A silver paste coating conductive uniformity testing system and testing method

Abstract

The present invention discloses a silver paste coating conductive uniformity testing system and testing method, which specifically relates to the technical field of silver paste coating conductive testing, and is used to solve the problem of inaccurate silver paste coating conductive uniformity testing; the present invention uses a high-precision pressure sensor and a camera device to monitor the contact state data of the probe at each measuring point in real time, generates a contact state matrix to support accurate correction of contact resistance, collects data and standardizes it, uses a support vector regression model to capture nonlinear relationships, optimizes the compensation model through cross-validation and parameter tuning, sets an optimal pressure range to stabilize contact conditions and smooth data, uses multi-channel data acquisition and distributed sampling synchronization algorithm to correct time deviation, and combines the compensation coefficient to correct errors, and finally calculates the conductivity uniformity index through the resistance data matrix, generates a thermal map classification evaluation, repairs low-uniformity areas, and significantly improves the consistency and reliability of the conductivity uniformity test.

Description

Technical Field

[0001] The present invention relates to the technical field of silver paste coating conductivity testing, and more particularly to a silver paste coating conductivity uniformity testing system and testing method. Background Art

[0002] During the coating process, due to the influence of factors such as equipment, environment and material properties, the thickness and particle distribution of the silver paste coating may be uneven, resulting in deviations in conductivity in different areas. If the conductivity of the silver paste coating is uneven, it will directly affect the overall performance of the electronic device and may even cause problems such as unstable current distribution and abnormal local resistance in the later use of the product. Therefore, conductive uniformity testing has become a key link in the quality control of silver paste coating.

[0003] Deficiencies in existing technologies: Due to the micro-roughness of the silver paste coating surface or poor probe contact, the contact resistance at different positions during the test will fluctuate significantly. This fluctuation not only affects the accuracy of the conductivity test in each area, but also easily leads to the accumulation of data deviations, reducing the accuracy of the overall uniformity assessment. The existing technology mostly uses a manual adjustment method with fixed contact pressure, which is difficult to adapt to the dynamically changing contact resistance requirements and lacks an effective real-time compensation mechanism. Especially in the multi-point test process, the unevenness of the contact resistance will be aggravated by factors such as ambient temperature and probe wear, making it difficult to achieve consistency in each measurement result, resulting in measurement errors that are difficult to control. Summary of the Invention

[0004] In order to overcome the above-mentioned defects of the prior art, the present invention provides a silver paste coating conductive uniformity testing system and testing method to solve the problem of inaccurate silver paste coating conductive uniformity testing in the above-mentioned background technology.

[0005] To achieve the above object, the present invention provides the following technical solutions:

[0006] A method for testing the conductive uniformity of a silver paste coating comprises the following steps:

[0007] At each measurement point, a pressure sensor is used to monitor the contact pressure and area of ​​the probe, and a camera is used to obtain surface roughness characteristics. The frequency domain information is analyzed using Fourier transform, and the contact resistance is corrected by combining the pressure and current-voltage data and integrated into a contact state matrix.

[0008] The collected data is standardized and nonlinear relationships are captured using support vector regression. After cross-validation and parameter tuning, an optimized contact resistance compensation model is generated for real-time resistance data adjustment to improve conductivity consistency and set the optimal pressure range based on the compensation model.

[0009] Contact pressure, area, roughness, and resistance data are collected in real time, input into the compensation model to correct the resistance value, and smoothed using a weighted moving average algorithm. The smoothed data is monitored and, if the deviation exceeds the tolerance range, the contact resistance compensation model is adaptively adjusted.

[0010] Multi-channel data acquisition and distributed sampling synchronization algorithms are used to synchronize measurement points, correct time deviations, and perform error correction in combination with compensation coefficients. The uniformity index is calculated based on the final resistance data matrix, a thermal map is generated, conductivity is classified and evaluated, and low-uniformity areas are repaired.

[0011] In a preferred embodiment, a pressure sensor is used to monitor the contact pressure and area of ​​the probe at each measurement point, and a camera is used to obtain surface roughness characteristics. The frequency domain information is analyzed using Fourier transform, and the contact resistance is corrected by combining the pressure and current and voltage data and integrated into a contact state matrix. The specific process is as follows:

[0012] At each measurement point, the pressure sensor collects the probe's contact pressure data in real time to generate a pressure matrix;

[0013] Calculate the actual contact area at each measurement point based on the measured contact pressure matrix;

[0014] A micro-camera device is used for microscopic scanning to generate high-resolution image data of the probe contact point. The image is analyzed in the frequency domain by Fourier transform to extract surface roughness features and generate a roughness matrix.

[0015] The contact resistance value is obtained by measuring the current and voltage, and the contact pressure, contact area and contact resistance are integrated to form a contact state matrix.

[0016] In a preferred embodiment, the collected data is standardized and nonlinear relationships are captured using support vector regression. The specific process is as follows:

[0017] The collected data is contact status data, which includes contact pressure, contact area, surface roughness and preliminary contact resistance measurement values;

[0018] The contact status data were converted into a standard distribution with zero mean and unit variance through Z-score standardization;

[0019] After completing data normalization, multiple linear regression was used to preliminarily model the relationship between contact state data and contact resistance to determine the effects of contact pressure, contact area, and roughness on contact resistance.

[0020] Support vector regression is used to model the data. The data is mapped from the original space to a high-dimensional space through kernel function mapping, thereby determining the nonlinear relationship in the data.

[0021] In a preferred embodiment, after cross-validation and parameter tuning, an optimized contact resistance compensation model is generated for real-time resistance data adjustment to improve conductivity consistency, and the optimal pressure range is set according to the compensation model. The specific process is as follows:

[0022] After establishing the preliminary support vector regression model, the K-fold method was used for cross-validation, and the penalty coefficient and kernel function parameters of the support vector regression were tuned using the grid search method;

[0023] After obtaining the optimized support vector regression model, the corresponding contact resistance compensation coefficient is calculated based on the contact state data of each measurement point. The contact resistance compensation coefficient formula is defined as: in, is the contact resistance compensation value predicted by the model, R i，j is the measured value of the initial contact resistance, K i，j The compensation coefficient for each measurement point characterizes the compensation relationship between contact state and resistance;

[0024] Integrate the contact resistance compensation coefficients of all measurement points to generate a compensation coefficient matrix, which is then corrected using the measured values ​​from the conductivity uniformity test.

[0025] Determine the optimal contact pressure range for each measurement point based on the contact resistance compensation model;

[0026] Apply initial contact pressure via a piezoelectric regulator or micro-electric device and collect real-time feedback of contact resistance values;

[0027] The feedback contact resistance value is used as the initial input of PID control;

[0028] The probe's contact pressure is adjusted through PID, and the resistance feedback value is collected in real time. After each adjustment, the PID controller recalculates the contact pressure deviation and adjusts the pressure to the optimal pressure range until the target pressure is reached.

[0029] When the probe contact pressure remains within the optimal pressure range and the resistance is stable, the pressure adjustment is completed and the optimal measurement state is reached, and the final contact pressure and resistance values ​​are recorded as the acquisition data of the measurement point.

[0030] In a preferred embodiment, contact pressure, area, roughness, and resistance data are collected in real time, input into a compensation model to correct the resistance value, and smoothed using a weighted moving average algorithm. The smoothed data is monitored, and if the deviation exceeds the tolerance range, the contact resistance compensation model is adaptively adjusted. The specific process is as follows:

[0031] Collect contact pressure, contact area, roughness and contact resistance data in real time. After acquiring the real-time data, input it into the established contact resistance compensation model to calculate the real-time contact resistance compensation coefficient of each measurement point.

[0032] The contact resistance value measured in real time is corrected by the contact resistance compensation coefficient, and the corrected contact resistance value is smoothed by using the weighted moving average algorithm;

[0033] The tolerance range is set for the smoothed resistance data and the contact resistance compensation model is dynamically adjusted.

[0034] In a preferred embodiment, multi-channel data acquisition and a distributed sampling synchronization algorithm are used to synchronize measurement points, correct time deviations, and perform error correction in combination with compensation coefficients. The uniformity index is calculated based on the final resistance data matrix, a thermal map is generated, conductivity is classified and evaluated, and low uniformity areas are repaired. The specific steps are as follows:

[0035] In the process of synchronous data collection, a distributed sampling synchronization algorithm is used to balance and correct the sampling deviation of each measurement point;

[0036] After acquiring the synchronous data, the deviation between the resistance data of each measurement point is compared and the compensation coefficient matrix is ​​applied to make corrections;

[0037] After error compensation, the resistance difference between different measurement points is calculated to evaluate the uniformity of the data, and the measurement points are corrected twice using the consistency correction algorithm;

[0038] After the data of all measurement points have passed consistency analysis and correction, the final corrected resistance data of each measurement point is recorded and a synchronously corrected resistance data matrix is ​​generated;

[0039] The resistance data matrix was analyzed, and the median, interquartile range and distribution density of the distribution characteristics were combined to generate the conductivity uniformity index;

[0040] Visualize the resistance data matrix to generate a resistance uniformity distribution heat map;

[0041] Based on the conductivity uniformity index and resistance distribution thermal map, a comprehensive judgment and repair is made on the conductivity uniformity of the silver paste coating.

[0042] A silver paste coating conductive uniformity testing system, used to implement the above-mentioned silver paste coating conductive uniformity testing method, comprising:

[0043] The measurement module is used to monitor the contact pressure and area of ​​the probe at each measurement point using a pressure sensor, obtain surface roughness characteristics through a camera, analyze frequency domain information using Fourier transform, and correct the contact resistance by combining pressure and current and voltage data to integrate into a contact state matrix;

[0044] The compensation determination module is used to standardize the collected data, capture nonlinear relationships based on support vector regression, and generate an optimized contact resistance compensation model after cross-validation and parameter tuning. This model is used for real-time resistance data adjustment to improve conductivity consistency and set the optimal pressure range based on the compensation model.

[0045] The model adjustment module is used to collect contact pressure, area, roughness and resistance data in real time, input the compensation model to correct the resistance value, and smooth it using a weighted moving average algorithm. The smoothed data is monitored and, if the deviation exceeds the tolerance range, the contact resistance compensation model is adaptively adjusted.

[0046] The test and repair module is used to synchronize measurement point acquisition through multi-channel data acquisition and distributed sampling synchronization algorithm, correct time deviation, and perform error correction in combination with compensation coefficients. It calculates the uniformity index based on the final resistance data matrix, generates a thermal map, classifies and evaluates conductivity, and repairs low-uniformity areas.

[0047] The technical effects and advantages of the present invention are as follows:

[0048] The present invention uses high-precision pressure sensors and camera devices at each measuring point to monitor and collect data on the probe contact pressure, contact area and surface roughness in real time, and combines Fourier transform to analyze surface frequency domain information to generate a contact state matrix to support the accurate calculation and correction of contact resistance. After the collected data is standardized, the support vector regression model is used to capture the nonlinear relationship. The optimized compensation model is obtained through cross-validation and parameter tuning to achieve precise adjustment of the real-time resistance data and set the optimal pressure range to stabilize the contact conditions. At the same time, the real-time data is smoothed and deviations are monitored by the weighted moving average algorithm, and the contact resistance compensation model is adaptively adjusted to ensure data stability. The synchronous acquisition of measurement points is achieved according to the multi-channel data acquisition and distributed sampling synchronization algorithm, the time deviation is corrected, and the error is further corrected in combination with the compensation coefficient. Finally, the conductivity uniformity index is calculated through the resistance data matrix and a thermal map is generated for classification evaluation and repair of low-uniformity areas, which significantly improves the overall consistency and reliability of the silver paste coating conductivity uniformity test. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] Figure 1 The present invention provides a flow chart of a method for testing the conductive uniformity of a silver paste coating.

[0050] Figure 2The figure is a structural schematic diagram of a silver paste coating conductive uniformity testing system of the present invention. DETAILED DESCRIPTION

[0051] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0052] Example 1: Figure 1 As shown, a method for testing the conductive uniformity of a silver paste coating comprises the following steps:

[0053] At each measurement point, a pressure sensor is used to monitor the contact pressure and area of ​​the probe, and a camera is used to obtain surface roughness characteristics. The frequency domain information is analyzed using Fourier transform, and the contact resistance is corrected by combining the pressure and current-voltage data and integrated into a contact state matrix.

[0054] The collected data is standardized and nonlinear relationships are captured using support vector regression. After cross-validation and parameter tuning, an optimized contact resistance compensation model is generated for real-time resistance data adjustment to improve conductivity consistency and set the optimal pressure range based on the compensation model.

[0055] Contact pressure, area, roughness, and resistance data are collected in real time, input into the compensation model to correct the resistance value, and smoothed using a weighted moving average algorithm. The smoothed data is monitored and, if the deviation exceeds the tolerance range, the contact resistance compensation model is adaptively adjusted.

[0056] Multi-channel data acquisition and distributed sampling synchronization algorithms are used to synchronize measurement points, correct time deviations, and perform error correction in combination with compensation coefficients. The uniformity index is calculated based on the final resistance data matrix, a thermal map is generated, conductivity is classified and evaluated, and low-uniformity areas are repaired.

[0057] Step 1: Real-time monitoring and data collection of the probe contact status. A high-precision pressure sensor is used to monitor the contact pressure and contact area between the probe and the silver paste coating surface in real time. Slight changes in contact pressure and contact area will cause fluctuations in contact resistance. Therefore, the probe contact status data is recorded at each measurement point. The contact status can provide a basic basis for contact resistance compensation. At the same time, a micro-camera device is used to perform a microscopic scan of the probe contact area to identify the surface roughness and probe contact status.

[0058] At each measurement point, the pressure sensor collects the contact pressure data of the probe in real time and generates a pressure matrix P i，j, where i and j represent the horizontal and vertical coordinates of the measurement point, respectively. A small change in the probe contact pressure will significantly affect the contact resistance. Therefore, the pressure value at each point must be accurately collected. The pressure value is calculated using the following formula: Where α is the sensor sensitivity coefficient, which represents the pressure change per unit voltage output, and β is the offset correction factor, which is used to correct the initial pressure error caused by environmental conditions.

[0059] Calculate the actual contact area A at each measurement point based on the measured contact pressure matrix i，j , the contact area directly affects the resistance, so the contact area is corrected by the following formula: Among them, σ r represents the micro-roughness of the silver paste coating, ∈ is a small correction term to avoid the denominator being zero, and γ is a nonlinear adjustment coefficient used to correct the nonlinear relationship of the contact area of ​​the rough surface;

[0060] Perform microscopic scanning and feature extraction of surface roughness. To obtain the surface characteristics of the probe contact area, a micro-camera device is used for microscopic scanning to generate high-resolution image data of the probe contact point. The image is analyzed in the frequency domain by Fourier transform to extract the surface roughness features and generate a roughness matrix: Σ i，j =F{I i，j}, where is the original image data at the measurement point (i, j), F represents the Fourier transform operation, which is used to extract the frequency domain components of the image to calculate the local roughness characteristics;

[0061] Use a high-resolution camera to capture images of the silver paste coating surface, and then use image processing algorithms (such as edge detection and frequency domain analysis) to extract roughness information. Assuming that the height data is Z (x, y), its roughness frequency distribution is obtained through Fourier transform. And it is used to represent the frequency amplitude value after Fourier transform, and the micro-roughness σ can be defined r for;

[0062] By measuring the current I i，j and voltage V i，j , get the contact resistance value R i，j , the contact resistance at each measurement point is calculated according to Ohm's law, which is defined as follows: Among them, δ i，j It is the microscopic fluctuation factor of contact resistance, which represents the resistance correction coefficient caused by small contact unevenness on the rough surface. It is used to correct the contact resistance deviation caused by surface roughness and pressure changes.

[0063] The contact pressure P i，j , contact area A i，j and contact resistance R i，jThe three are integrated to form the contact state matrix C i，j This matrix provides comprehensive reference data for subsequent resistance compensation and data correction.

[0064] θ

[0065] The body expression is as follows: C i，j =(P i，j ·A i，j )·R i，j , where θ is the nonlinear combination factor during matrix integration, which is used to dynamically adjust the weight relationship between pressure, area and resistance; C i，j That is, the complete contact state matrix, which is used in the compensation model to correct the conductivity error caused by uneven probe contact in real time.

[0066] Step 2: Establish and optimize the contact resistance compensation model, that is, standardize and preprocess the contact state data to ensure the consistency of the data dimension and provide a unified data scale for subsequent model training. The contact state data includes contact pressure, contact area, surface roughness, and preliminary contact resistance measurement values. The variables vary greatly in magnitude. Directly inputting them into the model may cause uneven weights and cause the model to favor specific variables. Therefore, all variables are converted to a standard distribution with zero mean and unit variance through Z-score standardization: Among them, X is the original value of the variable, μ X and σ X Denote the mean and standard deviation of each variable, respectively. X′ is the standardized data. After unification of scale, the data has a standard distribution with zero mean and unit variance.

[0067] The standardization process can avoid the influence of different variables due to dimensional differences, so that all variables are calculated with equal weight during model training. The standardized data is more conducive to the convergence and stability of subsequent machine learning algorithms (such as support vector regression SVR);

[0068] After completing the data standardization, multiple linear regression is used to preliminarily model the relationship between contact state data and contact resistance to determine the influence of variables such as contact pressure, contact area and roughness on contact resistance. The multiple regression model is as follows: R′ i，j =β0+β1P′ i,j + β 2A′ i，j +β3σ′ r +∈, where R′ i,j is the normalized contact resistance value, P′ i,j , A′ i，j ,σ′ rare the standardized contact pressure, contact area and roughness respectively, β0 is the bias term of the model; β1, β2, β3 are the regression coefficients to be determined, which represent the linear effect of each variable on the contact resistance; ∈ is the error term, which is used to represent the influence of random noise or unobserved factors;

[0069] Multiple linear regression can quickly establish a preliminary relationship between contact state data and contact resistance, and determine the linear influence weight of each variable on contact resistance. The model can provide a basic prediction value, enabling the subsequent support vector regression (SVR) model to perform more complex nonlinear fitting on this basis. The size of the regression coefficient can help determine the relative importance of contact pressure, contact area, and roughness on contact resistance, which helps to further optimize and adjust the model structure.

[0070] A nonlinear support vector regression (SVR) model was constructed. After establishing a preliminary multivariate regression model, support vector regression (SVR) was used to further model the data in order to improve the model's nonlinear fitting ability for contact resistance fluctuations. The data was mapped from the original space to a high-dimensional space through kernel function mapping, thereby capturing the nonlinear relationship in the data. The basic model form of SVR is as follows: Among them, K((P′ i，j , A′ i，j ,σ′ r ), X k ) represents the kernel function (RBF kernel function is selected here); α k is the weight coefficient of the support vector, which determines the importance of the support vector in nonlinear fitting; b is the bias term;

[0071] It should be noted that the RBF kernel function formula is selected based on its high efficiency in dealing with multivariable nonlinear relationships and can capture the complex nonlinear relationship between contact resistance and contact state data;

[0072] After establishing the initial support vector regression (SVR) model, cross-validation is required to evaluate the generalization ability of the model and further optimize the model performance through hyperparameter tuning. Cross-validation uses the K-fold method to divide the dataset into K subsets, select one subset in turn as the validation set, and the remaining K-1 subsets as the training set. This is repeated K times, and the average error of all rounds is calculated to obtain the stability and accuracy of the model. The score calculation formula for K-fold cross-validation is as follows: Among them, MSE k represents the mean square error of the samples in the k-fold validation set;

[0073] In the cross-validation process, in order to find the best hyperparameter combination that minimizes the validation error, the grid search method is used to tune the SVR hyperparameters (penalty coefficient) and kernel function parameters. The grid search specifies a range of hyperparameter combinations and performs K-fold cross-validation on each combination to find the optimal hyperparameter combination that minimizes the validation error. S The smallest parameter combination of core;

[0074] During model training, L2 regularization can be added to suppress excessive regression coefficients in the model and prevent overfitting. L2 regularization constrains the size of model parameters by adding the sum of squares of regression coefficients to the loss function, thereby improving the stability of the model. The addition of L2 regularization prevents the model from giving excessive weight to certain specific variables during training, especially balancing the relative importance of variables such as contact pressure and contact area.

[0075] The steps for generating the contact resistance compensation coefficient matrix are as follows: After obtaining the optimized support vector regression model, the corresponding contact resistance compensation coefficient is calculated based on the contact state data of each measurement point. The compensation coefficient is used to adjust the measured value of the contact resistance to more accurately reflect the actual conductive performance of the silver paste coating under different contact states. The specific calculation steps are as follows:

[0076] Input contact state data, and input the standardized contact pressure, contact area, and roughness of each measurement point into the optimized SVR model;

[0077] Calculate the compensation coefficient and use the trained SVR model to predict the input data of each measurement point to obtain the contact resistance prediction value after compensation, the contact resistance compensation coefficient K i，j Defined by the following formula: in, is the contact resistance compensation value predicted by the model, R i，j is the measured value of the initial contact resistance, K i，j The compensation coefficient for each measurement point characterizes the compensation relationship between contact state and resistance;

[0078] K of all measurement points i，j The values ​​are integrated to generate the compensation coefficient matrix K, which is used to correct the measured values ​​in the subsequent conductivity uniformity test.

[0079] Step 3: Adaptive adjustment of the probe contact pressure. In order to ensure the stability of the measurement data, the contact pressure needs to be adaptively adjusted. According to the contact resistance compensation model established in step 2, the optimal pressure range of each measurement point is determined. The selection of the optimal pressure range is based on the model's minimization of resistance fluctuations under different pressure values. That is, through the relationship diagram between contact pressure and contact resistance, find the pressure range that can effectively reduce contact resistance fluctuations. The optimal pressure range can be expressed as: Popt =[P min , P max ], where P min and P max The minimum and maximum pressure values ​​predicted by the model ensure that the resistance fluctuation is minimal within this range;

[0080] The optimal pressure range ensures that the probe pressure is sufficiently stable, reduces errors caused by uneven contact, and provides a reference standard for adaptive adjustment, so that the probe can be maintained in the appropriate pressure range in subsequent adjustments;

[0081] After the probe is moved to the measurement position, the initial contact pressure P0 is applied by the piezoelectric regulator or micro-electric device, and the real-time feedback contact resistance value P is immediately collected. init , the initial contact pressure is based on the optimal pressure range P opt A median value is selected within the range to ensure that the initial pressure is close to the optimal range. The initial contact resistance value is collected in real time to facilitate detection of whether the current contact state is stable, providing initial data for feedback control. By applying the initial pressure, it can quickly enter the state close to the target pressure range. The real-time feedback resistance value is used as the initial input for subsequent PID control, which helps to determine whether further adjustment is needed to achieve the ideal pressure.

[0082] Set the ratio of the PID controller (K p ), integral(K i ) and differential (K d ) parameters so that the controller can achieve fast response and stability in pressure regulation. The goal of the PID controller is to make the measured pressure P t Gradually approaching the set optimal range P opt , the formula is as follows: Among them, P t+1 is the pressure value at the next moment t+1, e(t)=P opt -P t is the deviation between the current pressure and the optimal pressure range; K p , K i , K d They are the proportional, integral and differential gain coefficients of the PID controller, which affect the response speed and stability of the system;

[0083] Under the action of PID control, the system continuously adjusts the contact pressure of the probe and collects the resistance feedback value in real time. After each adjustment, the PID controller recalculates the deviation of the contact pressure and adjusts the pressure to P opt In the range, until the target pressure is reached and the contact resistance reaches a stable value R stable,In this process, the resistance value is used as a reference indicator of pressure deviation.,If the resistance change is no longer significant, the contact is considered stable;

[0084] When the probe contact pressure is maintained at P opt If the contact pressure and resistance are within the specified range and the resistance is stable, the pressure adjustment is considered to be completed and the optimal measurement state is reached. The final contact pressure and resistance values ​​are recorded as the collected data of the measurement point for subsequent uniformity analysis and further use of the compensation model.

[0085] Step 4: Real-time contact resistance compensation and data correction. While measuring, the pressure sensor, contact area and other data are input into the compensation model in real time, and the conductivity data is immediately corrected according to the contact resistance compensation coefficient. By combining the real-time resistance data with the compensation parameters in the model, the error caused by poor contact is automatically corrected. The weighted moving average (WMA) algorithm is used to smooth the real-time resistance data to eliminate sudden noise interference, ensuring that the conductivity data is more stable and accurate, close to the actual conductivity performance. The specific steps are as follows:

[0086] First, data such as contact pressure, contact area, roughness, and contact resistance are collected in real time and passed as input to the compensation model. The real-time collected data constitutes the contact state parameter set of the measurement point and provides the basic data for contact resistance compensation. Real-time data collection and input ensures the timeliness of the measurement data, enabling the compensation model to immediately respond to changes in the contact state.

[0087] After acquiring the real-time data, it is input into the previously established contact resistance compensation model to calculate the real-time contact resistance compensation coefficient K of each measurement point. i，j , the compensation coefficient is used to correct the currently measured resistance value: Among them, R i，j is the measured original contact resistance, is the corrected resistance value after applying the compensation coefficient;

[0088] The real-time resistance data after compensation may still be affected by instantaneous fluctuations or external noise. Therefore, the weighted moving average (WMA) algorithm is used to smooth the corrected resistance value to eliminate sudden noise interference. The formula of the weighted moving average algorithm is as follows: in, is the smoothed resistance value; w t is the weighting coefficient, which makes the weight of recent data larger, w t The sum of is 1;

[0089] Real-time monitoring of smoothed resistance data Detect the stability and consistency of the data. When the data deviation is found to exceed the set tolerance range, the error information is fed back to the compensation model so that the model can make adaptive adjustments. The specific tolerance range is set as follows; Among them, ∈ is the allowable error threshold, which is used to determine whether the data deviates from the normal range;

[0090] The error feedback mechanism ensures that the compensation model can be dynamically adjusted to prevent contact resistance from being lost due to environmental fluctuations or changes in contact state. By continuously monitoring and feeding back errors, the compensation model can be continuously optimized to make the measured values ​​more stable and reliable.

[0091] When the contact resistance data After the stability and consistency requirements are met, it is recorded as the final resistance value of the measurement point. This data is recorded in the uniformity analysis data set and serves as the basis for subsequent conductivity uniformity evaluation. The final data format recorded is as follows: Among them, D i，j Represents the final data set of the measurement point (i, j), including contact pressure, contact area, roughness, and final contact resistance after smoothing.

[0092] Step 5: Perform multi-point synchronous calibration and data consistency assessment. Use a multi-channel data acquisition device to arrange measurement channels at each measurement point at the same time to ensure that all measurement points can synchronously collect contact resistance data, set a unique timestamp for each channel, and synchronize all channels in the acquisition device to ensure that data collection is carried out at the same time node. Synchronous recording of each measurement point ensures that data from different measurement points have time consistency.

[0093] In the process of synchronous data acquisition, a distributed sampling synchronization algorithm is used to balance and correct the sampling deviation of each measurement point. The distributed sampling synchronization algorithm can perform time calibration on the collected raw resistance data to ensure that the data of all measurement points are on the same time scale. The calculation process of the algorithm is as follows: T adjusted，i，j =T i，j +ΔT sync , where T i，j is the original timestamp of the measurement point (i, j), ΔT sync The time correction value calculated by the synchronization algorithm ensures that the data of all measurement points are consistent at all times. adjusted，i，j is the timestamp after calibration;

[0094] The distributed sampling synchronization algorithm ensures that the data of each measurement point are strictly aligned in terms of sampling time. By correcting the time deviation, it can eliminate the errors introduced by time asynchrony during data processing.

[0095] After collecting the synchronous data, the measurement error of each measurement point is calculated to evaluate the accuracy of the contact resistance data of each point. By comparing the deviations between the resistance data of each measurement point, the points with larger errors are identified and the compensation coefficient matrix K is applied. i，j Perform error correction: in, is the original measured resistance value of the measuring point (i, j), is the resistance value after correction;

[0096] The error calculation and compensation steps ensure that the resistance data of each measurement point is consistent within the error range. Through the role of the compensation coefficient matrix, the uncertainty in the contact resistance measurement is eliminated, so that the resistance data at different points can more accurately reflect the actual conductivity distribution;

[0097] After error compensation, the data consistency of each measurement point is further analyzed. The resistance difference between different measurement points is calculated to evaluate the uniformity of the data. The consistency correction algorithm is used to perform secondary correction on the measurement points with large deviations to ensure that the resistance values ​​of all measurement points meet the consistency requirements. The consistency calculation formula for resistance difference is as follows: Among them, D i，j is the average resistance between the measurement point (i, j) and all measurement points If D i，j If the allowed deviation threshold is exceeded, Perform secondary calibration to ensure that the resistance data of all measurement points have high consistency;

[0098] After the data of all measurement points have passed consistency analysis and correction, the final corrected resistance data of each measurement point is recorded to generate a synchronously corrected resistance data matrix: This matrix serves as the data basis for uniformity analysis and is used for the overall assessment of the conductivity uniformity of silver paste coatings.

[0099] The final resistance data matrix R final Analyze and calculate its distribution characteristics, mainly including median, interquartile range and distribution density, to avoid the possible misleading of simple characteristics of mean and variance. The median represents the central value of the conductivity distribution, the interquartile range is used to describe the degree of dispersion of the resistance distribution, and the distribution density estimates the resistance value and observes the distribution of resistance values ​​in different intervals.

[0100] Based on the resistance distribution characteristics, a conductivity uniformity index is defined to quantify the conductive consistency of the silver paste coating. This index combines the discreteness and deviation degree of the resistance distribution. The specific definition is as follows: Where M is the median resistance, IQR is the interquartile range, which indicates the discreteness of the resistance; D is the variance of the resistance density estimate, which indicates the discreteness of the distribution density of the resistance value; is the adjustment coefficient, which is used to control the proportion of IQR and D in CUI;

[0101] Visualize the resistance data matrix to generate a resistance uniformity distribution heat map, map the resistance values ​​to different colors, and the color depth indicates the high and low resistance values. Through the color gradient, the conductivity distribution can be intuitively observed.

[0102] Perform heat map color mapping, that is, by using a continuous gradient color spectrum, lower resistance areas are represented by cold colors and higher resistance areas are represented by warm colors, and mark the measurement points with abnormal resistance, especially the points with large deviations from the median;

[0103] Based on the conductivity uniformity index (CUI) and resistance distribution heat map, the conductivity uniformity of the silver paste coating is comprehensively judged and divided into the following grades;

[0104] Setting a first threshold value and a second threshold value for uniform conductivity, a first threshold value and a second threshold value for heat map color, and the first threshold value is greater than the second threshold value;

[0105] Highly uniform (high CUI, uniform thermal map color): indicates that the conductivity of the silver paste coating is consistent and the overall conductivity is stable. In other words, the conductivity uniformity of the silver paste coating that exceeds the first threshold of conductivity uniformity and the first threshold of thermal map color is considered highly uniform.

[0106] Moderate uniformity (medium CUI, slight deviation in thermal map color): The conductivity is basically consistent, but there are small resistance deviations in some areas. In other words, the conductivity uniformity of the silver paste coating that exceeds the second threshold of conductivity uniformity and the second threshold of thermal map color is considered moderately uniform.

[0107] Low uniformity (low CUI, uneven color distribution on the heat map): The conductivity of the silver paste coating varies greatly in different areas, and there may be coating defects or discontinuities. In other words, the conductivity uniformity of the silver paste coating that does not exceed the second threshold of conductivity uniformity and the second threshold of heat map color is considered low uniformity.

[0108] The color distribution in the uniformity distribution heat map identifies areas of high or low resistance and locates points with large conductivity differences. Local repair measures can be taken in these areas, including increasing the silver paste thickness, improving the coating process, or reducing physical damage to the coating. Precision spraying equipment can be used to evenly replenish silver paste in areas with low conductivity to ensure that the thickness meets the preset standard. Areas with high conductivity can be polished or lightly trimmed.

[0109] It should be noted that the thresholds involved in the embodiment can be determined according to specific scenarios and requirements.

[0110] The present invention uses high-precision pressure sensors and camera devices at each measuring point to monitor and collect data on the probe contact pressure, contact area and surface roughness in real time, and combines Fourier transform to analyze surface frequency domain information to generate a contact state matrix to support the accurate calculation and correction of contact resistance. After the collected data is standardized, the support vector regression model is used to capture the nonlinear relationship. The optimized compensation model is obtained through cross-validation and parameter tuning to achieve precise adjustment of the real-time resistance data and set the optimal pressure range to stabilize the contact conditions. At the same time, the real-time data is smoothed and deviations are monitored by the weighted moving average algorithm, and the contact resistance compensation model is adaptively adjusted to ensure data stability. The synchronous acquisition of measurement points is achieved according to the multi-channel data acquisition and distributed sampling synchronization algorithm, the time deviation is corrected, and the error is further corrected in combination with the compensation coefficient. Finally, the conductivity uniformity index is calculated through the resistance data matrix and a thermal map is generated for classification evaluation and repair of low-uniformity areas, which significantly improves the overall consistency and reliability of the silver paste coating conductivity uniformity test.

[0111] Example 2: A silver paste coating conductive uniformity test system, such as Figure 2 As shown, specifically including:

[0112] The measurement module is used to monitor the contact pressure and area of ​​the probe at each measurement point using a pressure sensor, obtain surface roughness characteristics through a camera, analyze frequency domain information using Fourier transform, and correct the contact resistance by combining pressure and current and voltage data to integrate into a contact state matrix;

[0113] The compensation determination module is used to standardize the collected data, capture nonlinear relationships based on support vector regression, and generate an optimized contact resistance compensation model after cross-validation and parameter tuning. This model is used for real-time resistance data adjustment to improve conductivity consistency and set the optimal pressure range based on the compensation model.

[0114] The model adjustment module is used to collect contact pressure, area, roughness and resistance data in real time, input the compensation model to correct the resistance value, and smooth it using a weighted moving average algorithm. The smoothed data is monitored and, if the deviation exceeds the tolerance range, the contact resistance compensation model is adaptively adjusted.

[0115] The test and repair module is used to synchronize measurement point acquisition through multi-channel data acquisition and distributed sampling synchronization algorithm, correct time deviation, and perform error correction in combination with compensation coefficients. It calculates the uniformity index based on the final resistance data matrix, generates a thermal map, classifies and evaluates conductivity, and repairs low-uniformity areas.

[0116] The above formulas are all dimensionless and calculated numerically. Specific dimension removal can be achieved by various means such as standardization, which will not be elaborated here. The formula is a formula obtained by collecting a large amount of data and performing software simulation to obtain the most recent real situation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0117] The above embodiments can be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer program are loaded or executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from one website, computer, server or data center to another website, computer, server or data center by wired or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or data center that contains one or more available media sets. The available medium can be a magnetic medium (e.g., a floppy disk, an ATA hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state ATA hard disk.

[0118] It should be understood that in the various embodiments of the present application, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0119] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0120] In the several embodiments provided in this application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are merely schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0121] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, and may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment as needed.

[0122] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0123] The above description is merely a specific embodiment of the present application, but the scope of protection of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this application should be included in the scope of protection of this application. Therefore, the scope of protection of this application should be based on the scope of protection of the claims.

Claims

1. A method for testing the conductive uniformity of a silver paste coating, characterized in that: The steps include: At each measurement point, a pressure sensor is used to monitor the contact pressure and area of ​​the probe, and a camera is used to obtain surface roughness characteristics. The frequency domain information is analyzed using Fourier transform, and the contact resistance is corrected by combining the pressure and current and voltage data and integrated into a contact state matrix. The specific process is as follows: At each measurement point, the pressure sensor collects the probe's contact pressure data in real time to generate a pressure matrix; Calculate the actual contact area at each measurement point based on the measured contact pressure matrix; A micro-camera device is used for microscopic scanning to generate high-resolution image data of the probe contact point. The image is analyzed in the frequency domain by Fourier transform to extract surface roughness features and generate a roughness matrix. The contact resistance value is obtained by measuring the current and voltage, and the contact pressure, contact area and contact resistance are integrated to form a contact state matrix. The collected data is standardized and nonlinear relationships are captured using support vector regression. After cross-validation and parameter tuning, an optimized contact resistance compensation model is generated for real-time resistance data adjustment to improve conductivity consistency and set the optimal pressure range based on the compensation model. Contact pressure, area, roughness, and resistance data are collected in real time, input into the compensation model to correct the resistance value, and smoothed using a weighted moving average algorithm. The smoothed data is monitored and, if the deviation exceeds the tolerance range, the contact resistance compensation model is adaptively adjusted. Specifically, the following steps are performed: Collect contact pressure, contact area, roughness and contact resistance data in real time. After acquiring the real-time data, input it into the established contact resistance compensation model to calculate the real-time contact resistance compensation coefficient of each measurement point. The contact resistance value measured in real time is corrected by the contact resistance compensation coefficient, and the corrected contact resistance value is smoothed by using the weighted moving average algorithm; Setting tolerance range for smoothed resistance data and dynamically adjusting contact resistance compensation model; Multi-channel data acquisition and distributed sampling synchronization algorithms are used to synchronize measurement points, correct time deviations, and perform error correction in combination with compensation coefficients. The uniformity index is calculated based on the final resistance data matrix, a thermal map is generated, conductivity is classified and evaluated, and low-uniformity areas are repaired.

2. The method for testing the conductive uniformity of a silver paste coating according to claim 1, wherein: The collected data is standardized and nonlinear relationships are captured using support vector regression. The specific process is as follows: The collected data is contact status data, which includes contact pressure, contact area, surface roughness and preliminary contact resistance measurement values; The contact status data were converted into a standard distribution with zero mean and unit variance through Z-score standardization; After completing data normalization, multiple linear regression was used to preliminarily model the relationship between contact state data and contact resistance to determine the effects of contact pressure, contact area, and roughness on contact resistance. Support vector regression is used to model the data. The data is mapped from the original space to a high-dimensional space through kernel function mapping, thereby determining the nonlinear relationship in the data.

3. The method for testing the conductive uniformity of a silver paste coating according to claim 2, wherein: After cross-validation and parameter tuning, an optimized contact resistance compensation model is generated for real-time resistance data adjustment to improve conductivity consistency. The optimal pressure range is then set based on the compensation model. The specific process is as follows: After establishing the preliminary support vector regression model, the K-fold method was used for cross-validation, and the penalty coefficient and kernel function parameters of the support vector regression were tuned using the grid search method; After obtaining the optimized support vector regression model, the corresponding contact resistance compensation coefficient is calculated based on the contact state data of each measurement point. The contact resistance compensation coefficient formula is defined as: in, is the contact resistance compensation value predicted by the model, R i，j is the measured value of the initial contact resistance, K i，j The compensation coefficient for each measurement point characterizes the compensation relationship between contact state and resistance; Integrate the contact resistance compensation coefficients of all measurement points to generate a compensation coefficient matrix, which is then corrected using the measured values ​​from the conductivity uniformity test. Determine the optimal contact pressure range for each measurement point based on the contact resistance compensation model; Apply initial contact pressure via a piezoelectric regulator or micro-electric device and collect real-time feedback of contact resistance values; The feedback contact resistance value is used as the initial input of PID control; The probe's contact pressure is adjusted through PID, and the resistance feedback value is collected in real time. After each adjustment, the PID controller recalculates the contact pressure deviation and adjusts the pressure to the optimal pressure range until the target pressure is reached. When the probe contact pressure remains within the optimal pressure range and the resistance is stable, the pressure adjustment is completed and the optimal measurement state is reached, and the final contact pressure and resistance values ​​are recorded as the acquisition data of the measurement point.

4. The method for testing the conductive uniformity of a silver paste coating according to claim 1, wherein: Multi-channel data acquisition and a distributed sampling synchronization algorithm are used to synchronize measurement points, correct time deviations, and perform error correction using compensation coefficients. Based on the final resistance data matrix, the uniformity index is calculated, a thermal map is generated, conductivity is evaluated by classification, and low-uniformity areas are repaired. The specific steps are as follows: In the process of synchronous data collection, a distributed sampling synchronization algorithm is used to balance and correct the sampling deviation of each measurement point; After acquiring the synchronous data, the deviation between the resistance data of each measurement point is compared and the compensation coefficient matrix is ​​applied to make corrections; After error compensation, the resistance difference between different measurement points is calculated to evaluate the uniformity of the data, and the measurement points are corrected twice using the consistency correction algorithm; After the data of all measurement points have passed consistency analysis and correction, the final corrected resistance data of each measurement point is recorded and a synchronously corrected resistance data matrix is ​​generated; The resistance data matrix was analyzed, and the median, interquartile range and distribution density of the distribution characteristics were combined to generate the conductivity uniformity index; Visualize the resistance data matrix to generate a resistance uniformity distribution heat map; Based on the conductivity uniformity index and resistance distribution thermal map, a comprehensive judgment and repair is made on the conductivity uniformity of the silver paste coating.

5. A silver paste coating conductive uniformity testing system, used to implement a silver paste coating conductive uniformity testing method according to any one of claims 1 to 4, characterized in that: include: The measurement module is used to monitor the contact pressure and area of ​​the probe at each measurement point using a pressure sensor, obtain surface roughness characteristics through a camera, analyze frequency domain information using Fourier transform, and correct the contact resistance by combining pressure and current and voltage data to integrate into a contact state matrix; The compensation determination module is used to standardize the collected data, capture nonlinear relationships based on support vector regression, and generate an optimized contact resistance compensation model after cross-validation and parameter tuning. This model is used for real-time resistance data adjustment to improve conductivity consistency and set the optimal pressure range based on the compensation model. The model adjustment module is used to collect contact pressure, area, roughness and resistance data in real time, input the compensation model to correct the resistance value, and smooth it using a weighted moving average algorithm. The smoothed data is monitored and, if the deviation exceeds the tolerance range, the contact resistance compensation model is adaptively adjusted. The test and repair module is used to synchronize measurement point acquisition through multi-channel data acquisition and distributed sampling synchronization algorithm, correct time deviation, and perform error correction in combination with compensation coefficients. It calculates the uniformity index based on the final resistance data matrix, generates a thermal map, classifies and evaluates conductivity, and repairs low-uniformity areas.

Images