Ultrahigh pixel density Micro-LED micro display device pixel addressing method and device

Abstract

The invention relates to a pixel addressing method and a pixel addressing device for a Micro-LED (Light Emitting Diode) micro display device with ultrahigh pixel density. The method comprises the following steps: carrying out compensation parameter acquisition on a pixel array structure parameter of the Micro-LED micro display device to obtain a pixel compensation reference value; performing compensation network construction on the threshold voltage deviation distribution characteristics to obtain a threshold voltage compensation network; carrying out segmented design on the power supply metal wire to obtain an IR Drop compensation parameter; performing process compensation calculation through a Markov decision reasoning model of double-layer compensation factors to obtain process compensation data; timing optimization of addressing control is carried out, and row and column scanning signal timing parameters are obtained; and performing a photoelectric characteristic test on the line-column scanning signal time sequence parameter, collecting display uniformity data, and obtaining a target compensation configuration parameter through compensation parameter evaluation. According to the invention, the number of chip pins is reduced, and the stability of a driving time sequence is ensured at the same time.

Description

Technical Field

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

[0002] With the rapid development of display technology, ultra-high pixel density Micro-LED micro-display devices have shown broad application prospects in the fields of AR / VR, smart wearables, etc. due to their advantages such as high brightness, high contrast, and low power consumption. However, in actual applications, due to the non-uniformity of device manufacturing technology and the parasitic effects of the driving circuit, display devices have obvious brightness non-uniformity problems, which seriously affects the display quality.

[0003] The current mainstream pixel addressing scheme mainly adopts no compensation or a single compensation strategy, which cannot effectively deal with the influence of complex factors such as process fluctuations and power supply network voltage drop. Especially in ultra-high pixel density scenarios, traditional compensation methods are difficult to accurately capture the spatial distribution characteristics of device characteristic deviations, and the compensation strategy lacks adaptability, making it difficult for the compensation effect to meet the requirements of high-quality display. Summary of the invention

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

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

[0006] The present invention also provides a pixel addressing device for an ultra-high pixel density Micro-LED micro-display device, comprising: 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 IR Drop 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 timing parameters of row and column scanning signals; 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.

[0007] The present invention also provides an ultra-high pixel density Micro-LED micro-display device pixel addressing system, on which a computer program is stored, and when the computer program is executed by a processor, the steps of any of the methods described above are implemented.

[0008] In summary, the technical solution provided by the present invention realizes the accurate collection and quantitative analysis of the characteristic parameters of the pixel unit by establishing a 2T1C driving circuit compensation model and introducing a weighted calculation mechanism, thereby 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 the threshold voltage deviation, so that the display uniformity error is controlled within a smaller range. The IR Drop compensation scheme based on the dynamic prediction-feedback structure accurately grasps the spatiotemporal distribution characteristics of the voltage drop in the power supply network, and effectively suppresses the display distortion caused by uneven power supply. An innovative Markov decision reasoning model of a double-layer compensation factor is proposed, and the correlation mapping between device characteristics and process parameters is established through the coupling coefficient matrix, so that the compensation strategy has strong adaptability. The time-sharing multiplexing control scheme with dynamic time slot allocation is adopted to optimize the configuration of data writing and storage time, while reducing the number of chip pins and ensuring the stability of the driving timing. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] Figure 1 1 is a schematic diagram of the steps of a method for addressing pixels of an ultra-high pixel density Micro-LED micro-display device according to an embodiment of the present invention; Figure 2 This is a structural block diagram of a pixel addressing device for an ultra-high pixel density Micro-LED micro-display device in one embodiment of the present invention.

[0010] The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0011] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with 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 intended to limit the present invention.

[0012] Reference Figure 1 , this embodiment provides a pixel addressing method for an ultra-high pixel density Micro-LED micro display device, comprising the following steps: S1, 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; Among them, data collection is performed on the physical characteristics of the pixel array, including structural parameters such as the size of the pixel, the spacing between pixels, and the specific arrangement of the pixels. These parameters are obtained through high-precision measurement tools to ensure the accuracy and comprehensiveness of the data. The structural parameters of the pixel array are analyzed to build a drive circuit compensation model based on the 2T1C structure. The 2T1C drive circuit consists of a switching transistor T1, a driving transistor T2, and a storage capacitor C. The switch transistor T1 is used 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. In the process of building this model, the actual physical size of the pixel unit and the electrical interconnection characteristics between pixels are considered to establish an equivalent compensation model that can accurately reflect the actual working conditions. Parameters of a single pixel unit in the 2T1C drive circuit are measured. The threshold voltage of the driving transistor is measured by measuring the critical voltage value when the transistor is turned on. At the same time, the electrical characteristics of the pixel unit are analyzed to obtain the mobility parameters. Mobility is an important indicator that describes the speed of carrier movement in a transistor. It is calculated by testing the output characteristic curve of the transistor under different voltage conditions. An equivalent circuit model is built for a single pixel unit, and its parasitic capacitance parameters are measured on this basis. Parasitic capacitance is caused by the internal structure of the pixel unit and the electrical coupling effect with the adjacent units, and its size directly affects the dynamic response speed of the pixel and the uniformity of the display. By accurately establishing the equivalent circuit model, accurate parasitic capacitance parameters are obtained, and an important basis is provided for the compensation model. The threshold voltage parameters, mobility parameters and parasitic capacitance parameters are input into the compensation parameter database for storage. The data in the compensation parameter database is weighted and calculated to fully consider the characteristic differences of pixels at different positions in the pixel array. The weight coefficient is dynamically adjusted according to the pixel position to form a weight coefficient matrix for weighted averaging of the compensation parameters. The adjustment of the weight coefficient is based on the position characteristics of the pixel. The pixel compensation reference value is obtained by weighted averaging based on the weight coefficient matrix.

[0013] S2, 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; Specifically, the threshold voltage parameters in the compensation parameter database are subjected to differential analysis. By extracting the threshold voltage data of each pixel recorded in the database, the deviation distribution characteristics of each pixel are calculated in combination with the reference value. Through data processing and statistical analysis, a threshold voltage deviation distribution diagram is drawn to show the spatial distribution characteristics of the voltage deviation of each pixel in the entire pixel array. The compensation circuit is planned based on the threshold voltage deviation distribution diagram. By analyzing the distribution law of the voltage deviation, the layout of the differential structure compensation branch is designed so that the distribution of the compensation branch is adapted to the voltage deviation characteristics. The differential structure compensation branch adjusts the actual pixel driving voltage to an ideal state by comparing and correcting the deviation value. In the layout planning stage, the spatial distribution of the pixel array and the difficulty of hardware implementation of the compensation circuit are considered to ensure the feasibility and effectiveness of the design. The circuit design of the differential structure compensation branch is performed. A compensation branch including a differential amplifier, a sampling and holding circuit and a comparator is constructed. 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 sampling and holding circuit is used to capture and maintain the instantaneous voltage signal to provide stable input data for the compensation circuit. The role of the comparator is to compare the amplified signal with the reference signal to generate a compensation control signal. When designing these core circuit components, the circuit device parameters are calculated according to the layout of the compensation branch, such as the capacitance of the compensation capacitor, the size of the compensation transistor, and the resistance 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 that the compensation circuit can operate efficiently. The capacitance of the compensation capacitor, the size of the compensation transistor, and the resistance of the feedback resistor are input into the timing simulation model to analyze the dynamic response characteristics of the compensation circuit. The timing simulation model simulates the dynamic behavior of the compensation circuit under different input signals to evaluate the performance of the circuit under actual working conditions, including response speed, stability, and compensation accuracy of the deviation signal. The simulation results verify whether the currently designed compensation circuit can meet the expected requirements. Based on the pixel compensation reference value, the compensation effect of the circuit response characteristics is analyzed to evaluate the actual uniformity compensation capability of the compensation circuit. By comparing the compensated voltage distribution with the target reference value, the uniformity compensation error data is calculated to reflect the actual correction effect of the compensation network in each pixel area and provide a clear direction for subsequent optimization. According to the uniformity compensation error data, the design parameters of the compensation branch are iteratively optimized, such as adjusting the gain of the differential amplifier, modifying the resistance of the feedback resistor, or optimizing the capacitance of the compensation capacitor. Through multiple optimization iterations, the compensation error is gradually reduced until the compensation network can control the voltage deviation of the pixel array within the allowable range and achieve the target uniformity. A high-precision threshold voltage compensation network is obtained.

[0014] S3, based on the threshold voltage compensation network, the power supply metal routing is segmented and the IR Drop compensation parameters are obtained; It should be noted that the resistance distribution simulation of the power supply metal routing is performed based on the threshold voltage compensation network. The simulation process calculates the equivalent resistance distribution data of the routing by analyzing the material properties, geometric shape and connection relationship of the power supply metal routing with the pixel array, thereby reflecting the resistance change at different positions. Based on the equivalent resistance distribution data, a voltage drop model of the power supply network is constructed. The model calculates the voltage drop value corresponding to the pixels at different positions in the pixel array by considering the current distribution and resistance characteristics in the routing. The metal routing is segmented according to the voltage drop value. According to the distribution characteristics of the voltage drop, the routing is divided into several areas with similar electrical characteristics so as to perform more accurate compensation design in each segment. At the same time, the width and wiring level of each routing segment are optimized. The optimization of the width comprehensively considers the resistance and manufacturing process limitations of the routing to reduce the routing area while reducing the voltage drop. The design of the wiring level considers the interconnection effect and parasitic characteristics of the multi-layer wiring, thereby improving the signal integrity and power supply efficiency on the basis of reducing the voltage drop. Through these design steps, a metal routing segment layout diagram is generated to reflect the specific structure of the segmented power supply network. A voltage detection circuit is designed for each segmented area in the metal routing segmented layout diagram to build a real-time voltage monitoring network. The real-time voltage monitoring network collects voltage signals from each segmented area in real time by embedding detection circuits, so as to monitor the voltage status of the power supply network at any time. These detection circuits include high-precision voltage sensors and signal processing units, which transmit the detected voltage signals to the central control unit to realize real-time monitoring of the dynamic behavior of the power supply network. Through the real-time monitoring mechanism, the details of voltage changes can be effectively captured. 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 build a voltage drop prediction model. The time correlation and spatial distribution characteristics in the historical data are comprehensively analyzed, and a suitable algorithm (such as a Markov model or a machine learning method) is used to predict the future voltage drop change trend. Through this prediction mechanism, possible problem areas are identified in advance. The voltage drop prediction model is input into the prediction-feedback compensation unit to realize dynamic compensation calculation. The compensation unit combines the prediction model and the real-time monitoring data to calculate the compensation voltage value required for each segmented area. The calculated compensation voltage value is implemented by the driving circuit to adjust the supply voltage in real time to offset the impact of the voltage drop on the pixel drive. At the same time, in order to verify the compensation effect, the compensation voltage value is verified and tested, and the actual compensation effect is evaluated by monitoring and comparing the voltage state after compensation. 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 obtain the optimized IRDrop compensation parameters.

[0015] S4, inputting the IR Drop compensation parameter into the Markov decision reasoning model of the double-layer compensation factor to perform process compensation calculation to obtain process compensation data; Specifically, by measuring the device characteristic deviation parameters, key data including threshold voltage deviation, channel length deviation and oxide layer thickness deviation are obtained. These data reflect the deviation of the pixel unit caused by material characteristics or process fluctuations in the actual manufacturing process. The device characteristic deviation parameters are feature extracted, and the correlation and influencing factors between different parameters are identified by analyzing their spatial distribution and statistical characteristics. The parameters are classified by data clustering algorithm to extract the first layer of compensation factors. Based on the first layer of compensation factors, a device compensation feature matrix is ​​constructed to describe the comprehensive impact of device-level deviation 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 of compensation factors to reflect the indirect impact of process fluctuations on device characteristics. According to the second layer of 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, the correlation analysis and regularization of the double-layer compensation factors are performed to obtain the compensation factor coupling matrix. The matrix integrates the interaction between device characteristic deviation and process parameters, and reduces noise interference in high-dimensional parameter space through regularization technology, making the compensation calculation more stable and reliable. The IR Drop compensation parameter and compensation factor coupling matrix are input into the Markov decision inference model of the double-layer compensation factor. In order to accurately simulate the fluctuation law of process parameters, the Markov model takes the state transition of process changes as the core and constructs the process fluctuation state transition matrix. The matrix describes the transition probability and evolution law between different process states, providing a decision basis for the optimization of dynamic compensation. The optimal compensation strategy of the process fluctuation state transition matrix is ​​solved by the dynamic programming algorithm, and the compensation strategy sequence is iteratively calculated based on the Bellman equation. The Bellman equation decomposes the global optimization problem and transforms it into a series of recursively solvable sub-problems to ensure the global optimality of the solution. The target interval judgment and variance analysis are performed on the compensation strategy sequence to evaluate the actual effect of different compensation strategies. Combined with the analysis results, the compensation optimization parameters are obtained by least squares fitting calculation to describe the compensation magnitude required for each pixel under different process conditions. The compensation optimization parameters are written into the compensation lookup table according to the pixel position and compensation magnitude to generate process compensation data.

[0016] S5, performing timing optimization of addressing control based on process compensation data, and adopting a dynamic time slot allocation scheme to perform time division multiplexing control to obtain row and column scanning signal timing parameters; Among them, the timing characteristics of the process compensation data are analyzed to extract key parameters related to display driving, such as the frequency of the row scanning signal, the data writing time and the storage time. These parameters affect the driving accuracy of the pixel and the dynamic performance of the display. By combining the deviation correction information in the process compensation data, the 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 and the storage time, the display driving time slot is dynamically adjusted, and the time-sharing multiplexing benchmark data is generated to ensure that the driving time of each pixel is optimally allocated between compensation and display, while reducing time slot conflicts and improving pixel addressing efficiency. When constructing the model, the pixel array scale, data transmission rate and complexity of process compensation are comprehensively considered to ensure the practical applicability and computational efficiency of the model. The row and column scanning control circuit is designed according to the time-sharing multiplexing benchmark data, and the synchronization design of the row selection signal and the column data signal is completed. Through the synchronous control design, the accurate matching of the row and column signals during the scanning process is guaranteed, and the possibility of signal delay and error accumulation is reduced. Combined with the physical characteristics of row and column signals, a scanning timing diagram is generated to show the distribution and coordination of row scanning signals and column data signals on the time axis. Based on the scanning timing diagram, the time slot interval is optimized, and the multiplexing ratio is dynamically adjusted by analyzing the display data characteristics (such as brightness distribution and pixel load difference), and a dynamic time slot allocation scheme is generated. This scheme dynamically allocates driving time according to the needs of different pixel areas, so that brightness uniformity and energy efficiency can be improved. The dynamic time slot allocation scheme is input into the timing controller for time-division multiplexing simulation, and the actual effect of the optimization scheme is verified by simulation, and multiplexing control parameters are generated. According to the multiplexing control parameters, the chip pins are mapped and designed to achieve efficient transmission and distribution of control signals. In this process, the data transmission channel is optimized to reduce transmission delay and signal loss, and the overall driving performance is improved. After completing the pin allocation design, the data writing and compensation processes are time-matched based on the allocation results, and the synchronization control sequence is generated by accurately adjusting the relative relationship between the writing and compensation time. The drive timing control optimization of the synchronization control sequence is performed 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 micro-display devices, significantly improve display uniformity and dynamic performance, and generate the final row and column scanning signal timing parameters.

[0017] S6, performing an optoelectronic characteristic test on the row and column scanning signal timing parameters, collecting display uniformity data, and obtaining target compensation configuration parameters through compensation parameter evaluation.

[0018] Specifically, the row and column scanning signal timing parameters are loaded with driving timing, and a test platform including a driving circuit, a signal generator and an optoelectronic test system is established. When building the test platform, the test system parameters are configured according to the specific driving characteristics of the Micro-LED micro-display device to ensure that the driving signal can be accurately loaded into the Micro-LED array, and the brightness response and electrical performance can be accurately measured to obtain the test system configuration data. The Micro-LED micro-display device is scanned for brightness uniformity based on the test system configuration data. The brightness uniformity scan is implemented by the photoelectric detection unit in the test system to obtain the original brightness distribution data of the Micro-LED pixel array under specific driving conditions. By performing multiple scans at different positions and different time points, the brightness change characteristics of the pixel array are captured, and the original brightness distribution data containing spatial distribution and time dynamics are generated. The original brightness distribution data is subjected to dual sampling processing in the spatial domain and the temporal domain. Spatial domain sampling is used to analyze whether there is a regional deviation 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 index and dynamic response index is constructed to comprehensively quantify the compensation effect. The static uniformity index is 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 index focuses on the response characteristics of the display device to the brightness change during the signal driving process, 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 Micro-LED micro-display devices 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 at different time points, generates compensation effect quantitative data, and reflects the specific effects of compensation, such as the improvement of brightness uniformity, the degree of improvement of contrast, and the optimization effect of response time. Multi-dimensional feature analysis is performed on the compensation effect quantitative 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 results. Parameter optimization configuration is performed based on the compensation parameter evaluation results. Combined with the display uniformity data and evaluation model analyzed in the previous stage, the key parameters in the compensation circuit, including driving voltage, scanning timing, and compensation coefficient, are adjusted to generate the optimal target compensation configuration parameters.

[0019] In one example, 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, threshold voltage, mobility and parasitic capacitance parameters are stored in a compensation parameter database, and pixel compensation reference values ​​are obtained, including: Collect data on pixel size, pixel spacing and pixel arrangement to obtain pixel array structural parameters; Perform parameter analysis on the pixel array structure parameters and construct a 2T1C driving circuit compensation model including a switching transistor T1, a driving transistor T2 and a storage capacitor C; Measure the parameters of a single pixel unit in the 2T1C driving circuit compensation model to obtain the threshold voltage parameters; Conducting electrical characteristic analysis on a single pixel unit to obtain mobility parameters, and conducting equivalent circuit modeling on a single pixel unit to obtain parasitic capacitance parameters; Inputting threshold voltage parameters, mobility parameters and parasitic capacitance parameters into a compensation parameter database for storage; The data in the compensation parameter database is weightedly calculated, wherein the weight coefficient is dynamically adjusted according to the pixel position to obtain a weight coefficient matrix, and the compensation parameters are weighted averaged based on the weight coefficient matrix to obtain a pixel compensation reference value.

[0020] In this example, the physical structural parameters of the pixel array, including the size of the pixel unit, are obtained by high-precision measurement equipment. , Pixel Pitch and the arrangement of pixels. The pixel arrangement is represented by a structure matrix , where each element The geometric characteristics of the pixel unit at the corresponding position are represented. Based on these structural parameters, the actual physical layout of the pixel array is understood. The pixel array structural parameters are analyzed to construct the , drive transistor and storage capacitor The 2T1C drive circuit compensation model. The switching transistor in this model Current It is expressed as: ; in, yes The migration rate, is the capacitance per unit area of ​​the gate oxide layer, and They are The width and length, is the gate-source voltage, is the threshold voltage. The driver transistor Current A similar expression is: ; The variables and The definition is similar to that of Storage capacitor The voltage maintenance capability is described as: ; in is the voltage on the storage capacitor, is the amount of stored charge, is the storage capacitor 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 parameters of a single pixel unit are measured. This parameter is the key characteristic of the transistor's on state and is obtained by testing the relationship curve between gate-source voltage and drain current. Analyze the electrical characteristics of the pixel unit and measure the mobility . Mobility Describes the movement rate of carriers in the transistor, and its calculation formula is: ; in 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, is the gate-source voltage, is the threshold voltage. Equivalent circuit modeling of the pixel unit is performed to measure the parasitic capacitance The parasitic capacitance is obtained by analyzing the high-frequency electrical characteristics or measuring the equivalent impedance of the pixel unit using test equipment. , and The input compensation parameter database is stored. In order to comprehensively analyze the compensation parameters of the entire pixel array, the data in the database is weighted, where the weight coefficient Dynamically adjusted according to pixel position. The weight calculation formula is: ; in Indicates pixel position The compensation parameter set includes , and . Weight coefficient Dynamically adjust based on factors such as edge effects at pixel locations and signal transmission path length. Based on weight coefficient matrix , perform weighted average calculation on the compensation parameters to obtain the pixel compensation reference value The formula for weighted average is: ; in is the size of the pixel array. By calculating the above formula, the compensation reference value of the entire pixel array is obtained. , which contains the comprehensive characteristic analysis results of all pixel units.

[0021] In one example, a compensation network is constructed 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, including: Performing differential 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; 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, and calculate 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; Input the compensation capacitor value, compensation transistor size and feedback resistor value into the timing simulation model to obtain the circuit response characteristics; The compensation effect of the circuit response characteristics 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.

[0022] In this example, the threshold voltage parameter of each pixel unit is extracted from the compensation parameter database and defined as ,in Indicates the position of the pixel unit in the array. By calculating the threshold voltage deviation of each pixel , and obtain the threshold voltage deviation distribution diagram. The deviation calculation formula is: ; in is the average threshold voltage of all pixels in the array, calculated as: ; in, and are the number of rows and columns of the pixel array respectively. Based on the threshold voltage deviation distribution diagram, the deviation characteristics of different areas in the pixel array are identified. For example, there is a larger threshold voltage deviation in the edge area, while the central area is relatively uniform. According to these distribution characteristics, the layout of the differential structure compensation branch is planned. Differential compensation realizes real-time detection and correction of voltage deviation through circuits. The layout planning divides the pixel array into multiple compensation areas according to the deviation distribution diagram, and designs a differential structure compensation branch for each area to deal with the threshold voltage deviation of the corresponding area. 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 function of the differential amplifier is to amplify the threshold voltage difference between pixels and output voltage The formula is: ; in is the differential-mode gain of the amplifier, and The sample and hold circuit is used to capture the instantaneous voltage signal during the pixel driving process. Its output voltage It is expressed as: ; in The function of the comparator is to compare the signal output by the differential amplifier with the reference voltage. Comparison is performed and a logic signal is output to trigger compensation action. The output logic of the comparator is expressed as: ; To ensure the accuracy of the compensation circuit, key device parameters are calculated, including the capacitance of the compensation capacitor. , the size of the compensation transistor (width and length ) and the resistance of the feedback resistor The calculation formula of compensation capacitor value is: ; in To compensate for the charge, is the corresponding voltage change. The size of the compensation transistor is adjusted according to its current driving capability and threshold voltage deviation, and the size parameter is calculated by the following formula: ; in 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 of the feedback resistor is determined by the compensation time constant, and the calculation formula is: ; in is the time constant, is the feedback capacitor. After completing the circuit parameter calculation, the compensation capacitor value, compensation transistor size and feedback resistor value 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: ; in is the complex frequency variable in the Laplace transform. The simulation results include circuit gain, response time, stability and other indicators. Based on the pixel compensation reference value, the compensation effect of the simulated circuit response characteristics is analyzed. The performance of the compensation network is evaluated by calculating the uniformity compensation error data. The uniformity compensation error is defined as: ; in Represents the root mean square error, the smaller the error, 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. , feedback resistance or compensation capacitor , thereby improving 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 threshold voltage deviation in the pixel array.

[0023] In one example, the power metal routing is segmented based on the threshold voltage compensation network to obtain the IRDrop compensation parameters, including: 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; The metal routing is segmented according to the voltage drop value, and the width and wiring level of each segment of the routing are designed to obtain a segmented layout diagram of the metal routing; 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 for dynamic compensation calculation to obtain the compensation voltage value, and the compensation voltage value is verified and tested and parameter optimized to obtain the IR Drop compensation parameter.

[0024] In this example, based on the threshold voltage compensation network, the resistance distribution of the power supply metal trace is simulated. By establishing the geometric model and material properties of the trace, combined with the physical properties of the miniaturized device, the equivalent resistance distribution data of the metal trace is calculated. Assume that the power supply trace is divided into several small segments, and the resistance of each segment is recorded as , then the total resistance It is expressed as: ; in, is the resistivity of the trace material, It is The length of the segment trace, is its cross-sectional area. Through simulation, the equivalent resistance value of each section in the metal trace is obtained, and the 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 value at different pixel positions. The voltage drop is calculated based on Ohm's law, combined with the trace current and its equivalent resistance , the voltage drop of each segment is expressed as: ; Assume that the number of nodes in the power supply network is , then the linear equations for voltage drop are established through the grid flow relationship of current: ; in is the node voltage drop vector, is the equivalent resistance matrix between nodes, is the node current vector. After solving this set of equations, the voltage drop value at each pixel position is obtained According to the calculated voltage drop value, the metal trace is segmented to optimize the trace design and reduce the overall voltage drop. The principle of segmentation is to divide the area with large voltage drop into independent segments according to the distribution characteristics of the voltage drop value, and to adjust the width of each trace. The width design formula is: ; in is the maximum voltage drop allowed. The wiring hierarchy design needs to consider the interconnection effect of multi-layer routing to balance electrical performance and manufacturing complexity. After the design is completed, a metal routing segment layout diagram is generated, in which the specific parameters of each segment area are based on the optimization calculation results. After the layout diagram is completed, in order to ensure the voltage stability of the power supply network during operation, a voltage detection circuit is designed for each segment area to build a real-time voltage monitoring network. The voltage detection circuit measures the voltage value on the segmented routing in real time by embedding a voltage sensor The output characteristic of the sensor is expressed as: ; in is the actual voltage on the trace, It is the measurement noise. The noise interference is eliminated by filtering and signal processing the detected voltage value. Based on the real-time voltage monitoring network, the historical voltage drop data of the routing is collected, and the 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 by the time series analysis method. For example, the first-order autoregressive model is used Represents the dynamic characteristics of voltage drop: ; in, is the regression coefficient, is the prediction error term. The historical data is fitted by the least square method to determine the parameters of the prediction model and predict the future voltage drop trend. The voltage drop prediction model is input into the prediction-feedback compensation unit to perform dynamic compensation calculations on the power supply of different segmented areas. The compensation unit compares the predicted voltage drop value with the target voltage in real time. , calculate the compensation voltage value that needs to be applied : ; in is the predicted voltage drop value. Compensation voltage value The compensation circuit is applied to the corresponding routing node to offset the voltage drop. After the compensation calculation is completed, the compensation voltage value is verified and tested, and the compensation effect is improved through parameter optimization. The verification test is performed by measuring the actual voltage drop value after compensation. , calculate its root mean square error: ; If the error is large, it is necessary to adjust the parameters of the compensation unit, such as 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 in the operation of micro-display devices to effectively improve the voltage stability of the power supply network and the uniformity of pixel drive, thereby improving display performance.

[0025] In one example, the IR Drop compensation parameter is input into the Markov decision inference model of the double-layer compensation factor to perform process compensation calculation, and process compensation data is obtained, including: 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 a first-layer compensation factor, and construct a device compensation feature matrix based on the first-layer compensation factor; Acquire process parameters, including etching depth, doping concentration and ion implantation energy, and extract features of the process parameters to obtain second-layer compensation factors, and construct a process compensation feature matrix based on 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 the compensation factor coupling matrix; 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, and a state transfer model is constructed according to the process parameter fluctuation law to obtain the process fluctuation state transfer matrix. The dynamic programming algorithm is used to solve the optimal compensation strategy for the process fluctuation state transfer matrix, and the compensation strategy sequence is obtained through iterative calculation of the Bellman equation. The target interval judgment 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 compensation magnitude to obtain the process compensation data.

[0026] In this example, for device characteristic deviation parameters, the threshold voltage deviation ( )、Channel length deviation( ) and oxide thickness deviation ( ) are key parameters. These parameters describe the changes in electrical and physical properties of micro-display devices caused by process fluctuations in actual manufacturing. By statistically analyzing these data, it is expressed in matrix form , where each row corresponds to the deviation parameter of a pixel unit. In order to effectively use this data, feature extraction is performed on the device characteristic deviation parameters. Feature extraction is achieved through principal component analysis, which reduces the dimension of high-dimensional deviation data and extracts the main influencing factors. For example, the goal of principal component analysis is to transform data from the original space Projection to the new feature space , the formula is: ; in is the bias parameter matrix, is the projection matrix, which consists of eigenvectors. The main features extracted are defined as the first-level compensation factors, which are used to quantify the impact of different deviations on device performance. After obtaining the first-level compensation factors, the device compensation feature matrix is ​​constructed. , which is of the form: ; in Indicates Compensation factors. Obtain process parameters, including etching depth ( ), doping concentration ( ) and ion implantation energy ( ). These parameters reflect the accuracy and consistency of material handling during the manufacturing process and are also expressed as matrices The feature extraction method for process parameters is similar to that for device characteristic parameters. The original parameters are reduced in dimension using the principal component analysis method, the second-layer compensation factors are extracted, and the process compensation feature matrix is ​​constructed. , which is of the form: ; in It is In order to comprehensively analyze the relationship between device characteristic compensation factors and process compensation factors, and Perform correlation analysis and regularization. By constructing the coupling matrix , quantifies the interaction between these two types of factors. Each element of the coupling matrix Device Characteristic Factor and process compensation factor The correlation is calculated as: ; Where Cov represents covariance, and They are and Through regularization processing, the influence of noise is eliminated and the final compensation factor coupling matrix is ​​obtained The IR Drop compensation parameter and compensation factor coupling matrix are input into the Markov decision inference model of the double-layer compensation factor to describe the dynamic evolution of process parameter fluctuations. The Markov model is based on the state transfer matrix The probability of describing a parameter changing from one state to another is defined as follows: ; in Is the current state Transfer to next state The probability of the state transition is calculated by multiple iterations to generate the process fluctuation state transition matrix. 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 that the cost function is , whose recurrence relationship is described by the Bellman equation: ; in Take action The immediate benefits is a discount factor used to weigh current returns and future returns. The compensation strategy sequence is generated through iterative calculation of the Bellman equation. The target interval determination and variance analysis are performed on the compensation strategy sequence to evaluate the stability and effect of different strategies. Combined with the least squares fitting, the compensation parameters are optimized to further improve the accuracy. The fitting formula is: ; in are the 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 compensation magnitude to form complete process compensation data.

[0027] In one example, the timing optimization of addressing control is performed based on process compensation data, and a dynamic time slot allocation scheme is used for time division multiplexing control to obtain row and column scanning signal timing parameters, including: Perform timing feature analysis on process compensation data, extract row scan signal frequency, data writing time and storage time parameters, and obtain initial timing configuration data; Based on the initial timing configuration data, a dynamic time slot allocation model is constructed, a threshold for the ratio of data writing to storage time is set, and time-division multiplexing benchmark data is obtained; Design the row and column scanning control circuit according to the time-division multiplexing reference data, and synchronize the row selection signal with the column data signal to obtain a scanning timing diagram; Dynamically optimize the time slot interval in the scanning timing diagram, adjust the multiplexing ratio according to the display data characteristics, and obtain a dynamic time slot allocation scheme; Input the dynamic time slot allocation scheme into the timing controller to perform time division multiplexing simulation and obtain multiplexing control parameters; According to the multiplexing control parameters, the chip pins are mapped and designed, the data transmission channels are optimized and configured, and the pin allocation data is obtained; 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 timing control is optimized to obtain row and column scanning signal timing parameters.

[0028] In this example, timing feature analysis is performed on the process compensation data 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 ( )、Data writing time( ) and storage time ( ). These parameters are described by formulas, for example, the frequency of the row scan signal is: ; in It is the time of a single line scan, including data writing time and signal switching time. A dynamic time slot allocation model is built 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. , which is used to balance the working efficiency of the driving circuit and the stability of the pixel brightness. The relationship between the proportional threshold is expressed as: ; By optimizing the ratio threshold under different row scanning conditions, the time-division multiplexing reference data is obtained to guide the rules of time slot allocation. Based on the time-division multiplexing reference data, the row and column scanning control circuit is designed, and the synchronization design of the row selection signal and the column data signal is completed. In the row and column scanning, the row selection signal ( ) and column data signals ( ) must work in coordination to ensure the correct driving of the pixels. The row strobe signal is expressed as: ; in and Respectively represent the start time and end time of row strobe. The column data signal switches synchronously with the row strobe signal to ensure that data is written during each row scan. The scan timing diagram is generated through circuit design to intuitively show the switching and coordination relationship of the signal. After completing the initial scan timing design, the time slot interval in the scan timing diagram is dynamically optimized. The time slot interval ( ) directly affects the refresh rate and power consumption of the display, and is adjusted according to the characteristics of the display data. For example, for areas with large brightness changes, the write 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: ; in 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 into 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 and column scanning signals and outputs the 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 a scan timing. According to the multiplexing control parameters, the pins of the chip are mapped to achieve efficient allocation of data transmission. In the mapping design, the layout of the data transmission channel and the signal allocation method are optimized to reduce latency and improve transmission reliability. The pin allocation data is represented by a vector: ; in Indicates The optimized pin assignment ensures balanced loads on each channel and reduces signal interference. Based on the pin assignment data, the timing of data writing and compensation process is matched. By adjusting the data writing time and compensation time , generate a synchronous control sequence. The core of the synchronous control sequence is to ensure the temporal coordination of the row and column scanning signals and the compensation signals, and its formula is: ; in It is the delay time of signal transmission. After verification and optimization, the synchronous control sequence is input into the drive timing control unit to improve display performance. By optimizing the drive timing control of the synchronous control sequence, the final timing parameters of the row and column scanning signals are generated. These parameters include the start and end time of each row scan, the interval of column data switching, and the application time of the compensation signal.

[0029] In one example, the photoelectric characteristics of the row and column scanning signal timing parameters are tested, 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; Based on the test system configuration data, the brightness uniformity of the Micro-LED micro display device is scanned to obtain the original brightness distribution data, and the original brightness distribution data is double sampled in the spatial domain and the temporal domain to obtain the display uniformity data; An evaluation system of static uniformity index and dynamic response index is constructed based on display uniformity data to obtain a compensation effect evaluation model; Input the display uniformity data into the compensation effect evaluation model for parameter analysis to obtain the compensation effect quantitative data; A multi-dimensional feature analysis is performed on the compensation effect quantitative data, and an evaluation index matrix including brightness uniformity, contrast and response time is established to obtain the compensation parameter evaluation results. Based on the compensation parameter evaluation results, parameter optimization configuration is performed to obtain the target compensation configuration parameters.

[0030] In this example, the row and column scan signal timing parameters are loaded with drive timing to build 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 real-time measurement of the brightness response of the Micro-LED display. The core of signal loading is to ensure the time accuracy of the row and column scan signals. The frequency of the row signal is expressed as: ; in, is the time for a single line scan, including data writing time and storage time. After the test platform is built, the brightness uniformity of the Micro-LED display device is scanned based on the configured test system. The pixel array is scanned line by line through the optoelectronic test system to obtain the brightness data of each pixel unit. Assume that the brightness of each pixel unit is ,in is the pixel position, is the time point. The brightness distribution data obtained by scanning is expressed as a three-dimensional matrix The original brightness distribution data is sampled in both the spatial domain and the temporal domain. Spatial domain sampling is used to extract the brightness distribution characteristics of different rows and columns in the pixel array, while temporal domain sampling is used to analyze the dynamic behavior of pixel brightness over time. The formula for spatial sampling is: ; in is the total number of sampling times. The formula for time sampling is: ; in and is the number of rows and columns of the pixel array. After sampling, display uniformity data is generated, including brightness change information in spatial and temporal dimensions. Based on the display uniformity data, an evaluation system including static uniformity index and dynamic response index is constructed to quantify the performance of the display. The static uniformity index is used to describe the consistency of pixel brightness in space, which is achieved by calculating the standard deviation of brightness. The formula is: ; in is the average brightness of the entire array. The dynamic response index is used to evaluate the temporal stability of pixel brightness, which is achieved by calculating the rate of change of time sampling data. The formula is: ; After the evaluation system is built, the display uniformity data is input into the compensation effect evaluation model for parameter analysis to obtain the compensation effect quantitative data. The compensation effect quantitative data reflects the actual improvement of the compensation network on brightness uniformity and dynamic response. Based on these data, a multi-dimensional feature analysis is performed to construct an evaluation index matrix including brightness uniformity, contrast and response time. The index matrix is ​​expressed as: ; in, is the contrast ratio, defined as the ratio of the maximum to the minimum brightness: ; The compensation effect is analyzed through the evaluation index matrix, and the compensation parameter evaluation results are generated. Based on the evaluation results, the 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, and the formula is: ; in, is the optimal solution of compensation parameters, is the input feature matrix, is the target brightness vector. The optimized compensation parameters are stored as target compensation configuration parameters and are used to adjust the driving conditions of the display device.

[0031] Reference Figure 2 This embodiment provides a pixel addressing device for an ultra-high pixel density Micro-LED micro display device, comprising: Acquisition module 1 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; Construction module 2, 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, to obtain a threshold voltage compensation network; Segmentation module 3, used to segment the power supply metal routing based on the threshold voltage compensation network to obtain the IRDrop compensation parameter; Compensation module 4, used for inputting IR Drop compensation parameters into the Markov decision reasoning model of the double-layer compensation factor to perform process compensation calculation and obtain process compensation data; Control module 5, used for optimizing the timing of addressing control based on process compensation data, and adopting a dynamic time slot allocation scheme to perform time-division multiplexing control to obtain row and column scanning signal timing parameters; The test module 6 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.

[0032] In this embodiment, for the specific implementation of each unit in the above device embodiment, please refer to the above method embodiment, which will not be repeated here.

[0033] An embodiment of the present invention further provides a pixel addressing system for an ultra-high pixel density Micro-LED micro-display device, on which a computer program is stored, and the above method is implemented when the computer program is executed by a processor. It can be understood that the pixel addressing system for an ultra-high pixel density Micro-LED micro-display device in this embodiment can be a volatile readable storage medium or a non-volatile readable storage medium.

[0034] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and 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-mentioned methods. Among them, any reference to memory, storage, database or other media provided by the present invention and used in the embodiments may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in a variety of forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double-speed data rate SDRAM (SSRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM.

[0035] It should be noted that, in this article, the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, device, article or method including a series of elements includes not only those elements, but also includes other elements not explicitly listed, or also includes elements inherent to such process, device, article or method. In the absence of further restrictions, an element defined by the sentence "includes a ..." does not exclude the presence of other identical elements in the process, device, article or method including the element.

[0036] The above description is only a preferred embodiment of the present invention, and does not limit the patent scope of the present invention. Any equivalent structure or equivalent process transformation made by using the contents of the present invention specification and drawings, or directly or indirectly applied in other related technical fields, are also 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 timing parameters of row and column scanning signals; 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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