Method and system for testing and evaluating reliability of integrated circuit chip

Abstract

The invention provides a reliability test evaluation method and system for an integrated circuit chip, and relates to the technical field of integrated circuit chip test.The method comprises the steps that a test target and a test index are defined; constructing a multi-level test environment; performing a dynamic stress test in the constructed multi-level test environment; based on the historical test data and the monitoring data, establishing a prediction model by using a machine learning algorithm to predict performance changes and potential faults of the integrated circuit chip under different conditions; performing intelligent diagnosis on faults occurring in the test process by using the established prediction model, and positioning a fault source; and continuously monitoring the test process through the online remote monitoring platform, and dynamically adjusting the test parameters and the test strategy according to the monitoring data and the prediction result of the prediction model. The reliability of the integrated circuit chip is comprehensively and accurately evaluated, and the test efficiency and the fault prevention capability are improved through a multi-level test environment, machine learning prediction and intelligent diagnosis.

Description

Technical Field

[0001] The present invention relates to the technical field of integrated circuit chip testing, and in particular to a reliability testing and evaluation method and system for an integrated circuit chip. Background Art

[0002] With the rapid development of electronic technology, integrated circuit chips, as core components of electronic equipment, have a reliability that is directly related to the stability and operating efficiency of the entire system. Therefore, it is particularly important to accurately test and evaluate the reliability of integrated circuit chips. At present, a variety of test and evaluation methods have been applied to the reliability detection of integrated circuit chips, among which a reliability test and evaluation method for integrated circuit chips disclosed in Chinese patent CN118566697A is a typical representative.

[0003] This method mainly builds a test box with temperature and humidity control functions to simulate different environmental conditions and obtain the performance indicators of integrated circuit chips under different temperatures and humidities. On this basis, stress is further applied to the integrated circuit chip to test its deformation under different stress gradients and performance indicator fluctuations, and the reliability coefficient of the integrated circuit chip under different stress environments is calculated accordingly. By comparing the size of the reliability coefficient, the reliability capability of the integrated circuit chip can be intuitively evaluated, thereby screening out inferior integrated circuit chips and ensuring product quality.

[0004] However, although the test evaluation method disclosed in CN118566697A has improved the accuracy and operability of integrated circuit chip reliability testing to a certain extent, it still has some shortcomings. Specifically, the method mainly focuses on the impact of environmental factors such as temperature and humidity on the performance of integrated circuit chips, but fails to fully consider other external conditions that may affect the reliability of integrated circuit chips.

[0005] In practical applications, the performance of integrated circuit chips is not only affected by temperature and humidity, but also by a variety of external conditions such as electromagnetic interference and vibration interference. These interference factors are often difficult to accurately simulate and control, resulting in certain limitations in existing test and evaluation methods when evaluating the reliability of integrated circuit chips.

[0006] In addition, due to the failure to fully consider these external interference factors, the test results of existing test and evaluation methods are often controversial. Therefore, a more comprehensive and accurate reliability test and evaluation method for integrated circuit chips is urgently needed. Summary of the invention

[0007] To this end, an embodiment of the present invention provides a reliability test and evaluation method and system for an integrated circuit chip, which is used to solve the problem that the reliability test and evaluation method for an integrated circuit chip in the prior art is incomplete and inaccurate.

[0008] In order to solve the above problems, an embodiment of the present invention provides a reliability test and evaluation method for an integrated circuit chip, the method comprising:

[0009] S1: Define test objectives and test indicators;

[0010] S2: Build a multi-level test environment, including building a physical laboratory, building an electromagnetic interference room, equipping a multi-axis vibration table, and building an online remote monitoring platform;

[0011] S3: Perform dynamic stress tests in a constructed multi-level test environment, including random stress tests and fatigue life tests, to evaluate the stability and life of integrated circuit chips under various stress conditions;

[0012] S4: Based on historical test data and monitoring data, a prediction model is established using machine learning algorithms to predict the performance changes and potential failures of integrated circuit chips under different conditions;

[0013] S5: Use the established prediction model to intelligently diagnose faults that occur during the test and locate the source of the fault;

[0014] S6: The test process is continuously monitored through the online remote monitoring platform, and the test parameters and test strategies are dynamically adjusted according to the monitoring data and the prediction results of the prediction model.

[0015] Preferably, in step S1, the test target is to evaluate the long-term stability and life of the integrated circuit chip under extreme temperature, high humidity, voltage fluctuation and electromagnetic interference conditions; the test indicators include failure rate, mean time between failures and performance degradation rate during the life cycle.

[0016] Preferably, the construction of a physical laboratory, the construction of an electromagnetic interference room, the provision of a multi-axis vibration table, and the construction of an online remote monitoring platform specifically include:

[0017] The physical laboratory is equipped with test chambers for different environmental conditions, including high / low temperature cycles, humidity control, and voltage / current fluctuation simulation; the electromagnetic interference chamber simulates electromagnetic environments of different intensities and frequencies; the multi-axis vibration table simulates the stability and life of integrated circuit chips under vibration; and the online remote monitoring platform uses IoT technology to collect real-time operating data, including temperature, power consumption, and operating frequency.

[0018] Preferably, in step S3, the random stress test simulates uncertainties in the real world by randomly generating various stress conditions; the fatigue life test evaluates the life of the integrated circuit chip by continuously running at a specific stress level until the integrated circuit chip fails.

[0019] Preferably, in step S4, the process of establishing a prediction model using a machine learning algorithm includes:

[0020] S41: Data collection and preprocessing, including collecting a large amount of data from physical testing and environmental monitoring, and converting the raw data into a format suitable for model training, where data preprocessing includes Z-score standardization;

[0021] S42: Time series segmentation, for time series data, the data is segmented into input sequence and output sequence, using a window size of T;

[0022] S43: Model training, using machine learning algorithms, including LSTM and RNN, to train the collected data and build a prediction model to predict the performance trend and potential failures of the chip under different conditions;

[0023] S44: Prediction and feedback, the model outputs prediction results to guide the adjustment of test strategies.

[0024] Preferably, in step S5, the method of using the established prediction model to intelligently diagnose faults occurring during the test and locate the source of the fault includes:

[0025] Establish a fault database containing known fault characteristics, and use deep learning technology to conduct in-depth analysis of monitoring data to automatically detect anomalies and locate the source of faults.

[0026] Preferably, the process of establishing a fault database containing known fault features comprises:

[0027] Fault feature data collection: Collect fault samples from historical test data and monitoring data, including fault modes, environmental conditions, and operating status information, and organize the collected data into a structured format to form a database;

[0028] Feature extraction: Signal processing methods are used to extract key features, including time domain features and frequency domain features. Time domain features include mean, variance, peak value and waveform; frequency domain features include Fourier transform and power spectral density;

[0029] Feature labeling: labeling features based on fault samples;

[0030] Establish a fault feature library: organize the extracted features and corresponding fault labels into a database for subsequent comparison and identification.

[0031] The process of combining deep learning technology to deeply analyze monitoring data, automatically detect anomalies and locate fault sources includes:

[0032] Preprocess the monitoring data, including removing noise and outliers, and normalizing the data to make it suitable for the input requirements of the prediction model;

[0033] The monitoring data is input into the prediction model for training. The prediction model training formula is:

[0034]

[0035] Where L is the loss function, N is the number of samples, and yi is the actual output. is the prediction output of the prediction model;

[0036] Input the monitoring data into the trained prediction model and output the failure probability or failure type. The failure probability formula is:

[0037] P(fault)=σ(W·X+b);

[0038] Where P(fault) is the probability of fault occurrence, X is the input feature, W and b are the parameters of the prediction model, and σ is the activation function;

[0039] According to the output fault probability, fault comparison is performed in combination with the fault feature library to quickly identify the fault type and location.

[0040] Preferably, the method for performing fault comparison is:

[0041] The real-time monitoring features are matched with the features in the fault feature library, and the most likely fault type is determined using a similarity metric, where the similarity calculation formula is:

[0042]

[0043] Where A is the feature vector of real-time monitoring, B is the feature vector in the fault feature library, cosine_inilarity(A,B) represents the cosine similarity between vector A and vector B; ||A|| and ||B|| represent the modulus of vector A and vector B respectively.

[0044] The embodiment of the present invention further provides a reliability test and evaluation system for an integrated circuit chip, which is used to implement the reliability test and evaluation method for an integrated circuit chip described above, and specifically includes:

[0045] Test objective and indicator definition module, used to define test objectives and test indicators;

[0046] Multi-level test environment construction module, used to build a multi-level test environment, including building a physical laboratory, building an electromagnetic interference room, equipping a multi-axis vibration table, and building an online remote monitoring platform;

[0047] Dynamic stress test execution module, used to perform dynamic stress tests in a constructed multi-level test environment, including random stress tests and fatigue life tests, to evaluate the stability and life of integrated circuit chips under various stress conditions;

[0048] A prediction model building module is used to build a prediction model based on historical test data and monitoring data using a machine learning algorithm to predict performance changes and potential failures of integrated circuit chips under different conditions;

[0049] The fault intelligent diagnosis and location module is used to use the established prediction model to intelligently diagnose faults that occur during the test and locate the source of the fault;

[0050] The continuous monitoring and dynamic adjustment module is used to continuously monitor the test process through an online remote monitoring platform, and dynamically adjust the test parameters and test strategies based on the monitoring data and the prediction results of the prediction model.

[0051] It can be seen from the above technical solutions that the present invention has the following beneficial effects:

[0052] (1) Comprehensive and realistic test environment: By building a multi-level test environment, including a physical laboratory, an electromagnetic interference room, a multi-axis vibration table, etc., it is possible to simulate a variety of actual working conditions, improve the comprehensiveness and authenticity of the test, and ensure the long-term stability of the chip in the real application environment.

[0053] (2) Intelligent prediction and dynamic adjustment: A prediction model is established using machine learning algorithms to predict the performance changes and potential failures of integrated circuit chips under different conditions. The test process is continuously monitored through an online remote monitoring platform, and test parameters and strategies are dynamically adjusted based on monitoring data and prediction results, thereby improving the efficiency and effectiveness of the test.

[0054] (3) Efficient and accurate fault diagnosis: By establishing a database containing known fault characteristics and combining deep learning for data analysis, faults can be quickly identified and located, reducing the time and cost of fault handling, improving the accuracy and efficiency of fault diagnosis, and helping to continuously improve and reduce the risk of chip failure during use. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] In order to more clearly illustrate the implementation cases of the present invention or the technical solutions in the prior art, the following is a brief description of the drawings required for use in the embodiments. By referring to the drawings, the features and advantages of the present invention will be more clearly understood. The drawings are schematic and should not be understood as limiting the present invention in any way. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work. Among them:

[0056] Figure 1A flowchart of a reliability test and evaluation method for an integrated circuit chip provided in an embodiment;

[0057] Figure 2 A block diagram of a reliability test and evaluation system for an integrated circuit chip provided in an embodiment. DETAILED DESCRIPTION

[0058] 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 conjunction with the drawings in 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.

[0059] Embodiment 1

[0060] In order to solve the problem that the reliability test and evaluation methods of integrated circuit chips in the prior art are incomplete and inaccurate. Figure 1 As shown, an embodiment of the present invention provides a reliability test and evaluation method for an integrated circuit chip, the method comprising:

[0061] S1: Define test objectives and test indicators;

[0062] S2: Build a multi-level test environment, including building a physical laboratory, building an electromagnetic interference room, equipping a multi-axis vibration table, and building an online remote monitoring platform;

[0063] S3: Perform dynamic stress tests in a constructed multi-level test environment, including random stress tests and fatigue life tests, to evaluate the stability and life of integrated circuit chips under various stress conditions;

[0064] S4: Based on historical test data and monitoring data, a prediction model is established using machine learning algorithms to predict the performance changes and potential failures of integrated circuit chips under different conditions;

[0065] S5: Use the established prediction model to intelligently diagnose faults that occur during the test and locate the source of the fault;

[0066] S6: The test process is continuously monitored through the online remote monitoring platform, and the test parameters and test strategies are dynamically adjusted according to the monitoring data and the prediction results of the prediction model.

[0067] It can be seen from the above technical scheme that the present invention proposes a reliability test and evaluation method for integrated circuit chips. By defining clear test objectives and indicators, a multi-level test environment including a physical laboratory, an electromagnetic interference room, a multi-axis vibration table and an online remote monitoring platform is constructed, and dynamic stress testing is implemented to comprehensively evaluate the stability and life of the chip. The method uses a machine learning algorithm to establish a prediction model, predicts chip performance changes and potential faults based on historical test data and monitoring data, and realizes intelligent fault diagnosis and fault source location. The test process is continuously monitored through an online remote monitoring platform, and test parameters and strategies are dynamically adjusted to ensure test efficiency and effectiveness. This solution not only improves the comprehensiveness and authenticity of the test, but also enhances fault prevention and diagnosis capabilities, significantly reduces the risk of chip failures, and improves product quality and market competitiveness. At the same time, the systematic testing and diagnosis method speeds up fault handling, reduces costs, enhances user trust, helps adapt to rapidly changing market demands, and achieves continuous improvement.

[0068] In this embodiment, in step S1, defining the test objectives and test indicators includes:

[0069] The test goal is to evaluate the long-term stability and life of integrated circuit chips under extreme temperature, high humidity, voltage fluctuations and electromagnetic interference conditions;

[0070] Test indicators include failure rate, mean time between failures and performance degradation rate during the life cycle.

[0071] In this embodiment, in step S2, a multi-level test environment is constructed, including constructing a physical laboratory, constructing an electromagnetic interference room, equipping a multi-axis vibration table, and constructing an online remote monitoring platform, including:

[0072] Building a physical laboratory: The physical laboratory is equipped with test chambers with different environmental conditions, including simulations of high / low temperature cycles, humidity control, voltage / current fluctuations, etc.

[0073] Constructing an electromagnetic interference chamber: The electromagnetic interference chamber simulates electromagnetic environments of different intensities and frequencies;

[0074] Equipped with a multi-axis vibration table: The multi-axis vibration table simulates the stability and life of integrated circuit chips under vibration;

[0075] Build an online remote monitoring platform: The online remote monitoring platform uses IoT technology to collect real-time operating data, including temperature, power consumption, operating frequency, etc., through tiny sensors embedded in chips.

[0076] In this embodiment, in step S3, a dynamic stress test is performed in a constructed multi-level test environment, including a random stress test and a fatigue life test, to evaluate the stability and life of the integrated circuit chip under various stress conditions.

[0077] Specifically, random stress testing simulates uncertainties in the real world by randomly generating various stress conditions; fatigue life testing evaluates the life of an integrated circuit chip by continuously running at a specific stress level until the chip fails.

[0078] In this embodiment, in step S4, a prediction model is established using a machine learning algorithm based on historical test data and monitoring data to predict performance changes and potential failures of integrated circuit chips under different conditions.

[0079] Specifically, the process of building a prediction model using machine learning algorithms includes:

[0080] S41: Data collection and preprocessing, including collecting a large amount of data from physical testing and environmental monitoring, including timestamps, environmental conditions, performance indicators, etc., and converting the raw data into a format suitable for model training. Data preprocessing includes Z-score normalization, the formula of which is:

[0081]

[0082] In the formula, x′ is the standardized data, x is the original data, μ is the sample mean, and σ is the sample standard deviation.

[0083] S42: Time series segmentation. For time series data, the data is segmented into input sequence and output sequence, using a window size of T.

[0084] S43: Model training, using machine learning algorithms, including LSTM and RNN, to train the collected data and build a prediction model to predict the performance change trend and potential failures of the chip under different conditions.

[0085] S44: Prediction and feedback, the model outputs prediction results to guide the adjustment of test strategies.

[0086] In this embodiment, in step S5, the established prediction model is used to perform intelligent diagnosis on the faults occurring during the test process and locate the source of the fault.

[0087] Specifically, this implementation automatically detects anomalies and locates the source of faults by establishing a fault database containing known fault features and combining deep learning technology to conduct in-depth analysis of monitoring data.

[0088] Furthermore, the process of establishing a fault database containing known fault characteristics includes:

[0089] Fault feature data collection: Collect fault samples from historical test data and monitoring data, including failure modes, environmental conditions, and operating status information, and organize the collected data into a structured format to form a database.

[0090] Feature extraction: Signal processing methods are used to extract key features, including time domain features and frequency domain features. Time domain features include mean, variance, peak and waveform; frequency domain features include Fourier transform and power spectral density.

[0091] The calculation formula when using the mean is:

[0092]

[0093] Where N is the number of samples, x i Represents the value of the i-th sample, and Mean is the mean value.

[0094] The calculation formula when using variance is:

[0095]

[0096] In the formula, Variance is the variance, N is the number of samples, Mean is the mean, and x i Represents the value of the i-th sample.

[0097] The calculation formula when using the peak value is:

[0098] Peak=max(x i );

[0099] Where Peak is the peak value, max(x i ) is the maximum value among the sample values.

[0100] Feature labeling: Label features based on fault samples.

[0101] Establish a fault feature library: organize the extracted features and corresponding fault labels into a database for subsequent comparison and identification.

[0102] Furthermore, the process of combining deep learning technology to deeply analyze monitoring data, automatically detect anomalies and locate fault sources includes:

[0103] Preprocess the monitoring data, including removing noise and outliers, and normalizing the data to make it suitable for the input requirements of the prediction model.

[0104] The monitoring data is input into the prediction model for training. The prediction model training formula is:

[0105]

[0106] Where L is the loss function, N is the number of samples, and yi is the actual output. is the prediction output of the prediction model.

[0107] Input the monitoring data into the trained prediction model and output the failure probability or failure type. The failure probability formula is:

[0108] P(fault)=σ(W·X+b);

[0109] Where P(fault) is the probability of fault occurrence, X is the input feature, W and b are the parameters of the prediction model, and σ is the activation function.

[0110] According to the output fault probability, fault comparison is performed in combination with the fault feature library to quickly identify the fault type and location.

[0111] The method for fault comparison is as follows:

[0112] The real-time monitoring features are matched with the features in the fault feature library, and the most likely fault type is determined using a similarity metric, where the similarity calculation formula is:

[0113]

[0114] Where A is the feature vector of real-time monitoring, B is the feature vector in the fault feature library, cosine_imilarity(A,B) represents the cosine similarity between vector A and vector B; ||A|| and ||B|| represent the modulus of vector A and vector B respectively.

[0115] In addition, the present invention also includes a feedback mechanism: feeding back the fault diagnosis results to the test strategy, adjusting the test conditions or adding specific test items to further evaluate the reliability of the chip.

[0116] In this embodiment, in step S6, the test process is continuously monitored through an online remote monitoring platform, and the test parameters and test strategies are dynamically adjusted according to the monitoring data and the prediction results of the prediction model.

[0117] Embodiment 2

[0118] like Figure 2 As shown, the present invention provides a reliability test and evaluation system for an integrated circuit chip, which is used to implement the reliability test and evaluation method for an integrated circuit chip of the first embodiment, and specifically includes:

[0119] A test target and indicator definition module 100 is used to define test targets and test indicators;

[0120] A multi-level test environment construction module 200 is used to construct a multi-level test environment, including constructing a physical laboratory, constructing an electromagnetic interference room, equipping a multi-axis vibration table, and constructing an online remote monitoring platform;

[0121] A dynamic stress test execution module 300 is used to perform dynamic stress tests in a constructed multi-level test environment, including random stress tests and fatigue life tests, to evaluate the stability and life of integrated circuit chips under various stress conditions;

[0122] A prediction model building module 400 is used to build a prediction model based on historical test data and monitoring data using a machine learning algorithm to predict performance changes and potential failures of integrated circuit chips under different conditions;

[0123] The fault intelligent diagnosis and location module 500 is used to use the established prediction model to intelligently diagnose the faults that occur during the test and locate the source of the faults;

[0124] The continuous monitoring and dynamic adjustment module 600 is used to continuously monitor the test process through an online remote monitoring platform, and dynamically adjust the test parameters and test strategies according to the monitoring data and the prediction results of the prediction model.

[0125] A reliability test and evaluation system for an integrated circuit chip in the present embodiment is used to implement the aforementioned reliability test and evaluation method for the integrated circuit chip. Therefore, the specific implementation method of the reliability test and evaluation system for the integrated circuit chip can be seen in the embodiment part of the reliability test and evaluation method for the integrated circuit chip mentioned above. For example, the test target and indicator definition module 100, the multi-level test environment construction module 200, the dynamic stress test execution module 300, the prediction model establishment module 400, the fault intelligent diagnosis and positioning module 500, and the continuous monitoring and dynamic adjustment module 600 are respectively used to implement steps S1, S2, S3, S4, S5, and S6 in the reliability test and evaluation method for the integrated circuit chip mentioned above. Therefore, its specific implementation method can refer to the description of the corresponding embodiments of each part. In order to avoid redundancy, it will not be repeated here.

[0126] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that include computer-usable program code.

[0127] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0128] These computer program instructions may also be stored in a computer readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture including an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 These computer program instructions can also be loaded onto a computer or other programmable data processing device, so that a series of operation steps are executed on the computer or other programmable device to produce a computer-implemented process, so that the instructions executed on the computer or other programmable device provide for implementing the process in the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0129] Obviously, the above embodiments are merely examples for clear explanation and are not intended to limit the implementation methods. For those skilled in the art, other different forms of changes or modifications can be made based on the above description. It is not necessary and impossible to list all the implementation methods here. The obvious changes or modifications derived from them are still within the protection scope of the invention.

Claims

1. A reliability test and evaluation method for an integrated circuit chip, characterized in that: include: S1: Define test objectives and test indicators; S2: Build a multi-level test environment, including building a physical laboratory, building an electromagnetic interference room, equipping a multi-axis vibration table, and building an online remote monitoring platform; S3: Perform dynamic stress tests in a constructed multi-level test environment, including random stress tests and fatigue life tests, to evaluate the stability and life of integrated circuit chips under various stress conditions; S4: Based on historical test data and monitoring data, a prediction model is established using machine learning algorithms to predict the performance changes and potential failures of integrated circuit chips under different conditions; S5: Use the established prediction model to intelligently diagnose faults that occur during the test and locate the source of the fault; S6: The test process is continuously monitored through the online remote monitoring platform, and the test parameters and test strategies are dynamically adjusted according to the monitoring data and the prediction results of the prediction model.

2. The reliability test and evaluation method of an integrated circuit chip according to claim 1, characterized in that: In step S1, the test goal is to evaluate the long-term stability and life of the integrated circuit chip under extreme temperature, high humidity, voltage fluctuation and electromagnetic interference conditions; the test indicators include failure rate, mean time between failures and performance degradation rate during the life cycle.

3. The reliability test and evaluation method of an integrated circuit chip according to claim 1, characterized in that: The construction of a physical laboratory, an electromagnetic interference room, a multi-axis vibration table, and an online remote monitoring platform specifically include: The physical laboratory is equipped with test chambers for different environmental conditions, including high / low temperature cycles, humidity control, and voltage / current fluctuation simulation; the electromagnetic interference chamber simulates electromagnetic environments of different intensities and frequencies; the multi-axis vibration table simulates the stability and life of integrated circuit chips under vibration; and the online remote monitoring platform uses IoT technology to collect real-time operating data, including temperature, power consumption, and operating frequency.

4. The reliability test and evaluation method of an integrated circuit chip according to claim 1, characterized in that: In step S3, the random stress test simulates uncertainties in the real world by randomly generating various stress conditions; the fatigue life test evaluates the life of the integrated circuit chip by continuously running at a specific stress level until the integrated circuit chip fails.

5. The reliability test and evaluation method of an integrated circuit chip according to claim 1, characterized in that: In step S4, the process of establishing a prediction model using a machine learning algorithm includes: S41: Data collection and preprocessing, including collecting a large amount of data from physical testing and environmental monitoring, and converting the raw data into a format suitable for model training, where data preprocessing includes Z-score standardization; S42: Time series segmentation, for time series data, the data is segmented into input sequence and output sequence, using a window size of T; S43: Model training, using machine learning algorithms, including LSTM and RNN, to train the collected data and build a prediction model to predict the performance trend and potential failures of the chip under different conditions; S44: Prediction and feedback, the model outputs prediction results to guide the adjustment of test strategies.

6. The reliability test and evaluation method of an integrated circuit chip according to claim 1, characterized in that: In step S5, the method of using the established prediction model to intelligently diagnose the faults occurring during the test and locate the source of the faults includes: Establish a fault database containing known fault characteristics, and use deep learning technology to conduct in-depth analysis of monitoring data to automatically detect anomalies and locate the source of faults.

7. The reliability test and evaluation method of an integrated circuit chip according to claim 6, characterized in that: The process of establishing a fault database containing known fault features includes: Fault feature data collection: Collect fault samples from historical test data and monitoring data, including fault modes, environmental conditions, and operating status information, and organize the collected data into a structured format to form a database; Feature extraction: Signal processing methods are used to extract key features, including time domain features and frequency domain features. Time domain features include mean, variance, peak value and waveform; frequency domain features include Fourier transform and power spectral density; Feature labeling: labeling features based on fault samples; Establish a fault feature library: organize the extracted features and corresponding fault labels into a database for subsequent comparison and identification.

8. The reliability test and evaluation method of an integrated circuit chip according to claim 6, characterized in that: The process of combining deep learning technology to deeply analyze monitoring data, automatically detect anomalies and locate fault sources includes: Preprocess the monitoring data, including removing noise and outliers, and normalizing the data to make it suitable for the input requirements of the prediction model; The monitoring data is input into the prediction model for training. The prediction model training formula is: Where L is the loss function, N is the number of samples, and yi is the actual output. is the prediction output of the prediction model; Input the monitoring data into the trained prediction model and output the failure probability or failure type. The failure probability formula is: P(fault)=σ(W·X+b); Where P(fault) is the probability of fault occurrence, X is the input feature, W and b are the parameters of the prediction model, and σ is the activation function; According to the output fault probability, fault comparison is performed in combination with the fault feature library to quickly identify the fault type and location.

9. The reliability test and evaluation method of an integrated circuit chip according to claim 8, characterized in that: The method for performing fault comparison is: The real-time monitoring features are matched with the features in the fault feature library, and the most likely fault type is determined using a similarity metric, where the similarity calculation formula is: Where A is the feature vector of real-time monitoring, B is the feature vector in the fault feature library, cosine_imilarity(A,B) represents the cosine similarity between vector A and vector B; ∥A∥ and ∥B∥ represent the modulus of vector A and vector B respectively.

10. A reliability test and evaluation system for an integrated circuit chip, characterized in that: The system is used to implement the reliability test and evaluation method of the integrated circuit chip according to any one of claims 1 to 9, specifically comprising: Test objective and indicator definition module, used to define test objectives and test indicators; Multi-level test environment construction module, used to build a multi-level test environment, including building a physical laboratory, building an electromagnetic interference room, equipping a multi-axis vibration table, and building an online remote monitoring platform; Dynamic stress test execution module, used to perform dynamic stress tests in a constructed multi-level test environment, including random stress tests and fatigue life tests, to evaluate the stability and life of integrated circuit chips under various stress conditions; A prediction model building module is used to build a prediction model based on historical test data and monitoring data using a machine learning algorithm to predict performance changes and potential failures of integrated circuit chips under different conditions; The fault intelligent diagnosis and location module is used to use the established prediction model to intelligently diagnose faults that occur during the test and locate the source of the fault; The continuous monitoring and dynamic adjustment module is used to continuously monitor the test process through an online remote monitoring platform, and dynamically adjust the test parameters and test strategies based on the monitoring data and the prediction results of the prediction model.

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