Chip test result real-time compensation method based on PSO-KF algorithm

Abstract

The invention discloses a chip test result real-time compensation method based on a PSO-KF algorithm, belongs to the technical field of integrated circuit testing, and solves the problem of how to improve the accuracy and stability of integrated circuit testing. Based on the real-time change of the measurement data and the environmental noise, a particle swarm optimization algorithm is adopted to optimize noise parameters in the Kalman filter; the particle swarm optimization algorithm is used for updating noise parameters after each measurement data is obtained, and the optimal parameters are applied to the Kalman filter again, so that real-time compensation of the measurement data is realized; and the yield is evaluated based on the compensated data, and the data is fed back to the particle swarm optimization algorithm for the next iterative optimization, so that the Kalman filter can be ensured to be adaptive to the noise change and keep the optimal compensation effect, and the technical scheme of the invention improves the precision, yield and product quality of the chip test.

Description

Technical Field

[0001] The invention belongs to the technical field of integrated circuit testing, and relates to a real-time compensation method for chip testing results based on a PSO-KF algorithm. Background Art

[0002] Integrated circuit (IC) test equipment plays a vital role in the development, manufacturing and quality control of ICs. With the improvement of IC integration and complexity, the performance and quality requirements of chips are also getting higher and higher, so the importance of the testing link is becoming more and more prominent. However, during the IC testing process, the impact of external noise interference on the test results cannot be ignored. This noise interference comes from a wide range of sources, which poses a great challenge to the accuracy and consistency of test data, especially in high-precision, high-frequency IC testing. This problem is particularly obvious.

[0003] In the production process of IC, the testing process is one of the key links to ensure product quality. The testing equipment needs to perform a large number of electrical performance and functional tests on the chip under different environmental conditions to verify whether it meets the design indicators and industry standards. However, since IC testing needs to process weak electrical signals, the inevitable noise interference in the test environment can easily affect the accuracy of the test data, resulting in deviations in the test results or even misjudgments.

[0004] Noise interference can be divided into two categories: internal noise and external noise. Internal noise mainly comes from the hardware structure of the test equipment itself and the internal structure of the IC under test, including thermal noise, shot noise and power supply noise. These noises are usually caused by the material properties and working conditions of the physical device and are difficult to completely eliminate. External noise mainly comes from external interference in the test environment, such as electromagnetic radiation, changes in ambient temperature and humidity, and power supply fluctuations. Other electronic devices around the test equipment, such as computers, power modules, radio equipment, etc., will generate electromagnetic interference to varying degrees. These interferences enter the test system through coupling, resulting in deviations in the test signal.

[0005] With the advancement of semiconductor process technology, the feature size of ICs continues to shrink and the operating frequency continues to increase, which greatly enhances the chip's sensitivity to noise. Especially at the nanoscale, the device's operating voltage is reduced to 1V or even lower, the signal amplitude becomes extremely weak, and the tolerance to noise is significantly reduced. Therefore, when the test equipment collects and measures chip signals, it is easily affected by various noise interferences, resulting in a decrease in test accuracy, an increase in test failure rate and rework rate, and thus affecting product yield.

[0006] The problem of test data deviation caused by noise interference during IC testing is a major technical problem in the field of IC testing. Especially with the current trend of IC developing towards higher integration and higher frequency, the impact of noise interference on test accuracy is becoming increasingly prominent. Therefore, how to compensate for IC test data affected by noise interference and ensure the accuracy and consistency of test data is still an urgent problem to be solved in the research and development of IC test equipment. Summary of the invention

[0007] The technical solution of the present invention is used to solve the problem of how to improve the accuracy and stability of integrated circuit testing.

[0008] The present invention solves the above technical problems through the following technical solutions:

[0009] A real-time compensation method for chip test results based on PSO-KF algorithm includes the following steps:

[0010] Step 1: Collect the original measurement data during the chip testing process and use the mode to supplement the missing values ​​in the data;

[0011] Step 2: Input the collected raw measurement data into the Kalman filter for preliminary filtering;

[0012] Step 3: Based on the real-time changes of the measurement data and the environmental noise, the noise parameters in the Kalman filter are optimized using the particle swarm optimization algorithm;

[0013] Step 4: The particle swarm optimization algorithm updates the noise parameters after obtaining each measurement data, and reapplies the optimal parameters to the Kalman filter, thereby achieving real-time compensation of the measurement data;

[0014] Step 5: Evaluate the yield based on the compensated data, and the data will be fed back to the particle swarm optimization algorithm for the next iterative optimization to ensure that the Kalman filter can adapt to noise changes and maintain the best compensation effect.

[0015] Furthermore, the method of inputting the collected raw measurement data into the Kalman filter for preliminary filtering processing described in step 2 is specifically as follows:

[0016] 1) Initialization of state variables and measurement model: Define the state variables of the Kalman filter according to the specific requirements of chip testing; initialize the initial values ​​and covariance matrix of the state variables to represent the uncertainty of the system state at the initial moment; build a measurement model, establish the mathematical relationship between the state variables and the actual measurement data, and define the state transfer matrix A, control matrix B, and observation matrix H;

[0017] 2) Prediction step: predict the state of the system according to the state transition equation, calculate the covariance matrix, and estimate the state value and uncertainty of the current data;

[0018] 3) Update step: Combined with the current measurement value, the state estimate is updated through the Kalman gain to reduce the difference between the predicted value and the measured value;

[0019] 4) Filtered data output: Output the updated state estimate as the estimation result of the real state at the current moment, and use it for subsequent data analysis and compensation; the filtered data will be fed back to the particle swarm optimization algorithm as input to optimize the noise parameters.

[0020] Furthermore, the method of predicting the state of the system according to the state transfer equation, calculating the covariance matrix, and estimating the state value and uncertainty of the current data is as follows:

[0021] Use the state transition equation to predict the estimated value of the system's current state:

[0022]

[0023] in, represents the state prediction value at the current moment, A is the state transfer matrix, is the last state estimate, B is the control matrix, μ k-1 is the control input;

[0024] Compute the covariance matrix of the predicted states:

[0025] P k|k-1 =AP k-1 A T +Q

[0026] Among them, P k|k-1 represents the prediction covariance matrix, P k-1 is the covariance matrix of the previous moment, and Q is the process noise covariance matrix.

[0027] Furthermore, the method of combining the current measured value and updating the state estimation value through the Kalman gain to reduce the difference between the predicted value and the measured value is as follows:

[0028] Calculate the Kalman gain K k as follows:

[0029] K k =P k|k-1 H T (HP k|k-1 H T +R) -1

[0030] Among them, K k represents the Kalman gain, H is the observation matrix, and R is the observation noise covariance matrix;

[0031] The state estimate is updated in combination with the measured data as follows:

[0032]

[0033] in, represents the updated state estimate, z k is the actual measured value at the current moment, is the measurement residual, i.e., the difference between the predicted and measured values;

[0034] The updated state covariance matrix is ​​as follows:

[0035] P k =(IK k H)P k|k-1

[0036] Among them, P k is the updated state covariance matrix, which represents the uncertainty of the updated state variables.

[0037] Furthermore, the method described in step 3 for optimizing the noise parameters in the Kalman filter using the particle swarm optimization algorithm based on the real-time changes of the measurement data and the environmental noise is as follows:

[0038] 1) Initialize the particle swarm: determine the size of the particle swarm, that is, the number of particles N. Each particle represents a noise parameter combination of the noise covariance matrix Q and the observation noise covariance matrix R. Randomly initialize the position and velocity of each particle and set the initial parameter range.

[0039] 2) Calculate the fitness function: For each particle, apply its current noise parameter combination to the Kalman filter to filter the measurement data; define the fitness function based on the filtering error to evaluate the filtering effect;

[0040] 3) Update individual extreme values ​​and global extreme values: For each particle, calculate the current fitness value. If the current fitness value is better than the historical optimal value of the particle, update the current fitness value and the corresponding position to the individual optimal value of the particle; find the particle with the best fitness value among the individual optimal values ​​of all particles, and update its position and fitness value to the global optimal value;

[0041] 4) Update particle speed and position;

[0042] 5) Boundary condition processing: Use the rebound boundary method or position reset method to process particles that exceed the boundary, so that the particle position is always within a reasonable range;

[0043] 6) Repeat steps 2) to 5) until a preset stop condition is met;

[0044] 7) Application of optimal noise parameters: After particle swarm optimization, the optimal noise covariance matrix Q and observation noise covariance matrix R are found and applied to the Kalman filter for the next round of data filtering and real-time compensation.

[0045] Furthermore, the formula for updating particle velocity is as follows:

[0046] v i t+1 =ωv i t +c1r1(pbest i -x i t )+c2r2(gbest i -x i t )

[0047] Among them, v i t+1 represents the velocity of particle i in the t+1th iteration, ω is the inertia weight, c1 and c2 are the individual and global learning factors, r1 and r2 are randomly generated numbers in the interval [0,1], and pbest i is the individual optimal position of the particle, gbest i is the global optimal position, x i t is the position of the particle at iteration t.

[0048] Furthermore, the formula for updating the particle position is as follows:

[0049] x i t+i =x i t +v i t+1

[0050] Among them, x i t+1 represents the position of the particle at the t+1th iteration.

[0051] Furthermore, the stopping condition is that the maximum number of iterations or the global optimal fitness value is less than a threshold.

[0052] The present invention also provides an electronic device, comprising a memory and a processor, characterized in that the memory is used to store a program that supports the processor to execute the above-mentioned real-time compensation method for chip test results based on the PSO-KF algorithm, and the processor is configured to execute the program stored in the memory.

[0053] The present invention also provides a storage medium storing a computer program, which executes the steps of the above-mentioned chip test result real-time compensation method based on the PSO-KF algorithm when the computer program is executed by a processor.

[0054] The advantages of the present invention are:

[0055] (1) In the process of data collection, traditional compensation methods are mostly performed offline, which makes it difficult to achieve real-time measurement data correction, thus limiting the timeliness and accuracy of data compensation. The present invention innovatively takes real-time as the core, and adopts a combination of Kalman filtering and PSO algorithm to instantly analyze and compensate each new measurement data. Through the real-time adjustment of the filter parameters by PSO, it is ensured that the measurement data can be accurately compensated immediately when the noise environment changes. This real-time compensation mechanism ensures that the equipment can continuously adapt to changes in external noise during operation, thereby significantly improving the accuracy and consistency of the measurement data.

[0056] (2) In traditional Kalman filtering methods, noise parameters are often fixed, and the dynamic changes of noise during the test process are not fully considered, resulting in less than ideal filtering effects in practical applications. To solve this problem, the method of the present invention introduces a particle swarm optimization (PSO) algorithm, which can dynamically adjust the noise parameters in the Kalman filter according to real-time measurement data to achieve adaptive optimization of the noise level. Through this optimization method, the compensation process can better adapt to changes in the noise environment, improve the accuracy of compensation and the robustness of the system, and ensure that the test process always maintains a high-precision filtering effect.

[0057] (3) During the chip testing process, traditional methods are often not timely and effective in dealing with equipment noise and errors, which affects the accuracy of the test results and leads to a reduction in yield. The present invention significantly improves the accuracy of chip test results and reduces the deviation of measurement data by effectively and in real time compensating for equipment noise and errors. In addition, through precise noise compensation and error correction, the proportion of defective products is effectively reduced and the overall yield of the chip is improved. This improvement not only improves the reliability of the test results, but also provides important technical support for quality control in the chip production process, promoting the overall improvement of production quality. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] Figure 1 It is a flow chart of a real-time compensation method for chip test results based on the PSO-KF algorithm;

[0059] Figure 2 This is the effect diagram of Kalman filter combined with PSO algorithm compensation current test data;

[0060] Figure 3This is the effect diagram of Kalman filtering combined with PSO algorithm compensation voltage test data. DETAILED DESCRIPTION

[0061] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in combination with the embodiments of the present invention. Obviously, the described embodiments are 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 creative work are within the scope of protection of the present invention.

[0062] The technical solution of the present invention is further described below in conjunction with the accompanying drawings and specific embodiments:

[0063] Embodiment 1

[0064] like Figure 1 As shown, a real-time compensation method for chip test results based on the PSO-KF algorithm according to an embodiment of the present invention comprises the following steps:

[0065] 1. Data collection and preprocessing

[0066] During the chip testing process, original measurement data is collected. The embodiment of the present invention collects current and voltage test data. In order to ensure the reliability of the data, these original test data need to be preprocessed, that is, the mode is used to supplement the missing values ​​in the data.

[0067] 2. Kalman filter processing

[0068] The collected raw measurement data is usually affected by different noise factors and needs to be filtered. The collected raw data is filtered using a Kalman filter. The specific steps are as follows:

[0069] 1) Initialization of state variables and measurement model

[0070] a. Define the state variables of the Kalman filter according to the specific requirements of the chip test. Usually, the state variables include the actual measurement quantities of the system (such as voltage, current, etc.);

[0071] b. Initialize the initial values ​​of the state variables (usually zero or estimated values) and the covariance matrix to represent the uncertainty of the system state at the initial moment;

[0072] c. Construct a measurement model, establish the mathematical relationship between state variables and actual measurement data, and define the state transfer matrix A, control matrix B, observation matrix H, etc.

[0073] 2) Prediction step

[0074] The state of the system is predicted based on the state transfer equation, and the covariance matrix is ​​calculated to estimate the state value and uncertainty of the current data.

[0075] a. Use the state transfer equation to predict the estimated value of the system's current state as follows:

[0076]

[0077] in, represents the state prediction value at the current moment, A is the state transfer matrix, is the last state estimate, B is the control matrix, μ k-1 is the control input.

[0078] b. Calculate the covariance matrix of the predicted state as follows:

[0079] P k|k-1 =AP k-1 A T +Q

[0080] Among them, P k|k-1 represents the prediction covariance matrix, P k-1 is the covariance matrix of the previous moment, and Q is the process noise covariance matrix, which is used to characterize the random disturbance of the system itself.

[0081] 3) Update steps

[0082] Combined with the current measurement value, the state estimate is updated through the Kalman gain to reduce the difference between the predicted value and the measured value.

[0083] a. Calculate the Kalman gain K k as follows:

[0084] K k =P k|k-1 H T (HP k|k-1 H T +R) -1

[0085] Among them, K k represents the Kalman gain, H is the observation matrix, and R is the observation noise covariance matrix. The Kalman gain is used to measure the influence of the measurement data on the state estimation.

[0086] b. Update the state estimate based on the measured data as follows:

[0087]

[0088] in, represents the updated state estimate, z k is the actual measured value at the current moment, is the measurement residual, that is, the difference between the predicted value and the measured value.

[0089] c. Update the state covariance matrix as follows:

[0090] P k =(IK k H)P k|k-1

[0091] Among them, P k is the updated state covariance matrix, which represents the uncertainty of the updated state variables.

[0092] 4) Filter data output:

[0093] a. Output the updated state estimate It is used as an estimate of the actual state at the current moment and is used for subsequent data analysis and compensation.

[0094] b. The filtered data will be fed back as input to the particle swarm optimization (PSO) algorithm to optimize the noise parameters and ensure a more accurate filtering effect in the next iteration.

[0095] After the above steps, the Kalman filter performs preliminary processing on the original measurement data, minimizing the uncertainty caused by noise in the system, and providing a basic guarantee for the real-time compensation of the subsequent particle swarm optimization algorithm.

[0096] 3. Particle Swarm Optimization (PSO) adaptively adjusts filter parameters

[0097] Based on the real-time changes of measurement data and environmental noise, the particle swarm optimization (PSO) algorithm is used to optimize the noise parameters in the Kalman filter. The PSO algorithm randomly initializes a group of particles, each particle represents a different combination of noise parameters, and searches for the optimal noise parameters in the solution space through multiple iterations to improve the filtering effect. The specific steps are as follows:

[0098] (1) Initialize particle swarm

[0099] First, determine the size of the particle group, that is, the number of particles N. Each particle represents a possible combination of noise parameters (such as process noise covariance matrix Q and observation noise covariance matrix R).

[0100] The position and velocity of each particle are randomly initialized. The position represents the initial value of the noise parameter, and the velocity represents the step size of these parameters in the solution space.

[0101] The initial parameter range is set so that particles can search for the optimal solution in a larger solution space, ensuring the diversity of parameter settings and avoiding falling into local optimality.

[0102] (2) Calculate the fitness function

[0103] For each particle, its current noise parameter combination is applied to the Kalman filter to filter the measurement data.

[0104] Define a fitness function to evaluate the filtering effect. Usually, the fitness function can be based on the filtering error (such as mean square error, MSE), which is used to measure the difference between the measured data after filtering and the true value. The smaller the fitness function value, the better the noise parameter combination corresponding to the particle.

[0105] (3) Update individual extreme values ​​and global extreme values

[0106] For each particle, calculate the current fitness value. If the current fitness value is better than the historical optimal value of the particle, update the current fitness value and corresponding position to the individual optimal value (pbest) of the particle.

[0107] Find the particle with the best fitness value among the individual optimal values ​​of all particles, and update its position and fitness value to the global optimal value (gbest).

[0108] (4) Update particle speed and position

[0109] Update the velocity of each particle according to the following formula:

[0110] v i t+1 =ωv i t +c1r1(pbest i -x i t )+c2r2(gbest i -x i t )

[0111] Among them, v i t+1 represents the speed of particle i in the t+1th iteration, ω is the inertia weight, which is used to control the influence of the current speed of the particle on the next speed, c1 and c2 are individual and global learning factors respectively, r1 and r2 are randomly generated numbers in the interval [0,1], (pbest i ) is the individual optimal position of the particle, (gbest i ) is the global optimal position, x i t is the position of the particle at the tth iteration.

[0112] Update the particle's position according to the following formula:

[0113] xi t+i =x i t +v i t+1

[0114] Among them, x i t+1 Represents the position of the particle in the t+1th iteration, that is, the updated noise parameter combination.

[0115] (5) Boundary condition processing

[0116] If the position of a particle exceeds the preset boundary of the solution space, its position needs to be limited within the boundary to avoid non-physical values ​​of the noise parameters. The rebound boundary method or position reset method can be used to process particles that exceed the boundary so that the particle position is always within a reasonable range.

[0117] (6) Iterate until the stopping condition is met

[0118] Repeat steps (2) to (5) until a preset stop condition is met. The stop condition can be the maximum number of iterations or the global optimal fitness value is less than a threshold.

[0119] Finally, the found global optimal noise parameter combination is applied to the Kalman filter for real-time data compensation to ensure the accuracy and stability of the filtering process.

[0120] (7) Application of optimal noise parameters

[0121] After particle swarm optimization, the optimal noise covariance matrix (Q) and (R) are found. These optimal parameters are applied to the Kalman filter for the next round of data filtering and real-time compensation. After each new measurement data is input, the PSO algorithm will re-evaluate the noise level and continuously iterate and optimize the noise parameters to ensure that the Kalman filter always operates in the optimal state and provides accurate data compensation.

[0122] 4. Real-time filtering and data compensation

[0123] The PSO algorithm updates the noise parameters after obtaining each measurement data, and reapplies the optimal parameters to the Kalman filter, thereby achieving real-time compensation of the measurement data. Through this adaptive adjustment filtering process, the Kalman filter can respond to noise changes in the measurement environment in real time and ensure the accuracy of the filtering effect.

[0124] Figure 2The figure shows the comparison between the original current test data and the filtered data. The blue curve represents the original data, which has large fluctuations and contains a lot of high-frequency noise, resulting in an unsmooth test result. After Kalman filtering, the red curve shows that the filtered data is smoother than the original data. By combining the PSO algorithm, the filter can adaptively adjust the noise parameters, thereby significantly suppressing the noise, retaining the main features of the original signal, and improving the measurement accuracy of the current data. This shows that this method is effective in noise suppression and provides a higher quality signal basis for subsequent data analysis.

[0125] Figure 3 The figure shows the comparison between the original voltage test data and the filtered data, which shows the effect of filtering on the voltage data. The blue curve represents the original voltage data. Although the amplitude fluctuation is not large, the influence of noise on the data is obvious. After the Kalman filter processing optimized by PSO, the filtered data represented by the red curve becomes more stable. The green and blue dotted lines represent the upper and lower limits of the system voltage, respectively. The filtered data remains within this range and significantly reduces irregular fluctuations, ensuring the stability and consistency of the data. This shows that in voltage testing, the present invention can effectively suppress noise interference and make the data more reliable and stable.

[0126] Experimental results show that the Kalman filter combined with PSO optimization can effectively suppress random noise in current and voltage tests, making the signal smoother and keeping it within the expected working range. This method significantly improves the quality and accuracy of test data while maintaining the original characteristics of the signal, which plays an important role in improving the overall yield and reliability of chip production and electrical testing processes.

[0127] 5. Yield improvement and feedback

[0128] Yield is an indicator to measure the proportion of products that meet quality requirements. The data in Table 1 compares the yield before and after compensation. After obtaining high-precision filtering data, the yield performance during chip testing is further analyzed. Table 1 shows the yield improvement effect of the real-time compensation method for chip test results of the present invention on current and voltage test data.

[0129] Table 1 Kalman filter combined with PSO compensation test data before and after yield comparison table

[0130] Yield Current test yield Voltage test yield Before compensation 98.365％ 99.598％ After compensation 99.754％ 99.983％ Improved value 1.389％ 0.385％

[0131] For the current test results, the yield before compensation was 98.365%, and after Kalman filtering and PSO compensation, the yield was significantly improved to 99.754%. This change shows that the compensation process effectively reduced the noise and errors in the measurement data, allowing more products to meet quality standards. In terms of voltage testing, the yield before compensation was 99.598%, and after compensation it increased to 99.983%. Although the yield before compensation was already high, the further improvement after compensation shows that this method still has a significant effect in improving accuracy.

[0132] These results show that the real-time compensation method for chip test results of the present invention performs well in improving the quality of test data, can effectively reduce the impact of noise, and significantly improve the reliability and yield of measurement data. For current testing, the yield improvement after compensation is large, indicating that the noise impact in the original data is significant, and the quality has been significantly improved after compensation. For voltage testing, although the initial yield is high, it is still further improved after compensation, indicating that this method also has room for improvement under high quality requirements, and can ensure the consistency and stability of product quality. Therefore, the compensation method combining Kalman filtering and PSO is suitable for various noise environments, which helps to improve the overall yield and production quality in the chip testing process.

[0133] By reducing the noise and deviation of the measurement data, the proportion of defective products in the production process has been significantly reduced. Throughout the process, the measurement results will be continuously fed back to the PSO algorithm for parameter optimization, ensuring that the filter can be adaptively adjusted in real time in a constantly changing noise environment to maintain the best compensation effect.

[0134] In summary, the real-time compensation method for chip test results of the present invention not only effectively improves the accuracy and yield of chip testing, but also provides a more scientific and reasonable idea in chip resource management, providing strong technical support for the development of the integrated circuit testing field.

[0135] The present invention first performs preliminary processing on the measurement data through Kalman filtering, but unlike traditional methods, the noise parameters are no longer fixed, but are adaptively optimized and adjusted with the help of the PSO algorithm. Specifically, during the data acquisition process, each time a new batch of measurement data is obtained, the PSO algorithm will evaluate the noise level in the current measurement environment and dynamically adjust the noise parameters of the Kalman filter so that the filtering process can adapt to the real-time changing noise environment and ensure the accuracy of data processing. At the same time, the compensation process is carried out in real time, and the Kalman filter works together with the PSO algorithm to perform instant compensation for each measurement data. This process is repeated continuously to ensure that the measuring equipment can always maintain high-precision measurement results in an unstable or noisy environment, thereby improving the accuracy, yield and product quality of chip testing.

[0136] Embodiment 2

[0137] An electronic device includes a memory and a processor, wherein the memory is used to store a program that supports the processor to execute the real-time compensation method for chip test results based on the PSO-KF algorithm in Example 1, and the processor is configured to execute the program stored in the memory.

[0138] Embodiment 3

[0139] A storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the real-time compensation method for chip test results based on the PSO-KF algorithm in embodiment 1 are executed.

[0140] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A real-time compensation method for chip test results based on PSO-KF algorithm, characterized in that: The following steps are involved: Step 1: Collect the original measurement data during the chip testing process and use the mode to supplement the missing values ​​in the data; Step 2: Input the collected raw measurement data into the Kalman filter for preliminary filtering; Step 3: Based on the real-time changes of the measurement data and the environmental noise, the noise parameters in the Kalman filter are optimized using the particle swarm optimization algorithm; Step 4: The particle swarm optimization algorithm updates the noise parameters after obtaining each measurement data, and reapplies the optimal parameters to the Kalman filter, thereby achieving real-time compensation of the measurement data; Step 5: Evaluate the yield based on the compensated data, and the data will be fed back to the particle swarm optimization algorithm for the next iterative optimization to ensure that the Kalman filter can adapt to noise changes and maintain the best compensation effect.

2. The real-time compensation method for chip test results based on the PSO-KF algorithm according to claim 1 is characterized in that: The method described in step 2 of inputting the collected raw measurement data into the Kalman filter for preliminary filtering processing is specifically as follows: 1) Initialization of state variables and measurement model: Define the state variables of the Kalman filter according to the specific requirements of chip testing; initialize the initial values ​​and covariance matrix of the state variables to represent the uncertainty of the system state at the initial moment; build a measurement model, establish the mathematical relationship between the state variables and the actual measurement data, and define the state transfer matrix A, control matrix B, and observation matrix H; 2) Prediction step: predict the state of the system according to the state transition equation, calculate the covariance matrix, and estimate the state value and uncertainty of the current data; 3) Update step: Combined with the current measurement value, the state estimate is updated through the Kalman gain to reduce the difference between the predicted value and the measured value; 4) Filtered data output: Output the updated state estimate as the estimation result of the real state at the current moment, and use it for subsequent data analysis and compensation; the filtered data will be fed back to the particle swarm optimization algorithm as input to optimize the noise parameters.

3. The chip test result real-time compensation method based on the PSO-KF algorithm according to claim 2 is characterized in that: The method of predicting the state of the system according to the state transfer equation, calculating the covariance matrix, and estimating the state value and uncertainty of the current data is as follows: Use the state transition equation to predict the estimated value of the system's current state: in, represents the state prediction value at the current moment, A is the state transfer matrix, is the last state estimate, B is the control matrix, μ k-1 is the control input; Compute the covariance matrix of the predicted states: P k | k-1 =AP k-1 From T +Q Among them, P k|k-1 represents the prediction covariance matrix, P k-1 is the covariance matrix of the previous moment, and Q is the process noise covariance matrix.

4. The chip test result real-time compensation method based on the PSO-KF algorithm according to claim 3 is characterized in that: The method of combining the current measured value and updating the state estimate value through the Kalman gain to reduce the difference between the predicted value and the measured value is as follows: Calculate the Kalman gain K k as follows: K k =P k | k-1 H T (HP k | k-1 H T +R) -1 Among them, K k represents the Kalman gain, H is the observation matrix, and R is the observation noise covariance matrix; The state estimate is updated in combination with the measured data as follows: in, represents the updated state estimate, z k is the actual measured value at the current moment, is the measurement residual, i.e., the difference between the predicted and measured values; The updated state covariance matrix is ​​as follows: P k =(I-K k H)P k|k-1 Among them, P k is the updated state covariance matrix, which represents the uncertainty of the updated state variables.

5. The chip test result real-time compensation method based on the PSO-KF algorithm according to claim 2 is characterized in that: The method described in step 3 for optimizing the noise parameters in the Kalman filter using the particle swarm optimization algorithm based on the real-time changes of the measurement data and the environmental noise is as follows: 1) Initialize the particle swarm: determine the size of the particle swarm, that is, the number of particles N. Each particle represents a noise parameter combination of the noise covariance matrix Q and the observation noise covariance matrix R. Randomly initialize the position and velocity of each particle and set the initial parameter range. 2) Calculate the fitness function: For each particle, apply its current noise parameter combination to the Kalman filter to filter the measurement data; Define a fitness function based on the filtering error to evaluate the filtering effect; 3) Update individual extreme values ​​and global extreme values: For each particle, calculate the current fitness value. If the current fitness value is better than the historical optimal value of the particle, update the current fitness value and the corresponding position to the individual optimal value of the particle; find the particle with the best fitness value among the individual optimal values ​​of all particles, and update its position and fitness value to the global optimal value; 4) Update particle speed and position; 5) Boundary condition processing: Use the rebound boundary method or position reset method to process particles that exceed the boundary, so that the particle position is always within a reasonable range; 6) Repeat steps 2) to 5) until a preset stop condition is met; 7) Application of optimal noise parameters: After particle swarm optimization, the optimal noise covariance matrix Q and observation noise covariance matrix R are found and applied to the Kalman filter for the next round of data filtering and real-time compensation.

6. The real-time compensation method for chip test results based on the PSO-KF algorithm according to claim 5 is characterized in that: The formula for updating particle velocity is as follows: v i t+1 =ωv i t +c1r1(pbest i -x i t )+c2r2(gbest i -x i t ) Among them, v i t+1 represents the velocity of particle i in the t+1th iteration, ω is the inertia weight, c1 and c2 are the individual and global learning factors, r1 and r2 are randomly generated numbers in the interval [0,1], and pbest i is the individual optimal position of the particle, gbest i is the global optimal position, x i t is the position of the particle at the tth iteration.

7. The chip test result real-time compensation method based on the PSO-KF algorithm according to claim 6 is characterized in that: The formula for updating the particle position is as follows: x i t+i =x i t +v i t+1 Among them, x i t+1 represents the position of the particle at the t+1th iteration.

8. The chip test result real-time compensation method based on the PSO-KF algorithm according to claim 5 is characterized in that: The stopping condition is that the maximum number of iterations or the global optimal fitness value is less than a threshold.

9. An electronic device, comprising a memory and a processor, characterized in that: The memory is used to store a program that supports the processor to execute the real-time compensation method for chip test results based on the PSO-KF algorithm as described in any one of claims 1 to 8, and the processor is configured to execute the program stored in the memory.

10. A storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the real-time compensation method for chip test results based on the PSO-KF algorithm according to any one of claims 1 to 8 are executed.

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