, a sequence of compensation strategies is generated. The target interval determination and variance analysis are performed on the sequence of compensation strategies to evaluate the stability and effectiveness of different strategies. Combining the least squares fitting, the compensation parameters are optimized to further improve the accuracy, and the fitting formula is:

[0127] ;

[0128] wherein are fitting parameters, is the design matrix, is the target output. The optimized compensation parameters are written into the compensation lookup table according to the pixel position and the compensation magnitude to form complete process compensation data.

[0129] In one example, timing optimization for addressing control is performed based on the process compensation data, and a dynamic time slot allocation scheme is adopted for time-division multiplexing control to obtain the timing parameters of the row-column scan signals, including:

[0130] Perform timing feature analysis on the process compensation data, extract the row scan signal frequency, data write time, and storage time parameters to obtain the initial timing configuration data;

[0131] Based on the initial timing configuration data, construct a dynamic time slot allocation model, set the data write-to-storage time ratio threshold to obtain the time-division multiplexing reference data;

[0132] Design the row-column scan control circuit according to the time-division multiplexing reference data, and perform synchronous design on the row strobe signal and the column data signal to obtain the scan timing schematic diagram;

[0133] Dynamically optimize the time slot interval in the scan timing schematic diagram, adjust the multiplexing ratio according to the display data characteristics to obtain the dynamic time slot allocation scheme;

[0134] Input the dynamic time slot allocation scheme into the timing controller for time-division multiplexing simulation to obtain the multiplexing control parameters;

[0135] Design the mapping of the chip pins according to the multiplexing control parameters, optimize the configuration of the data transmission channel to obtain the pin allocation data;

[0136] Perform timing matching on the data write and compensation processes based on the pin allocation data to obtain the synchronous control sequence, and optimize the drive timing control of the synchronous control sequence to obtain the timing parameters of the row-column scan signals.

[0137] In this example, for the process compensation data, timing feature analysis is performed to extract key parameters. The process compensation data includes the driving requirements of the pixel array and the dynamic response characteristics of the compensation network. By analyzing the process compensation data, the frequency of the row scan signal ( ), the data write time ( ), and the storage time ( ) are extracted. These parameters are described by formulas. For example, the frequency of the row scan signal is:

[0138] ;

[0139] wherein is the time of single-line scanning, including data writing time and signal switching time. A dynamic time slot allocation model is constructed based on the initial timing configuration data. The core of the dynamic time slot allocation model is to set the ratio threshold of data writing time to storage time , so as to balance the working efficiency of the driving circuit and the stability of pixel brightness. The relationship of the ratio threshold is expressed as:

[0140] ;

[0141] By optimizing the ratio threshold under different line scanning conditions, time-division multiplexing reference data is obtained to guide the rules of time slot allocation. Based on the time-division multiplexing reference data, a row-column scanning control circuit is designed, and the synchronization design of the row strobe signal and the column data signal is completed. In row-column scanning, the row strobe signal ( ) and the column data signal ( ) must work in coordination to ensure the correct driving of pixels. The row strobe signal is expressed as:

[0142] ;

[0143] where and represent the start time and end time of row strobe respectively. The column data signal switches synchronously with the row strobe signal to ensure data writing is completed during each row scan. A scanning timing schematic diagram is generated through circuit design to visually display the switching and coordination relationship of signals. After completing the initial scanning timing design, the time slot interval in the scanning timing schematic diagram is dynamically optimized. The time slot interval ( ) directly affects the display refresh rate and power consumption, and is adjusted according to the display data characteristics. For example, for areas with large brightness changes, the writing time slot is shortened; while for areas with relatively stable brightness, the storage time is extended to reduce energy consumption. The optimized dynamic time slot allocation scheme is expressed as:

[0144] ;

[0145] where is the brightness distribution characteristic, is the response characteristic of the compensation circuit. The result of dynamic adjustment better adapts to different display contents and improves the overall efficiency of the system. The dynamic time slot allocation scheme is input to the timing controller for time-division multiplexing simulation. The simulation verifies the feasibility of the time slot allocation scheme by simulating the dynamic behavior of row-column scanning signals and outputs multiplexing control parameters. These parameters include the timing configuration of each row scan and the relative delay of signal synchronization. The multiplexing control parameters are expressed as a matrix , where each element defines a specific setting of the scanning timing sequence. According to the multiplexing control parameters, the pin mapping design of the chip is carried out to achieve efficient allocation of data transmission. In the mapping design, the layout of the data transmission channels and the signal allocation method are optimized to reduce latency and improve the reliability of transmission. The pin allocation data is represented as a vector:

[0146] ;

[0147] where represents the data signal corresponding to the th pin. The optimized pin allocation can ensure the load balance of each channel and reduce signal interference. Based on the pin allocation data, the timing matching of the data writing and compensation processes is carried out. By adjusting the data writing time and the compensation time , a synchronization control sequence is generated. The core of the synchronization control sequence is to ensure the coordination of the row and column scanning signals and the compensation signal in time, and its formula is:

[0148] ;

[0149] where is the delay time of signal transmission. After being verified and optimized, the synchronization control sequence is input into the driving timing control unit to improve the display performance. By optimizing the driving timing control of the synchronization control sequence, the final timing parameters of the row and column scanning signals are generated. These parameters include the start and end times of each row scan, the interval of column data switching, and the application time of the compensation signal, etc.

[0150] In an example, the optoelectronic characteristics of the row and column scanning signal timing parameters are tested, the display uniformity data is collected, and the target compensation configuration parameters are obtained through the evaluation of the compensation parameters, including:

[0151] The driving timing of the row and column scanning signal timing parameters is loaded, a test platform including a driving circuit, a signal generator, and an optoelectronic test system is established, and the test system configuration data is obtained;

[0152] Based on the test system configuration data, the brightness uniformity of the Micro-LED microdisplay device is scanned, the original brightness distribution data is obtained, and the original brightness distribution data is double-sampled in the spatial domain and the time domain to obtain the display uniformity data;

[0153] According to the display uniformity data, an evaluation system for static uniformity indicators and dynamic response indicators is constructed to obtain a compensation effect evaluation model;

[0154] The display uniformity data is input into the compensation effect evaluation model for parameter analysis to obtain the compensation effect quantization data;

[0155] Perform multi-dimensional feature analysis on the compensated effect quantization data, establish an evaluation index matrix including brightness uniformity, contrast, and response time, obtain the compensated parameter evaluation results, and perform parameter optimization configuration based on the compensated parameter evaluation results to obtain the target compensated configuration parameters.

[0156] In this example, drive timing loading is performed on the row and column scan signal timing parameters to construct a complete test platform including a drive circuit, a signal generator, and an optoelectronic test system. In the test platform, the drive circuit is used to generate accurate row and column scan signals, the signal generator provides control signals for the drive circuit, and the optoelectronic test system is responsible for measuring the brightness response of the Micro-LED display screen in real time. The core of signal loading lies in ensuring the time accuracy of the row and column scan signals. The frequency of the row signal is expressed as:

[0157] ;

[0158] where is the time for single-row scanning, including data writing time and storage time. After completing the construction of the test platform, based on the configured test system, perform brightness uniformity scanning on the Micro-LED display device. Through the optoelectronic test system, perform row-by-row scanning on the pixel array to obtain the brightness data of each pixel unit. Assume that the brightness of each pixel unit is where is the pixel position, is the time point. The brightness distribution data obtained through scanning is represented as a three-dimensional matrix . Perform dual sampling on the original brightness distribution data in the spatial domain and the time domain. Spatial domain sampling is used to extract the brightness distribution characteristics of different rows and columns in the pixel array, and time domain sampling is used to analyze the dynamic behavior of pixel brightness changing with time. The formula for spatial sampling is:

[0159] ;

[0160] where is the total number of sampling times. The formula for time sampling is:

[0161] ;

[0162] where and are the number of rows and columns of the pixel array respectively. After sampling, generate display uniformity data, including brightness change information in the spatial and time dimensions. According to the display uniformity data, construct an evaluation system including static uniformity indicators and dynamic response indicators to quantify the performance of the display screen. The static uniformity indicator is used to describe the consistency of pixel brightness in space and is achieved by calculating the standard deviation of brightness. The formula is:

[0163] ;

[0164] Wherein is the average brightness of the entire array. The dynamic response index is used to evaluate the stability of pixel brightness over time and is achieved by calculating the change rate of time-sampled data. The formula is:

[0165] ;

[0166] After the evaluation system is constructed, the display uniformity data is input into the compensation effect evaluation model for parameter analysis to obtain the compensated effect quantization data. The compensated effect quantization data reflects the actual improvement of the compensation network on brightness uniformity and dynamic response. Based on these data, multi-dimensional feature analysis is performed on them to construct an evaluation index matrix including brightness uniformity, contrast, and response time. The index matrix is expressed as:

[0167] ;

[0168] Wherein, is the contrast, defined as the ratio of the maximum brightness to the minimum brightness:

[0169] ;

[0170] Through the evaluation index matrix, the compensation effect is analyzed, and the compensation parameter evaluation result is generated. Based on the evaluation result, parameter optimization configuration is performed. The optimization goal is to minimize the static and dynamic brightness errors while ensuring that the contrast meets the design requirements. The optimization is achieved by fitting the compensation parameters using the least squares method. The formula is:

[0171] ;

[0172] Wherein, is the optimal solution of the compensation parameter, is the input feature matrix, is the target brightness vector. The optimized compensation parameters are stored as the target compensation configuration parameters for adjusting the driving conditions of the display device.

[0173] Referring to Figure 2 , this embodiment provides a pixel addressing device for a micro-LED microdisplay device with ultra-high pixel density, including:

[0174] Acquisition module 1, configured to collect compensation parameters for the pixel array structure parameters of the micro-LED microdisplay device, establish a 2T1C drive circuit compensation model, store the threshold voltage, mobility, and parasitic capacitance parameters in the compensation parameter database, and obtain the pixel compensation reference value;

[0175] A building module 2, configured to construct a threshold voltage compensation network based on a compensation parameter database and a pixel compensation reference value, so as to obtain a threshold voltage compensation network;

[0176] A segmentation module 3, configured to perform segmented design on a power supply metal trace based on the threshold voltage compensation network, so as to obtain IR Drop compensation parameters;

[0177] A compensation module 4, configured to input the IR Drop compensation parameters into a Markov decision inference model with a double-layer compensation factor for process compensation calculation, so as to obtain process compensation data;

[0178] A control module 5, configured to perform timing optimization of addressing control based on the process compensation data, and perform time-division multiplexing control by adopting a dynamic time slot allocation scheme, so as to obtain row-column scan signal timing parameters;

[0179] A test module 6, configured to perform optoelectronic characteristic tests on the row-column scan signal timing parameters, collect display uniformity data, and obtain target compensation configuration parameters through compensation parameter evaluation.

[0180] In this embodiment, for the specific implementation of each unit in the above device embodiment, please refer to the description in the above method embodiment, and details are not described herein again.

[0181] An embodiment of the present invention further provides a pixel addressing system for a super-high pixel density Micro-LED microdisplay device, on which a computer program is stored. When the computer program is executed by a processor, the above method is implemented. It can be understood that the pixel addressing system for the super-high pixel density Micro-LED microdisplay device in this embodiment can be a volatile readable storage medium or a non-volatile readable storage medium.

[0182] Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be completed by instructing relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above methods. Among them, any reference to a memory, storage, database, or other medium provided by the present invention and used in the embodiments can include non-volatile and / or volatile memories. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (SSRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM, etc.

[0183] It should be noted that in this article, the terms "include", "comprise", or any other variant thereof are intended to cover non-exclusive inclusion, such that a process, apparatus, article, or method that includes a series of elements includes not only those elements but also other elements not expressly listed, or elements that are inherent to such process, apparatus, article, or method. Without further limitation, an element defined by the statement "including one..." does not exclude the existence of additional identical elements in the process, apparatus, article, or method that includes such element.

[0184] The above are only the preferred embodiments of the present invention, and do not limit the patent scope of the present invention accordingly. Any equivalent structural or equivalent process transformation made by using the specification and drawings of the present invention, or directly or indirectly applied in other related technical fields, shall be equally included in the patent protection scope of the present invention.

Claims

1. A pixel addressing method for an ultra-high pixel density Micro-LED micro-display device, characterized in that: The following steps are involved: The compensation parameters of the pixel array structure parameters of the Micro-LED micro display device are collected, a 2T1C drive circuit compensation model is established, the threshold voltage, mobility and parasitic capacitance parameters are stored in the compensation parameter database, and the pixel compensation reference value is obtained; Constructing a compensation network for the threshold voltage deviation distribution characteristics according to the compensation parameter database and the pixel compensation reference value to obtain a threshold voltage compensation network; Based on the threshold voltage compensation network, the power supply metal routing is segmented to obtain IR Drop compensation parameters; Inputting the IR Drop compensation parameter into the Markov decision inference model of the double-layer compensation factor to perform process compensation calculation to obtain process compensation data; Based on the process compensation data, the timing of addressing control is optimized, and a dynamic time slot allocation scheme is used to perform time-division multiplexing control to obtain row and column scanning signal timing parameters; The photoelectric characteristic test is performed on the row and column scanning signal timing parameters, the display uniformity data is collected, and the target compensation configuration parameters are obtained through compensation parameter evaluation.

2. The pixel addressing method of the ultra-high pixel density Micro-LED micro-display device according to claim 1, characterized in that: The compensation parameters of the pixel array structure parameters of the Micro-LED micro display device are collected, a 2T1C drive circuit compensation model is established, the threshold voltage, mobility and parasitic capacitance parameters are stored in the compensation parameter database, and the pixel compensation reference value is obtained, including: Collect data on pixel size, pixel spacing and pixel arrangement to obtain pixel array structural parameters; Performing parameter analysis on the pixel array structure parameters, and constructing a 2T1C driving circuit compensation model including a switching transistor T1, a driving transistor T2 and a storage capacitor C; Performing parameter measurement on a single pixel unit in the 2T1C driving circuit compensation model to obtain a threshold voltage parameter; Performing electrical characteristic analysis on the single pixel unit to obtain a mobility parameter, and performing equivalent circuit modeling on the single pixel unit to obtain a parasitic capacitance parameter; Inputting the threshold voltage parameter, the mobility parameter and the parasitic capacitance parameter into a compensation parameter database for storage; A weighted calculation is performed on the data in the compensation parameter database, wherein the weight coefficient is dynamically adjusted according to the pixel position to obtain a weight coefficient matrix, and a weighted average operation is performed on the compensation parameters based on the weight coefficient matrix to obtain a pixel compensation reference value.

3. The pixel addressing method of the ultra-high pixel density Micro-LED micro-display device according to claim 1, characterized in that: The method of constructing a compensation network for the threshold voltage deviation distribution characteristics according to the compensation parameter database and the pixel compensation reference value to obtain a threshold voltage compensation network includes: Performing difference analysis on the threshold voltage parameters in the compensation parameter database to obtain a threshold voltage deviation distribution diagram, and performing compensation circuit planning based on the threshold voltage deviation distribution diagram to obtain a differential structure compensation branch layout; Designing the circuit of the differential structure compensation branch, constructing a compensation branch including a differential amplifier, a sampling and holding circuit, and a comparator, and calculating the circuit device parameters according to the layout of the compensation branch to obtain the compensation capacitor value, the compensation transistor size, and the feedback resistor value; Inputting the compensation capacitor value, the compensation transistor size and the feedback resistor value into a timing simulation model to obtain a circuit response characteristic; The compensation effect of the circuit response characteristic is analyzed based on the pixel compensation reference value to obtain uniformity compensation error data, and the compensation branch parameters are iteratively optimized according to the uniformity compensation error data to obtain a threshold voltage compensation network.

4. The pixel addressing method of the ultra-high pixel density Micro-LED micro-display device according to claim 1, characterized in that: The step of designing the power supply metal wiring in sections based on the threshold voltage compensation network to obtain IR Drop compensation parameters includes: Based on the threshold voltage compensation network, resistance distribution simulation is performed on the power supply metal wiring to obtain equivalent resistance distribution data, and a power supply network voltage drop model is constructed based on the equivalent resistance distribution data to obtain voltage drop values ​​of pixels at different positions; Divide the metal routing into sections according to the voltage drop value, and design the width and wiring level of each section of the routing to obtain a metal routing section layout diagram; Design a voltage detection circuit for each segment area in the metal routing segment layout diagram to obtain a real-time voltage monitoring network; Based on the real-time voltage monitoring network, historical voltage drop data is collected, and trend analysis is performed on the historical voltage drop data to obtain a voltage drop prediction model; The voltage drop prediction model is input into the prediction-feedback compensation unit to perform dynamic compensation calculation to obtain a compensation voltage value, and the compensation voltage value is subjected to verification test and parameter optimization to obtain an IR Drop compensation parameter.

5. The pixel addressing method of the ultra-high pixel density Micro-LED micro-display device according to claim 1, characterized in that: The step of inputting the IR Drop compensation parameter into the Markov decision inference model of the double-layer compensation factor to perform process compensation calculation to obtain process compensation data includes: Acquire device characteristic deviation parameters, the device characteristic deviation parameters including threshold voltage deviation, channel length deviation and oxide layer thickness deviation, perform feature extraction and data clustering on the device characteristic deviation parameters to obtain a first layer compensation factor, and construct a device compensation feature matrix based on the first layer compensation factor; Acquiring process parameters, the process parameters including etching depth, doping concentration and ion implantation energy, and performing feature extraction on the process parameters to obtain second-layer compensation factors, and constructing a process compensation feature matrix according to the second-layer compensation factors; Based on the device compensation characteristic matrix and the process compensation characteristic matrix, correlation analysis and regularization processing of the double-layer compensation factors are performed to obtain a compensation factor coupling matrix; Inputting the IR Drop compensation parameter and the compensation factor coupling matrix into the Markov decision inference model of the double-layer compensation factor, constructing a state transfer model according to the process parameter fluctuation law, and obtaining a process fluctuation state transfer matrix; A dynamic programming algorithm is used to solve the optimal compensation strategy for the process fluctuation state transfer matrix, and a compensation strategy sequence is obtained through iterative calculation of the Bellman equation; The target interval determination and variance analysis are performed on the compensation strategy sequence, and the compensation optimization parameters are obtained by least square fitting. The compensation optimization parameters are written into the compensation lookup table according to the pixel position and the compensation magnitude to obtain the process compensation data.

6. The pixel addressing method of the ultra-high pixel density Micro-LED micro-display device according to claim 1, characterized in that: The timing optimization of addressing control based on the process compensation data and the use of a dynamic time slot allocation scheme for time division multiplexing control to obtain row and column scanning signal timing parameters include: Performing timing characteristic analysis on the process compensation data, extracting row scanning signal frequency, data writing time and storage time parameters, and obtaining initial timing configuration data; Building a dynamic time slot allocation model based on the initial timing configuration data, setting a data writing and storage time ratio threshold, and obtaining time-division multiplexing benchmark data; Designing a row and column scanning control circuit according to the time-division multiplexing reference data, and synchronously designing a row selection signal and a column data signal to obtain a scanning timing diagram; Dynamically optimizing the time slot intervals in the scanning timing diagram, adjusting the multiplexing ratio according to display data characteristics, and obtaining a dynamic time slot allocation scheme; Input the dynamic time slot allocation scheme into a timing controller to perform time division multiplexing simulation to obtain multiplexing control parameters; Mapping and designing chip pins according to the multiplexing control parameters, optimizing and configuring the data transmission channels, and obtaining pin allocation data; The data writing and compensation processes are time-matched based on the pin allocation data to obtain a synchronous control sequence, and the synchronous control sequence is driven and optimized to obtain row and column scanning signal timing parameters.

7. The pixel addressing method of the ultra-high pixel density Micro-LED micro-display device according to claim 1, characterized in that: The photoelectric characteristic test of the row and column scanning signal timing parameters is performed, display uniformity data is collected, and target compensation configuration parameters are obtained through compensation parameter evaluation, including: The row and column scanning signal timing parameters are loaded with driving timing, a test platform including a driving circuit, a signal generator and an optoelectronic test system is established, and test system configuration data is obtained; Scanning the brightness uniformity of the Micro-LED micro display device based on the test system configuration data to obtain original brightness distribution data, and performing dual sampling of the original brightness distribution data in the space domain and the time domain to obtain display uniformity data; Constructing an evaluation system of static uniformity index and dynamic response index according to the display uniformity data to obtain a compensation effect evaluation model; Inputting the display uniformity data into the compensation effect evaluation model for parameter analysis to obtain compensation effect quantitative data; A multi-dimensional feature analysis is performed on the compensation effect quantification data, an evaluation index matrix including brightness uniformity, contrast and response time is established, compensation parameter evaluation results are obtained, and parameter optimization configuration is performed based on the compensation parameter evaluation results to obtain target compensation configuration parameters.

8. A pixel addressing device for an ultra-high pixel density Micro-LED micro-display device, characterized in that: For implementing the steps of the method according to any one of claims 1 to 7, the ultra-high pixel density Micro-LED micro-display device pixel addressing device comprises: The acquisition module is used to collect compensation parameters of the pixel array structure parameters of the Micro-LED micro display device, establish a 2T1C drive circuit compensation model, store the threshold voltage, mobility and parasitic capacitance parameters in the compensation parameter database, and obtain the pixel compensation reference value; A construction module, used to construct a compensation network for the threshold voltage deviation distribution characteristics according to the compensation parameter database and the pixel compensation reference value, so as to obtain a threshold voltage compensation network; A segmentation module, used for segmenting the power supply metal routing based on the threshold voltage compensation network to obtain IRDrop compensation parameters; A compensation module, used for inputting the IR Drop compensation parameter into a Markov decision inference model of a double-layer compensation factor to perform process compensation calculation to obtain process compensation data; A control module, used for performing timing optimization of addressing control based on the process compensation data, and performing time-division multiplexing control using a dynamic time slot allocation scheme to obtain row and column scanning signal timing parameters; The test module is used to perform an optoelectronic characteristic test on the row and column scanning signal timing parameters, collect display uniformity data, and obtain target compensation configuration parameters through compensation parameter evaluation.

9. An ultra-high pixel density Micro-LED micro-display device pixel addressing system having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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