A method and system for detecting and analyzing performance of an integrated circuit motherboard

Abstract

This invention relates to the field of integrated circuit testing technology and discloses a method and system for performance testing and analysis of integrated circuit motherboards. The method includes acquiring and cleaning historical voltage, current, and temperature data to generate a clean historical dataset; evaluating the parameter fluctuations of this dataset and calculating stability estimates for voltage, current, and temperature; inputting these estimates into a pre-trained detection cycle recommendation network, which outputs a customized detection frequency by comparing with a historical pattern library; deploying real-time monitoring based on this frequency to collect instantaneous voltage, current, and temperature readings and signal quality indicators, assembling them into a real-time dataset; and using a pattern recognition engine to perform multi-dimensional analysis of the real-time dataset, classifying the motherboard's performance status and outputting evaluation labels. This invention achieves adaptive optimization of the detection frequency and intelligent diagnosis of performance status, improving detection efficiency and the accuracy of early warnings.

Description

Technical Field

[0001] This invention relates to the field of integrated circuit testing technology, specifically to a method and system for performance testing and analysis of integrated circuit motherboards. Background Technology

[0002] Currently, performance testing and health monitoring of integrated circuit motherboards generally rely on fixed-period testing strategies. The monitoring system collects basic parameters such as voltage, current, and temperature at preset time intervals and compares the collected real-time data with preset fixed safety thresholds. Once a parameter exceeds the upper or lower limit of the threshold, the system triggers an alarm. This timed, threshold-based monitoring method is a widely adopted basic approach in industry.

[0003] Fixed-cycle monitoring has significant drawbacks. For motherboards with stable performance and moderate loads, excessively frequent monitoring wastes computing and storage resources, increasing unnecessary monitoring costs. For motherboards operating under complex and variable conditions or already showing slight performance degradation, fixed long monitoring cycles may fail to detect critical transient anomalies or early signs of performance decline in a timely manner, resulting in delayed warnings. Fixed threshold alarms only reflect instantaneous exceedances of parameters and cannot assess the fluctuation trend of parameters within the normal range or the long-term risks they indicate, lacking the ability to predict trends in motherboard health.

[0004] Existing alarm mechanisms are typically limited to threshold judgments for single parameters, failing to integrate and analyze multi-dimensional parameters such as voltage, current, and temperature. Conventional technologies rarely incorporate key indicators like signal quality, which directly reflect circuit timing and logic correctness, into real-time monitoring and analysis. This isolated, single-point judgment method struggles to identify complex fault modes caused by the combined effects of subtle changes in multiple parameters. The output is merely a binary judgment of "normal" or "out of limit," unable to provide a more refined classification of the motherboard's performance status. Summary of the Invention

[0005] The purpose of this invention is to provide a method and system for performance testing and analysis of integrated circuit motherboards, so as to solve the problems mentioned in the background art.

[0006] To achieve the above objectives, the present invention provides a performance testing and analysis method for integrated circuit motherboards, the method comprising:

[0007] Export voltage, current, and temperature sampling data within a historical time range from the monitoring system of the integrated circuit motherboard, perform integrity verification and outlier removal on the voltage, current, and temperature sampling data, and generate a clean historical data set.

[0008] A parameter fluctuation assessment is performed on the clean historical data set. By calculating the oscillation amplitude of voltage data, the drift range of current data, and the fluctuation of temperature data, voltage stability estimates, current stability estimates, and temperature stability estimates are generated.

[0009] The voltage stability estimate, current stability estimate, and temperature stability estimate are input into a pre-trained detection cycle recommendation network, which outputs a customized detection frequency by comparing with a historical pattern library.

[0010] Based on the customized detection frequency, a real-time monitoring process is deployed to capture instantaneous voltage readings, instantaneous current readings, instantaneous temperature readings, and signal quality indicators of the integrated circuit motherboard, and to assemble a real-time data set;

[0011] A pattern recognition engine is used to perform multi-dimensional analysis on the real-time data set, classify performance status categories, and output performance evaluation labels.

[0012] Preferably, the integrity verification and outlier removal of the voltage sampling data, current sampling data, and temperature sampling data includes:

[0013] Check the continuity of timestamps for voltage, current, and temperature sampling data, and identify the data points corresponding to missing timestamps.

[0014] For data points with missing timestamps, linear interpolation is used to fill the missing timestamps, forming a continuous data sequence.

[0015] Calculate the deviation between each data point and its neighboring data points in a continuous data sequence. If the deviation exceeds a preset tolerance threshold, mark it as an outlier and remove it.

[0016] The data sequence after removing outliers is normalized to ensure that all data are scaled uniformly, resulting in a clean historical data set.

[0017] Preferably, the parameter fluctuation assessment performed on the clean historical data set includes:

[0018] Extract voltage data sequences from the clean historical data set, and calculate the average absolute value of the difference between adjacent sampling points in the voltage data sequence as the oscillation amplitude;

[0019] Extract the current data sequence from the clean historical data set, and calculate the difference between the maximum and minimum values ​​of the current data sequence divided by the average value as the drift range;

[0020] Extract the temperature data sequence from the clean historical data set, and calculate the ratio of the standard deviation to the mean of the temperature data sequence as the degree of fluctuation;

[0021] By mapping the oscillation amplitude, drift range, and fluctuation degree to the range of zero to one, we obtain the voltage stability estimate, current stability estimate, and temperature stability estimate.

[0022] Preferably, the pre-trained detection cycle recommendation network is constructed in the following manner:

[0023] Collect multiple sets of historical voltage stability estimates, historical current stability estimates, and historical temperature stability estimates, along with corresponding historical detection cycle data, to form a training sample set;

[0024] A three-layer neural network structure is constructed. The input layer contains three nodes corresponding to voltage stability estimation, current stability estimation, and temperature stability estimation, respectively. The output layer has one node corresponding to the detection period.

[0025] Supervised learning of the neural network is performed using a training sample set, and the network weights are adjusted through the backpropagation algorithm until the prediction error is lower than a set threshold.

[0026] Save the trained network parameters and structure as a recommendation network for the detection cycle.

[0027] Preferably, the step of inputting the voltage stability estimate, current stability estimate, and temperature stability estimate into the pre-trained detection cycle recommendation network includes:

[0028] The voltage stability estimate, current stability estimate, and temperature stability estimate are combined into an input vector.

[0029] The input vector is fed into the input layer of the detection period recommendation network, and a weighted sum is calculated through the hidden layer nodes and an activation function is applied.

[0030] The output layer nodes calculate the final value and map the final value to the actual detection cycle range to obtain the customized detection frequency.

[0031] Preferably, the deployment of real-time monitoring includes:

[0032] Set a timer according to the customized detection frequency to trigger data acquisition events;

[0033] When the timer is triggered, the voltage sensor readings, current sensor readings, temperature sensor readings, and jitter index output by the signal analyzer are read synchronously from the integrated circuit motherboard.

[0034] Each collected reading is time-stamped and stored as a real-time data record;

[0035] Accumulate real-time data records from multiple time points and assemble a real-time data set.

[0036] Preferably, the step of using a pattern recognition engine to perform multi-dimensional analysis of the real-time data set includes:

[0037] Extract voltage reading sequences, current reading sequences, temperature reading sequences, and signal quality index sequences from the real-time dataset;

[0038] Calculate the statistical characteristics of each sequence, including mean, variance, skewness, and kurtosis, to form a feature vector;

[0039] The feature vector is input into a pre-trained classification model, which performs state division based on decision tree rules;

[0040] The classification results are output as performance evaluation labels.

[0041] Preferably, the pre-trained classification model is trained through the following process:

[0042] Collect historical real-time data sets and their corresponding known performance status labels as training datasets;

[0043] A subset is randomly selected from the training dataset to construct multiple decision trees, each using a different subset of features;

[0044] The final classification rule is determined by combining the voting results of multiple decision trees;

[0045] Save the decision tree parameters and voting mechanism as a classification model.

[0046] Preferably, the calculation of the statistical characteristics of each sequence includes:

[0047] For a voltage reading sequence, the arithmetic mean of all voltage values ​​is calculated as the mean, the average square of the voltage values ​​deviating from the mean is calculated as the variance, the asymmetry of the voltage value distribution is calculated as the skewness, and the sharpness of the voltage value distribution is calculated as the kurtosis.

[0048] Perform the same calculations on the current reading sequence and the temperature reading sequence to obtain their respective mean, variance, skewness, and kurtosis;

[0049] For a sequence of signal quality indicators, calculate the average value and coefficient of variation of the jitter values.

[0050] Preferably, the present invention also includes a performance testing and analysis system for an integrated circuit motherboard, the system including a memory, a processor, and a computer program stored in the memory and running on the processor, wherein the processor, when executing the computer program, implements the steps of the performance testing and analysis method for an integrated circuit motherboard as described above.

[0051] Compared with the prior art, the beneficial effects of the present invention are:

[0052] Based on a parameter fluctuation assessment and detection cycle recommendation network, a fundamental shift in detection frequency from a fixed pattern to dynamic adaptation has been achieved. First, historical operating data is quantitatively analyzed to calculate voltage oscillation amplitude, current drift range, and temperature fluctuation, generating accurate stability estimates. These estimates serve as input to a trained network, which, by comparing with a built-in historical pattern library, identifies which historical pattern corresponds to the current motherboard's operational stability characteristics. This mechanism makes the detection frequency no longer static but personalized based on the actual stability exhibited by the motherboard. For motherboards that have operated stably for a long time, the system automatically recommends a lower detection frequency, effectively saving computing, storage, and communication resources. For motherboards with specific parameter fluctuation patterns or showing potential instability trends, the system recommends a higher detection frequency, enabling more intensive data capture and early warning of performance degradation or potential faults. This data-driven intelligent scheduling mechanism improves the resource utilization efficiency of the entire monitoring system while ensuring comprehensive monitoring coverage.

[0053] By introducing signal quality indicators and applying a pattern recognition engine for multi-dimensional analysis, performance evaluation has been significantly improved, moving from single-parameter threshold judgment to comprehensive status classification. The real-time monitoring process not only captures instantaneous readings of conventional voltage, current, and temperature, but also integrates the synchronous acquisition of signal quality indicators, constructing a more comprehensive real-time dataset reflecting the actual operating status of the motherboard. The pattern recognition engine performs comprehensive analysis on this dataset, uncovering deep correlations and complex patterns between different parameters. This analytical capability allows it to identify abnormal states indicated by fluctuations in a single parameter within the normal range, but with multiple parameters changing synergistically. The system outputs a classification label for the overall health status of the motherboard, rather than a simple binary normal / abnormal judgment. This fine-grained status classification provides richer and more accurate decision-making basis for predictive maintenance, allowing maintenance actions to be scheduled based on the urgency of the status. Attached Figure Description

[0054] Figure 1 This is a schematic diagram illustrating the working principle of the integrated circuit motherboard performance testing and analysis method described in this invention.

[0055] Figure 2 A flowchart for data integrity verification and outlier removal;

[0056] Figure 3 A flowchart for parameter fluctuation assessment;

[0057] Figure 4 Comparison chart of multi-parameter stability estimates for integrated circuit motherboards;

[0058] Figure 5 This is a statistical analysis chart of signal quality jitter indicators for integrated circuit motherboards. Detailed Implementation

[0059] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0060] Please see Figure 1 This invention provides a performance testing and analysis method for integrated circuit motherboards. The method includes: exporting voltage sampling data, current sampling data, and temperature sampling data collected within a historical time range from the monitoring system of the integrated circuit motherboard; performing integrity verification and outlier removal on these sampling data to generate a clean historical data set; performing parameter fluctuation assessment on the clean historical data set, specifically by calculating the oscillation amplitude of voltage data, the drift range of current data, and the fluctuation degree of temperature data to generate quantified voltage stability estimates, current stability estimates, and temperature stability estimates; feeding these stability estimates as input into a pre-trained detection cycle recommendation network, which outputs a customized detection frequency for the current motherboard state by comparing with an internally stored historical pattern library; deploying a real-time monitoring process based on this customized detection frequency, which captures instantaneous voltage readings, instantaneous current readings, instantaneous temperature readings, and signal quality indicators of the integrated circuit motherboard at a set frequency, and assembling these readings into a real-time data set; and using a pattern recognition engine to perform multi-dimensional analysis on the real-time data set, classifying the motherboard's performance state according to the analysis results, and outputting corresponding performance evaluation labels, thereby completing a comprehensive testing and analysis of the motherboard performance.

[0061] Example 1: See Figure 2In practical implementation, the data preprocessing stage of the integrated circuit motherboard performance testing and analysis method involves rigorous integrity verification and outlier removal of voltage, current, and temperature sampling data exported from the monitoring system. The first step in integrity verification is to check whether the timestamps of all sampled data remain continuous. When the monitoring system collects data within a historical time range, some timestamps may be missing due to temporary equipment failures or communication interference. The system scans the timestamp intervals of the entire data sequence, identifies the data positions corresponding to the missing timestamps, and adds specific markers to these positions. In some embodiments, for the identified missing timestamp data points, a linear interpolation algorithm is used to fill them. The linear interpolation algorithm uses the effective data point values ​​of the adjacent timestamps before and after the missing point to calculate the linear estimate between the two and insert it into the missing position, thereby forming a continuous and uninterrupted voltage, current, and temperature data sequence in the time dimension. It can be understood that linear interpolation can better maintain the local trend of the data and avoid introducing drastic fluctuations. After interpolation, the data sequence achieves complete coverage on the time axis.

[0062] In practice, the outlier removal process is performed on continuous voltage, current, and temperature data sequences. The deviation of each data point from its neighboring data points within a certain window is calculated. This deviation is typically calculated using absolute difference or standard deviation methods. If the calculated deviation exceeds a preset tolerance threshold for that type of parameter, the data point is considered an outlier and removed from the sequence. Optionally, the tolerance threshold can be dynamically adjusted based on historical data statistical characteristics, such as by setting it based on moving averages or standard deviation multiples. After removing outliers, gaps may remain in the data sequence, but these gaps are not subject to secondary interpolation to maintain data accuracy. In some embodiments, the data sequence after outlier removal needs to be normalized. Normalization scales the voltage, current, and temperature data to the same numerical range, such as zero to one. The scaling formula uses a minimum-maximum normalization method, mapping the minimum value of each sequence to zero, the maximum value to one, and the intermediate values ​​linearly, thus unifying all data scales and eliminating dimensional effects. Understandably, normalization helps subsequent fluctuation assessment algorithms treat data of different physical quantities fairly, avoids certain parameters from dominating the assessment results due to their large values, and ultimately obtains a clean historical data set to provide high-quality input for parameter fluctuation assessment.

[0063] In practical implementation, the timestamp continuity check for integrity verification involves parsing the timestamp information attached to each sampled data point. Timestamps are typically stored in Unix timestamp or standard date and time format. The system sorts all data points chronologically and calculates the interval between adjacent timestamps. If the interval exceeds the sampling period threshold, a missing data point is identified. Optionally, the sampling period threshold is determined based on the monitoring system's preset sampling frequency. For example, if the sampling frequency is once per second, the threshold can be set to 1.5 seconds. Data points exceeding the threshold are considered missing. After identifying the data point corresponding to the missing timestamp, the system records the missing location index for subsequent processing. When filling missing timestamp data points using linear interpolation, the algorithm needs to obtain the data values ​​of the preceding and following valid timestamps. Then, it performs a linear weighted calculation based on the relative position of the missing timestamp within the preceding and following timestamps. The filled value equals the preceding value plus the product of the difference between the preceding and following values ​​and the time ratio, thus ensuring a smooth transition in the filled data. The deviation calculation in the outlier removal phase can employ a sliding window method. The window size is set according to the data characteristics; for example, the window might include five points before and after the current point. The average or maximum deviation between the current point and its neighboring points within the window is calculated. If the deviation exceeds a tolerance threshold, it is marked as an outlier. The tolerance threshold might be set to 5% of the rated voltage for voltage data, 10% of the rated current for current data, and 15% of the allowable temperature rise for temperature data. Removing outliers may shorten the sequence length but improve data quality. The normalization phase requires calculating the minimum and maximum values ​​for the voltage, current, and temperature data sequences separately. Then, a scaling formula is applied to each data point. After scaling, all data points fall within the zero-to-one range, making the voltage, current, and temperature sampled data numerically comparable. This clean historical data set is thus generated and stored for subsequent analysis.

[0064] In practice, the integrity verification and outlier removal process can be accomplished by writing a dedicated data cleaning module. This module reads the original voltage sampling data, current sampling data, and temperature sampling data files, parses the data format, and performs the above steps. The module outputs a clean historical data set file or database records.

[0065] Example 2: See Figure 3In practical implementation, the parameter fluctuation assessment phase analyzes the cleanroom historical data set to quantify the stability of voltage, current, and temperature signals. The assessment process calculates specific fluctuation indices for voltage, current, and temperature data sequences respectively. For the voltage data sequence, the oscillation amplitude index is calculated. The oscillation amplitude is obtained by calculating the absolute value of the difference between all adjacent sampling points in the voltage data sequence, and then averaging these absolute values. This average value reflects the average intensity of voltage change within a unit sampling interval. In some embodiments, the calculation of the oscillation amplitude of the voltage data sequence requires traversing every position in the sequence from the first sampling point to the second-to-last sampling point, calculating the absolute value of the difference between the value of the corresponding sampling point at each position and the value of the next sampling point, summing all absolute values, and dividing by the total number of absolute values ​​to obtain the oscillation amplitude value. It can be understood that the smaller the oscillation amplitude value, the more stable the voltage data sequence; the larger the value, the more severe the voltage fluctuation.

[0066] In practical implementation, for current data sequences, the drift range index is evaluated. Calculating the drift range requires identifying the maximum and minimum values ​​in the current data sequence and calculating their difference. The current data sequence is a sequence of current sampled values ​​extracted from a clean historical data set and arranged in chronological order. The maximum value is found by traversing the entire current data sequence. A variable is initialized to store the current maximum value, initially set to the first element of the current data sequence. Then, starting from the second element, each element is compared with the current maximum value. If the current element value is greater than the current maximum value, the current maximum value is updated to that element value. After traversal, the current maximum value is the maximum value of the current data sequence. The minimum value is found using a similar method. The current minimum value is initialized to the first element of the current data sequence. Then, subsequent elements are compared sequentially. If the current element value is less than the current minimum value, the current minimum value is updated to that element value. After traversal, the minimum value of the current data sequence is obtained. The difference between the maximum and minimum values ​​is calculated by subtracting the minimum value from the maximum value, yielding the range. This range reflects the absolute range of change of the current data sequence throughout the entire observation period. The current maximum and minimum values ​​are initialized as the first elements of the current data sequence. Then, starting from the second element, the sequence iterates. In each iteration, the current element is compared with the current maximum value; if greater, the current maximum value is updated. Then, the current element is compared with the current minimum value; if less, the current minimum value is updated. This synchronous traversal method requires only one linear scan of the current data sequence, with a time complexity of O(n), where n is the length of the current data sequence. The range is calculated after the traversal is complete, ensuring numerical stability and avoiding floating-point calculation errors. Optionally, for particularly long current data sequences, a divide-and-conquer strategy can be used to find the maximum and minimum values ​​in parallel. The current data sequence is divided into multiple subsequences, and the local maximum and local minimum values ​​of each subsequence are found in different computational units. All local maximum values ​​are compared to obtain the global maximum, and all local minimum values ​​are compared to obtain the global minimum. The range is still calculated by subtracting the global minimum from the global maximum. This method is suitable for distributed computing environments or scenarios requiring the processing of massive historical data.

[0067] In practice, after calculating the range, it needs to be divided by the arithmetic mean of the current data sequence to obtain the drift range. The arithmetic mean is calculated by summing all elements of the current data sequence and dividing by the number of elements. The summation operation is achieved by traversing the current data sequence and accumulating all current values, then dividing by the sequence length n to obtain the average value. When calculating the range divided by the average, it is necessary to check whether the average value is zero. If the average value is close to zero, the drift range may be meaningless, requiring special handling, such as directly using the range or marking the data as abnormal. As a dimensionless relative indicator, the drift range can eliminate the influence of different current reference values ​​on fluctuation judgment, allowing for comparison of motherboard current stability under different operating currents. It is understandable that this drift range calculation method based on the range and average value is simple, intuitive, and computationally efficient, making it suitable for integration into automated performance testing and analysis processes.

[0068] For temperature data series, the degree of fluctuation is evaluated using an index. This fluctuation is calculated by dividing the standard deviation of the temperature data series by its mean; this ratio, known as the coefficient of variation, reflects the relative dispersion of temperature values ​​around the mean. Calculating the standard deviation of a temperature data series involves first obtaining the mean, then calculating the square of the difference between each temperature sample and the mean, averaging these squares, taking the square root, and finally dividing the standard deviation by the mean to obtain the fluctuation index. In essence, the fluctuation index eliminates the interference of a high or low temperature baseline on the judgment of fluctuation, allowing for comparison of fluctuations at different average temperatures.

[0069] In practical implementation, the calculated oscillation amplitude, drift range, and fluctuation degree are all numerical values ​​with original physical dimensions. They need to be mapped to a zero-to-one range to generate standardized stability estimates. The mapping process uses a minimum-maximum normalization method. The voltage stability estimate is generated by mapping the voltage oscillation amplitude value to a zero-to-one range. During mapping, a reasonable range for the voltage oscillation amplitude needs to be determined based on historical data or prior knowledge. The minimum value within the range is mapped to zero, the maximum value to one, and intermediate values ​​are linearly interpolated. The current stability estimate is generated by mapping the current drift range value to a zero-to-one range, similarly using minimum-maximum normalization based on the possible value range of the current drift range. The temperature stability estimate is generated by mapping the temperature fluctuation degree value to a zero-to-one range, determining the mapping interval based on the historical statistical characteristics of the temperature fluctuation degree. Optionally, the mapping interval can be determined based on the minimum and maximum values ​​of each indicator obtained from a large amount of historical data, or theoretical limit values ​​can be set as boundaries according to circuit design specifications. The calculation of voltage stability estimates, current stability estimates, and temperature stability estimates can be implemented through a separate fluctuation assessment module. This module receives a clean historical dataset as input and processes the voltage, current, and temperature data sequences respectively. The fluctuation assessment module contains three parallel processing units, each specifically calculating the oscillation amplitude, drift range, and fluctuation degree. Then, a normalization unit maps the three index values ​​to the zero-to-one range. It can be understood that the closer the stability estimate is to zero, the more unstable the parameter; the closer it is to one, the more stable the parameter. This normalization process allows the stability of different physical quantities to be compared and integrated on the same scale. The output of the fluctuation assessment module is three values ​​within the zero-to-one range, which serve as the voltage stability estimate, current stability estimate, and temperature stability estimate, respectively. These estimates will be used as input features for the detection cycle recommendation network.

[0070] In practice, oscillation amplitude calculation involves differential operations on the voltage data sequence. The differential operation yields a sequence of absolute values ​​of the differences between adjacent points. Then, the arithmetic mean of this absolute value sequence is calculated; this average is the original value of the oscillation amplitude. Drift range calculation requires extreme value searching on the current data sequence to find the global maximum and minimum values. The range is calculated, and then divided by the average to obtain the original value of the drift range. Fluctuation degree calculation requires calculating the standard deviation of the temperature data sequence. The standard deviation is the positive square root of the variance, which is the average of the squares of the differences between each temperature sample and the average. Finally, the standard deviation is divided by the average to obtain the original value of the fluctuation degree. These three original values ​​are converted into voltage stability estimates, current stability estimates, and temperature stability estimates within the range of zero to one using normalization functions. The normalization functions require preset theoretical minimum and maximum values ​​for each indicator; these boundary values ​​can be pre-configured according to circuit characteristics. The output of the fluctuation assessment stage is three standardized estimates that comprehensively reflect the voltage stability, current stability, and temperature stability performance of the integrated circuit motherboard during the historical observation period.

[0071] See Figure 4 This figure is a key visualization result of the parameter fluctuation assessment stage in the integrated circuit motherboard performance testing and analysis method. The horizontal axis represents the motherboard number, and the vertical axis represents the stability estimate; in the legend, the blue, cyan, and yellow bars correspond to voltage stability, current stability, and temperature stability, respectively. Technically, these stability estimates are the core inputs to the testing cycle recommendation network. Motherboards with lower stability require higher testing frequencies to avoid performance degradation or delayed fault warnings; motherboards with higher stability can have lower testing frequencies, saving resources. This figure provides an intuitive decision-making basis for adaptive testing cycles by quantitatively comparing the stability differences of different motherboards in the voltage, current, and temperature dimensions. It is a crucial link in achieving a breakthrough from fixed-cycle testing to data-driven dynamic testing technology, effectively solving the shortcomings of fixed-cycle testing in the background technology, such as resource waste or untimely warnings.

[0072] Example 3: In specific implementation, the construction of the detection cycle recommendation network is a supervised learning process, requiring the collection of a large amount of historical data as training samples. The training samples include multiple sets of historical voltage stability estimates, historical current stability estimates, and historical temperature stability estimates, as well as historical detection cycle data corresponding to each set of estimates that have been proven reasonable in practice. These historical detection cycle data represent the optimal detection interval under specific stability states, collectively constituting the training sample set. Each sample in the training sample set is a quadruple, containing three input feature values ​​(historical voltage stability estimate, historical current stability estimate, and historical temperature stability estimate) and a target output value (historical detection cycle data). The number of samples needs to be sufficiently large to cover various possible combinations of stability states. In some embodiments, the training samples can be collected from monitoring records of multiple long-running integrated circuit motherboards. The appropriate detection cycle for each historical time period is determined through expert experience or offline analysis, forming labeled data.

[0073] In implementation, the network structure employs a three-layer feedforward neural network. The input layer consists of three nodes, each receiving one of three input features: historical voltage stability estimates, historical current stability estimates, and historical temperature stability estimates. The output layer comprises one node, whose output corresponds to the recommended detection period, typically expressed in seconds or milliseconds. The number of nodes in the hidden layers needs to be determined based on the problem complexity and the amount of training data. Too many hidden layer nodes may lead to overfitting, while too few may lead to underfitting; the optimal number needs to be determined experimentally. It is understood that the three-layer feedforward neural network structure can learn the nonlinear mapping relationship between input features and output periods, and the nonlinear activation function of the hidden layers is key to achieving this capability.

[0074] In practice, the training process employs backpropagation for supervised learning. During training, samples from the training set are input into the network individually or in batches. The error between the network output value and the actual historical detection period data is calculated. Then, the weights of each connection node in the network are adjusted layer by layer based on the error. The error calculation typically uses the mean squared error function, and the weight adjustment is based on the gradient descent principle. The direction and magnitude of the adjustment are determined by calculating the partial derivative of the error with respect to each weight, and the learning rate controls the adjustment step size. The training process is iterated multiple times until the prediction error falls below a set threshold or the training epochs reach their limit, indicating that the network has learned the mapping relationship from stability estimation to detection period. The trained network parameters and structure are saved. The network parameters include the weight matrix from the input layer to the hidden layer, the weight vector from the hidden layer to the output layer, the bias vector of the hidden layer nodes, and the bias value of the output layer nodes. The network structure includes the number of nodes in each layer and the type of activation function, which together form the detection period recommendation network.

[0075] In practical implementation, when inputting the real-time calculated voltage stability estimate, current stability estimate, and temperature stability estimate into the pre-trained detection period recommendation network, the three estimates need to be combined into a three-dimensional input vector. This input vector is fed into the input layer of the detection period recommendation network. The three nodes in the input layer receive the voltage stability estimate, current stability estimate, and temperature stability estimate, respectively, and directly pass the values ​​to the hidden layer without transformation. Each node in the hidden layer calculates the weighted sum of the input vector and its corresponding weight vector, adds its bias value, and then performs a nonlinear transformation through an activation function. The activation function can be either a sigmoid function or a rectified linear unit function. The output calculation of the hidden layer nodes can be expressed as:

[0076]

[0077] in: It is the first The output of each hidden layer node It is an activation function. It is the input layer. The node is connected to the hidden layer. The connection weight of each node It is the first of the input vectors Each component (i.e., voltage stability estimate, current stability estimate, or temperature stability estimate). It is the hidden layer. The bias values ​​of each node are calculated. The output layer node receives the outputs of all nodes in the hidden layer, calculates the weighted sum, adds the bias values ​​of the output layer nodes, and then generates the final output value through the output activation function. The output activation function can be selected as a linear function or a sigmoid function depending on the range of the detection period.

[0078] In some embodiments, the calculation of the output layer node can be represented as:

[0079]

[0080] in: It is the output value of the output layer node. It is the output activation function. It is the hidden layer. The connection weights from each node to the output layer node It is the number of hidden layer nodes. It is the bias value of the output layer node. It is the first The output of each hidden layer node. Output value It needs to be mapped to the actual detection cycle range. The mapping function is designed according to the actual application requirements. For example, it can be... The detection period is linearly scaled between the minimum and maximum detection periods. This means the detection period recommendation network can adaptively recommend a detection frequency based on the motherboard's current stability; a longer detection period is recommended when stability is high to reduce resource consumption, while a shorter detection period is recommended when stability is low to enhance monitoring.

[0081] In some embodiments, the training of the detection period recommendation network can employ various techniques to improve performance, such as using momentum to accelerate gradient descent convergence, using regularization to prevent overfitting, and using early stopping to avoid overtraining. Network evaluation after training should be performed on an independent test set that does not overlap with the training set. Evaluation metrics include mean absolute error or root mean square error to ensure the network has good generalization ability. Optionally, the detection period recommendation network can also be implemented using other machine learning models, but a three-layer feedforward neural network has the advantages of being easy to train and capable of approximating any continuous function.

[0082] Example 4: In specific implementation, the real-time monitoring process is deployed based on the customized detection frequency recommended by the network output during the detection cycle. The customized detection frequency is a value in units of time, representing the ideal interval for data acquisition. For example, the customized detection frequency may be 100 milliseconds or 1 second. Starting the real-time monitoring process requires initializing a timer component. The timer component can be a software-implemented counting timer or a hardware-supported high-precision timer. The configuration parameters of the timer component are directly derived from the customized detection frequency, and the trigger period of the timer component is set equal to the customized detection frequency value. After the timer component starts running, it periodically generates data acquisition events according to the customized detection frequency. A data acquisition event is a system interrupt or software signal used to notify the data acquisition module to perform a reading operation. The data acquisition event handler needs to ensure timely response upon triggering to avoid missing acquisition opportunities. In some embodiments, the implementation of the timer component can be based on the time service provided by the real-time operating system. The precision adjustment of the timer component is achieved by setting the interval time in the itimerspec structure, ensuring strict synchronization with the customized detection frequency.

[0083] In practical implementation, when the timer component triggers a data acquisition event, the data acquisition module is activated. The data acquisition module needs to synchronously read data from various sensors connected to the integrated circuit motherboard, including voltage sensor readings, current sensor readings, temperature sensor readings, and jitter indicators output by the signal analyzer. Synchronous reading requires all data points to be captured at the same time point as much as possible to reduce time deviations introduced by sequential readings. Synchronous reading can be achieved by accessing the sensor interfaces in parallel or by using a synchronization signal to trigger multiple sensors to sample simultaneously. Voltage sensor readings are acquired through an ADC chip, which typically connects to test points on the motherboard. The ADC chip converts analog voltage values ​​into digital values. Current sensor readings are obtained through a Hall effect sensor or a sampling resistor circuit; the current value is amplified and digitized. Temperature sensor readings are read through a thermistor or a digital temperature sensor such as the DS18B20. The jitter indicators output by the signal analyzer are measured by dedicated analyzer hardware or a timer circuit integrated on the motherboard, reflecting the stability of the signal timing. All readings are read immediately after the data acquisition event is triggered and temporarily stored in a buffer.

[0084] Optionally, the synchronous reading process can be optimized using multi-threading techniques, such as creating an independent reading thread for each sensor type. All threads start synchronously when a data acquisition event is triggered, and a barrier synchronization mechanism ensures consistent reading completion times. If a sensor fails or times out during the reading process, the data acquisition module can record an error flag but continue reading data from other available sensors to maintain partial data integrity. The raw values ​​of the voltage sensor readings, current sensor readings, temperature sensor readings, and jitter index need to undergo preliminary verification, such as range checks, to exclude obviously invalid values, before being prepared for time stamping. Each successfully acquired reading must be accurately time-stamped using a high-precision clock source, such as a Unix timestamp synchronized with the system clock via NTP, or a nanosecond-level timestamp provided by a hardware counter. The additional time-stamping operation is performed immediately after data reading to ensure accurate correspondence between the time stamp and the reading. After time-stamping, the readings are assembled into a real-time data record, which is a structured data object containing a timestamp field, a voltage reading field, a current reading field, a temperature reading field, and a jitter index field. Real-time data records can be stored in memory or persistently. Memory storage uses a circular buffer to manage and save the latest record in a circular overwrite manner. Persistent storage writes records to a database or file system, such as using an SQLite database table, with each record inserted as a row. The structure of real-time data records is shown in Table 1, which illustrates the field definitions and data types of the records.

[0085] Table 1: Real-time Data Recording Structure Table

[0086] field name Data types describe Time stamp Timestamp The precise time of data collection, in the format YYYY-MM-DDHH:MM:SS.ssssss, for example, 2023-10-01 12:00:00.123456 Voltage reading floating-point numbers The voltage sensor reads the voltage value in volts, and the range is determined by the motherboard design, for example, 0-5V. Current reading floating-point numbers The current sensor reads the current value in amperes, and the range is determined by the motherboard load, for example, 0-10A. Temperature reading floating-point numbers The temperature sensor reads temperature values ​​in degrees Celsius, with a range determined by heat dissipation conditions, for example, -40 to 125°C. jitter index floating-point numbers The jitter value output by the signal analyzer, in nanoseconds, represents the time deviation of the signal edge.

[0087] Optionally, real-time data recordings can be stored using compression algorithms to reduce storage space, such as differential encoding or quantization techniques for floating-point segments. However, the compression process must ensure reversible decompression to avoid information loss. Indexing mechanisms, such as time-stamped B-tree indexes, can also be added during storage to accelerate subsequent data retrieval and collection assembly operations. Real-time data records accumulate sequentially over time, forming a serialized data stream.

[0088] In practice, the accumulation of real-time data records across multiple time points is achieved through a data management module. This module monitors the number of records in the storage area and triggers the collection assembly process when the number of records reaches a preset threshold or after a fixed time window. The collection assembly process retrieves a series of real-time data records from the storage medium at consecutive time points, arranges them in ascending order by timestamp, and assembles them into a real-time data set. The real-time data set can be represented as a time-series array or a data frame structure, with each time point corresponding to one record. The overall size of the real-time data set depends on the customized detection frequency and the accumulated time length. For example, if the customized detection frequency is 1 second and the accumulated time is 1 minute, the real-time data set contains 60 records. The real-time data set is maintained in memory as a dynamic array or linked list, supporting fast iterative access, and can also be exported to file formats such as CSV or JSON for external analysis. During the assembly process, the data management module verifies the temporal continuity of the records. If a timestamp interval does not match the customized detection frequency, an empty record is inserted or a missing point is marked, but the originality of the data is prioritized.

[0089] Example 5: In specific implementation, the pattern recognition engine performs multi-dimensional analysis on the real-time data set to classify the performance status categories of the integrated circuit motherboard. The analysis process first extracts voltage reading sequences, current reading sequences, temperature reading sequences, and signal quality index sequences from the real-time data set. The real-time data set consists of multiple real-time data records arranged in chronological order. Each real-time data record contains a timestamp, voltage reading, current reading, temperature reading, and jitter index fields. The sequence extraction operation sorts and groups the field values ​​based on the timestamp. The voltage reading sequence is a list of voltage readings from all real-time data records arranged in chronological order; the current reading sequence is a list of current readings arranged in chronological order; the temperature reading sequence is a list of temperature readings arranged in chronological order; and the signal quality index sequence is a list of jitter index values ​​arranged in chronological order. After sequence extraction, completeness needs to be verified to check for missing time points or outliers. However, since the continuity of the real-time data set is usually processed during the assembly stage, it directly enters the feature calculation stage. In some embodiments, sequence extraction can be achieved using database query operations, such as executing a SELECT statement on an SQLite database storing a real-time dataset to retrieve specific fields sorted by timestamp, or using list operations in a programming language to slice sequence values ​​from an in-memory data structure.

[0090] In practical implementation, calculating the statistical characteristics of each sequence is the core step of feature engineering. For voltage reading sequences, the statistical characteristics include mean, variance, skewness, and kurtosis. The mean is calculated as the arithmetic mean of all voltage readings in the sequence, i.e., the sum of all voltage values ​​divided by the number of voltage values. The variance is calculated as the average of the squares of the differences between each voltage reading and the mean, reflecting the dispersion of the voltage values. Skewness is calculated as the third standard moment of the voltage reading sequence, measuring the asymmetry of the voltage value distribution. Kurtosis is calculated as the fourth standard moment of the voltage reading sequence, measuring the sharpness of the voltage value distribution compared to a normal distribution. For current reading sequences, the mean, variance, skewness, and kurtosis are calculated similarly, using the same methods as for voltage reading sequences, but applied to current data. For temperature reading sequences, the mean, variance, skewness, and kurtosis are calculated. For a signal quality index sequence, statistical characteristics include the mean and coefficient of variation of jitter values. The mean jitter value is calculated as the arithmetic mean of all jitter index values, and the coefficient of variation is calculated as the standard deviation of the jitter index sequence divided by the mean jitter value, reflecting the relative volatility of jitter. Optionally, the mean can be calculated using an iterative algorithm to avoid large-number accumulation errors, the variance can be calculated using a two-pass algorithm or an online algorithm, and the skewness and kurtosis are calculated based on the sample moment estimation formula, but unbiased correction should be noted.

[0091] In some embodiments, the specific implementation of statistical feature calculation can be achieved by writing a feature extraction function, which takes a numerical sequence as input and outputs feature values. Skewness calculation requires first calculating the mean and mean, then using a formula based on third-moment normalization; kurtosis calculation requires normalization based on fourth-moment normalization; and the coefficient of variation calculation for the signal quality index sequence requires first calculating the standard deviation and mean. All calculations ensure numerical stability, avoiding division-by-zero errors or overflow. Optionally, for short sequences, statistical features may not be robust; therefore, the real-time dataset needs to contain a sufficient number of real-time data records, such as at least 30 points, to ensure feature representativeness.

[0092] In practice, forming a feature vector is the process of combining all calculated statistical features into a high-dimensional vector. The feature vector serves as input to the classification model, and its dimension depends on the number of sequences and the number of features calculated for each sequence. For example, voltage reading sequences contribute four features (mean, variance, skewness, and kurtosis), current reading sequences contribute four features, temperature reading sequences contribute four features, and signal quality index sequences contribute two features (mean and coefficient of variation), resulting in a total feature dimension of 14. Feature vectors are assembled in a fixed order, such as voltage features first, then current features, then temperature features, and finally signal features, ensuring consistent feature vector structure each time. Feature vectors require normalization to ensure that all feature values ​​are at a similar scale. For example, min-max normalization can be used to scale each feature to the range of zero to one, preventing certain features from dominating the classification result due to their large values. Normalization parameters are obtained statistically from the training data and reused in applications. Optionally, feature vectors can be dimensionality-reduced, such as through principal component analysis to reduce redundancy, but this increases complexity; usually, the original feature vectors are used directly.

[0093] In practice, the pre-trained classification model uses decision tree rules for state partitioning. The training process employs the random forest algorithm, an ensemble learning method that improves classification accuracy by constructing multiple decision trees and combining voting results. Training the classification model requires collecting historical real-time data sets and their corresponding known performance status labels. The historical real-time data set is a collection of previously collected real-time data records, and the known performance status labels are assigned by experts based on motherboard behavior, such as "normal," "warning," and "abnormal," together forming the training dataset. The training process randomly selects multiple subsets from the training dataset. Each subset is obtained through sampling with replacement, called a bootstrap set, and a decision tree is constructed for each bootstrap set. When constructing the decision tree, each node split only considers a randomly selected feature subset. The size of the feature subset is typically the square root or logarithm of the total number of features, increasing the diversity between trees. Decision tree growth involves recursively splitting nodes, selecting the optimal splitting features and thresholds to maximize information gain or reduce Gini impurity. Tree growth stops when node purity is too high or the number of samples is too low. After training, the structural parameters and splitting rules of all decision trees, as well as the voting mechanism, are saved to form the classification model. Optionally, hyperparameters of random forests, such as the number of trees and maximum depth, can be adjusted through cross-validation to avoid overfitting.

[0094] In practical implementation, when the feature vector is input into the pre-trained classification model, each decision tree within the model makes predictions independently. Each decision tree starts from the root node and traverses the tree branches to the leaf nodes based on the feature values ​​of the feature vector. The leaf nodes store a class probability or a direct class label. The random forest integrates the prediction results of all decision trees and determines the final classification result through a majority voting mechanism; that is, the class with the most votes is output as the performance evaluation label. For example, if there are 100 decision trees, 60 predict "normal," 30 predict "warning," and 10 predict "abnormal," then the output performance evaluation label is "normal." The voting process can be weighted, assigning weights based on the accuracy of the trees, but usually simple majority voting is sufficient. The application of the classification model needs to be implemented efficiently, especially for scenarios with high real-time requirements. Decision tree predictions can be optimized through pre-compiled rules or lookup tables. It can be understood that the random forest classification model can handle non-linear relationships, is robust to noise, and is suitable for classifying the performance status of integrated circuit motherboards. Optionally, the classification model can be updated periodically, retrained with new data to adapt to motherboard aging or environmental changes.

[0095] In practical implementation, the overall implementation of the pattern recognition engine can be encapsulated as a software module. The module takes a real-time dataset as input and outputs performance evaluation labels. The internal process includes sequence extraction, feature calculation, feature vector assembly, and classification prediction. In the sequence extraction stage, voltage reading sequences, current reading sequences, temperature reading sequences, and signal quality index sequences are parsed from the real-time dataset. In the feature calculation stage, statistical functions are called to calculate the mean, variance, skewness, kurtosis, and signal characteristics of each sequence. In the feature vector assembly stage, feature values ​​are combined sequentially and normalized. In the classification prediction stage, a pre-trained classification model file is loaded for inference. The module is implemented using programming languages ​​such as Python or C++, utilizes scientific computing libraries for numerical calculations, and loads a random forest model using machine learning libraries. The module is deployed on a performance analysis server and integrated with the real-time monitoring process to achieve automated performance evaluation. Optionally, the module can add a logging function to record intermediate results during the classification process for debugging and auditing.

[0096] See Figure 5 This image illustrates four key statistical data points related to jitter in integrated circuit motherboard performance testing. These data are crucial inputs for the pattern recognition engine to analyze real-time datasets, perfectly aligning with the technical workflow. During real-time monitoring, jitter metrics output by the signal analyzer are simultaneously acquired. Subsequently, the average and coefficient of variation of the jitter values ​​are calculated to form the statistical characteristics of the signal quality index sequence. The minimum and maximum values ​​define the fluctuation range of the jitter index, the average value reflects the overall level of jitter, and the coefficient of variation quantifies the relative dispersion of jitter. The data format in this image directly corresponds to the technical requirements for calculating the statistical characteristics of the signal quality index sequence, providing crucial data support for performance status classification and serving as a vital step in achieving multi-dimensional intelligent diagnostics.

[0097] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0098] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A method for performance testing and analysis of an integrated circuit motherboard, characterized in that, The method includes: Export voltage, current, and temperature sampling data within a historical time range from the monitoring system of the integrated circuit motherboard, perform integrity verification and outlier removal on the voltage, current, and temperature sampling data, and generate a clean historical data set. A parameter fluctuation assessment is performed on the clean historical data set. By calculating the oscillation amplitude of voltage data, the drift range of current data, and the fluctuation of temperature data, voltage stability estimates, current stability estimates, and temperature stability estimates are generated. The voltage stability estimate, current stability estimate, and temperature stability estimate are input into a pre-trained detection cycle recommendation network, which outputs a customized detection frequency by comparing with a historical pattern library. Based on the customized detection frequency, a real-time monitoring process is deployed to capture instantaneous voltage readings, instantaneous current readings, instantaneous temperature readings, and signal quality indicators of the integrated circuit motherboard, and to assemble a real-time data set; A pattern recognition engine is used to perform multi-dimensional analysis on the real-time dataset, classify performance status categories, and output performance evaluation labels. The pre-trained detection cycle recommendation network is constructed in the following way: Collect multiple sets of historical voltage stability estimates, historical current stability estimates, and historical temperature stability estimates, along with corresponding historical detection cycle data, to form a training sample set; A three-layer neural network structure is constructed. The input layer contains three nodes corresponding to voltage stability estimation, current stability estimation, and temperature stability estimation, respectively. The output layer has one node corresponding to the detection period. Supervised learning of the neural network is performed using a training sample set, and the network weights are adjusted through the backpropagation algorithm until the prediction error is lower than a set threshold. Save the trained network parameters and structure as a recommendation network for the detection cycle; The multi-dimensional analysis of the real-time data set using a pattern recognition engine includes: Extract voltage reading sequences, current reading sequences, temperature reading sequences, and signal quality index sequences from the real-time dataset; Calculate the statistical characteristics of each sequence, including mean, variance, skewness, and kurtosis, to form a feature vector; The feature vector is input into a pre-trained classification model, which performs state division based on decision tree rules; The classification results are output as performance evaluation labels; The pre-trained classification model is trained through the following process: Collect historical real-time data sets and their corresponding known performance status labels as training datasets; A subset is randomly selected from the training dataset to construct multiple decision trees, each using a different subset of features; The final classification rule is determined by combining the voting results of multiple decision trees; Save the decision tree parameters and voting mechanism as a classification model.

2. The performance testing and analysis method for integrated circuit motherboards according to claim 1, characterized in that, The integrity verification and outlier removal of the voltage sampling data, current sampling data, and temperature sampling data include: Check the continuity of timestamps for voltage, current, and temperature sampling data, and identify the data points corresponding to missing timestamps. For data points with missing timestamps, linear interpolation is used to fill the missing timestamps, forming a continuous data sequence. Calculate the deviation between each data point and its neighboring data points in a continuous data sequence. If the deviation exceeds a preset tolerance threshold, mark it as an outlier and remove it. The data sequence after removing outliers is normalized to ensure that all data are scaled uniformly, resulting in a clean historical data set.

3. The performance testing and analysis method for integrated circuit motherboards according to claim 2, characterized in that, The parameter fluctuation assessment performed on the clean historical data set includes: Extract voltage data sequences from the clean historical data set, and calculate the average absolute value of the difference between adjacent sampling points in the voltage data sequence as the oscillation amplitude; Extract the current data sequence from the clean historical data set, and calculate the difference between the maximum and minimum values ​​of the current data sequence divided by the average value as the drift range; Extract the temperature data sequence from the clean historical data set, and calculate the ratio of the standard deviation to the mean of the temperature data sequence as the degree of fluctuation; By mapping the oscillation amplitude, drift range, and fluctuation degree to the range of zero to one, we obtain the voltage stability estimate, current stability estimate, and temperature stability estimate.

4. The performance testing and analysis method for integrated circuit motherboards according to claim 1, characterized in that, The step of inputting the voltage stability estimate, current stability estimate, and temperature stability estimate into the pre-trained detection cycle recommendation network includes: Combine the voltage stability estimate, current stability estimate, and temperature stability estimate into an input vector; The input vector is fed into the input layer of the detection period recommendation network, and a weighted sum is calculated through the hidden layer nodes and an activation function is applied. The output layer nodes calculate the final value and map the final value to the actual detection cycle range to obtain the customized detection frequency.

5. The performance testing and analysis method for integrated circuit motherboards according to claim 4, characterized in that, The deployment of real-time monitoring process includes: Set a timer according to the customized detection frequency to trigger data acquisition events; When the timer is triggered, the voltage sensor readings, current sensor readings, temperature sensor readings, and jitter index output by the signal analyzer are read synchronously from the integrated circuit motherboard. Each collected reading is time-stamped and stored as a real-time data record; Accumulate real-time data records from multiple time points and assemble a real-time data set.

6. The performance testing and analysis method for integrated circuit motherboards according to claim 1, characterized in that, The calculation of the statistical characteristics of each sequence includes: For a voltage reading sequence, the arithmetic mean of all voltage values ​​is calculated as the mean, the average square of the voltage values ​​deviating from the mean is calculated as the variance, the asymmetry of the voltage value distribution is calculated as the skewness, and the sharpness of the voltage value distribution is calculated as the kurtosis. Perform the same calculations on the current reading sequence and the temperature reading sequence to obtain their respective mean, variance, skewness, and kurtosis; For a sequence of signal quality indicators, calculate the average value and coefficient of variation of the jitter values.

7. A performance testing and analysis system for an integrated circuit motherboard, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the performance detection and analysis method for the integrated circuit motherboard as described in any one of claims 1 to 6.

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