Semiconductor device test optimization method

Abstract

The invention proposes a semiconductor device test optimization method, and the method comprises the steps: initializing a test environment, obtaining the test data of a semiconductor, and automatically configuring the test environment; constructing a signal adjustment model to dynamically generate a preliminary test signal; designing a sensor network to monitor a preliminary test signal and response of the semiconductor device in real time, dynamically adjusting sensor data, and designing a data optimization acquisition strategy; dividing the parameters into a high priority and a low priority, dynamically compressing the parameters, implementing fast Fourier transform, extracting spectrum components as features, and analyzing the features; decompressing and preprocessing the compressed data, and performing deep fault analysis and positioning; and optimizing test data of the conductor, carrying out secondary test, and verifying a parameter adjustment result. According to the invention, the test quality and efficiency of the semiconductor device can be obviously improved, the product failure rate caused by insufficient test can be reduced, and the method plays an important role in semiconductor manufacturing industry, especially high-end chip production.

Description

Technical Field

[0001] The invention belongs to the field of semiconductor testing, and in particular relates to a semiconductor device testing optimization method. Background Art

[0002] Semiconductor devices play a core role in modern electronic devices, and their performance directly affects the reliability and efficiency of the entire system. With the rapid development of science and technology, the complexity of semiconductor devices is increasing, which puts higher demands on their testing technology. Traditional semiconductor testing methods usually rely on preset test programs and signals to evaluate the performance and reliability of devices. Although these methods performed well in the early stages of semiconductor development, with the diversification and complexity of integrated circuit functions, the limitations of traditional testing methods have gradually emerged. For example, fixed test signals and programs are often unable to adapt to all types of semiconductor devices. Especially when facing new or high-performance devices, this one-size-fits-all testing method may lead to incomplete test coverage, high false positive rate and low test efficiency.

[0003] In addition, traditional semiconductor test equipment usually has limited processing and analysis capabilities for test data and relies heavily on the operator's experience to make judgments, which not only increases the risk of human error, but also significantly increases the cost of testing. In practical applications, how to quickly and accurately identify and classify potential defects in semiconductor devices is the key to improving production efficiency and product reliability. Therefore, there is an urgent need for a new semiconductor testing method that can intelligently adapt to different testing requirements and improve test accuracy and efficiency. Summary of the invention

[0004] The purpose of the present invention is to design a semiconductor device test optimization method to overcome the limitations of traditional test methods in test signal generation, fault prediction, data processing and test parameter optimization.

[0005] In order to achieve the above object, the present invention provides a semiconductor device test optimization method, the method comprising:

[0006] Set environmental parameters, initialize the test environment, obtain semiconductor test data, perform pre-test analysis based on the test data, and automatically configure the test environment;

[0007] Building a signal adjustment model based on semiconductor test data to dynamically generate preliminary test signals;

[0008] Design sensor networks to monitor preliminary test signals and responses of semiconductor devices in real time, dynamically adjust sensor data acquisition parameters according to preset performance benchmarks, and design data optimization acquisition strategies to collect parameters;

[0009] Parameters are divided into high priority and low priority according to their importance and change characteristics, and the corresponding data compression algorithm is used to dynamically compress the parameters, and fast Fourier transform is performed on key signals to extract the main spectral components as features, and the features are analyzed;

[0010] Decompress and preprocess the compressed data, and then conduct in-depth fault analysis and location;

[0011] Optimize the test data of the conductor and then conduct a secondary test to further verify the parameter adjustment results.

[0012] Furthermore, the method further comprises the following steps:

[0013] Design physical models based on the working principles and physical characteristics of semiconductor devices to analyze test data and obtain evaluation data, verify model test results, generate test reports and optimize test parameters;

[0014] The physical model parameters are calibrated according to the evaluation data, and the optimization strategy is updated at the same time, and the algorithm model is updated using the online learning mechanism.

[0015] Furthermore, the environmental parameters include temperature, humidity and voltage.

[0016] Furthermore, the step of constructing a signal adjustment model according to semiconductor test data to dynamically generate a preliminary test signal specifically includes:

[0017] Select the most suitable basic signal form according to the response characteristics of the semiconductor device;

[0018] The basic parameters of the signal are set, including frequency f, amplitude A and phase φ, and a signal parameter fine adjustment model is constructed, which is expressed as follows:

[0019]

[0020]

[0021]

[0022] Where k represents the equivalent electrical stiffness, m represents the equivalent mass, E represents the energy level of the predetermined input, v0 and x0 represent the velocity and displacement under the initial conditions, respectively, which are used to calculate the phase;

[0023] Dynamically adjust the signal's detailed parameters and make real-time adjustments to the signal;

[0024] The final signal is output through a high-precision signal generator to test the selected device samples, and the signal is adjusted in real time based on the preliminary test feedback.

[0025] Furthermore, the data optimization acquisition strategy includes designing an adaptive data buffer to dynamically adjust storage according to the importance and specificity of the data.

[0026] Furthermore, an efficient data compression algorithm is applied to reduce storage requirements while maintaining data integrity, as shown below:

[0027]

[0028] Among them, CR represents the compression ratio, D orig Indicates the original data size, D comp Indicates the size of the compressed data.

[0029] Furthermore, the parameters are divided into high priority and low priority according to their importance and change characteristics, wherein high priority data adopts lossless compression technology and low priority data adopts lossy compression technology, low compression is applied to high priority data and high compression is applied to low priority data.

[0030] Furthermore, the method of dynamically compressing the parameters using a corresponding data compression algorithm comprises the following specific steps:

[0031] Differential encoding is used for data with strong time-varying properties, and the difference between consecutive data points is calculated for compression;

[0032] Frequency domain compression using discrete cosine transform for stationary or periodic data;

[0033] The adaptive parameter adjustment is designed to dynamically adjust the compression rate according to the data variability, as shown below:

[0034] CompressionFactor=β·VarD

[0035] Among them, β represents the adjustment coefficient and VarD represents the variability of data D.

[0036] Furthermore, the compressed data is decompressed and preprocessed, and then in-depth fault analysis and location are performed. The specific steps include the following:

[0037] Decompress the dynamically compressed parameters, normalize the time series data, and use wavelet transform to extract key features from the time series;

[0038] A diffusion model is designed to predict the dynamic development of faults as follows:

[0039] Assume that the distribution of fault states at time t is given by p t x represents, where x is a vector describing the device state, and the diffusion process is modeled by the following stochastic differential equation:

[0040]

[0041] where Dx,t is the state- and time-dependent diffusion coefficient, σx,t is the noise intensity, and dW t represents the Wiener process; the diffusion coefficient and noise intensity that are both state- and time-dependent are estimated based on historical fault data, once the model parameters are determined, to simulate future fault diffusion paths using numerical methods;

[0042] The self-attention mechanism is introduced to process all positions in the input sequence at the same time and calculate the dependencies between positions, which can be expressed as follows:

[0043]

[0044] Among them, Q represents query, K represents key, V represents value, AttentionQ, K, V are linear transformations of input data, d k is the dimension of the key vector; the attention weights output by the transformer model are used to identify the key time points related to the fault, and then the model predicts the fault state of the device by learning the complex relationship in the input sequence, which is expressed as follows:

[0045] MultiHeadQ, K, V = Concathead1, head2,..., head h W O

[0046]

[0047] in, and W O is a learnable weight matrix.

[0048] Furthermore, the test data of the optimized conductor includes the following specific steps:

[0049] Design multi-agent learning, configure an agent for each test parameter, define the environment as a semiconductor test platform, and the reward mechanism is expressed as follows:

[0050] R=α·Coverage-β·Time-γ·Energy

[0051] Among them, Coverage represents the coverage of fault detection, Time represents the test time, Energy represents the energy consumed during the test, and α, β, and γ represent the coefficients for weighing various aspects respectively;

[0052] Using a performance-based weighted integration method, the decision outputs of each agent are integrated to determine the optimal test parameters. The weight ω i Determined by the following expression:

[0053]

[0054] Among them, Performance i is the performance of agent i, Performance j is the performance of agent j, κ is the adjustment coefficient used to amplify or reduce the impact of performance differences;

[0055] According to the decision dynamic weight ω output by ensemble learning i Adjust test parameters.

[0056] The beneficial technical effects of the present invention are at least as follows:

[0057] (1) The present invention uses a deep learning algorithm to dynamically generate and adjust test signals based on device feedback. In this way, different semiconductor devices can receive signals that best suit their characteristics during the test process, thereby effectively improving the coverage and accuracy of the test. This solves the problem that fixed test signals in traditional methods cannot adapt to new or complex devices.

[0058] (2) The present invention uses machine learning techniques, such as support vector machines (SVM) and random forests, to analyze test data of semiconductor devices and automatically predict and locate potential faults. This method can significantly reduce reliance on manual experience, reduce human errors, and improve the speed and accuracy of fault detection.

[0059] (3) The present invention utilizes advanced data compression technologies such as automatic encoders to process and compress the large amount of data generated during the test in real time, thereby ensuring the high efficiency and real-time performance of data processing. This not only improves the speed of data processing, but also reduces the burden on storage and processing equipment.

[0060] (4) Combined with reinforcement learning algorithms, such as Q-learning, the subsequent test parameters and strategies are adjusted according to the previous test results to ensure continuous optimization of the test process. This method ensures the adaptability and flexibility of the test process, enabling the system to continuously learn and optimize its own test strategies, effectively improving the efficiency and accuracy of the entire test process. BRIEF DESCRIPTION OF THE DRAWINGS

[0061] The present invention is further described using the accompanying drawings, but the embodiments in the accompanying drawings do not constitute any limitation to the present invention. A person skilled in the art can obtain other drawings based on the following drawings without creative work.

[0062] Figure 1 The present invention is a flow chart of a semiconductor device testing optimization method. DETAILED DESCRIPTION

[0063] Embodiments of the present invention are described in detail below, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, and cannot be understood as limiting the present invention.

[0064] In response to the above technical problems, the overall idea of ​​the technical solution provided by this application is as follows:

[0065] In one or more embodiments, Figure 1 As shown, a semiconductor device test optimization method of the present invention is disclosed, and the method comprises:

[0066] S100, setting environmental parameters, initializing the test environment, acquiring semiconductor test data, performing pre-test analysis based on the test data, and automatically configuring the test environment.

[0067] Specifically, the environmental parameters include temperature setting, humidity control and voltage range setting. The temperature setting determines the optimal test temperature based on the device specification and previous test experience. For example, if the device shows the best electrical performance at 25°C, the test chamber temperature is set constant at this value. Temperature has a significant impact on the performance of semiconductor devices, so a constant environment needs to be maintained through a precise temperature control system. set =25℃, T set Indicates the set laboratory environment temperature. The humidity control sets the relative humidity to 50% to prevent high humidity environment from causing potential damage to semiconductor materials, especially for those high-frequency devices that are sensitive to humidity. Use a dehumidifier and a humidifier to regulate the test environment humidity. set =50% where H set Indicates the relative humidity of the laboratory. The voltage range setting is based on the maximum withstand voltage and working voltage of the device, and the supply voltage is set. For example, for standard CMOS technology, the supply voltage is often set to 3.3V. Use a regulated power supply to ensure voltage stability. V set =3.3V, where V set Indicates the set supply voltage.

[0068] Perform physical analysis based on device characteristics, as follows:

[0069] Current-Voltage (IV) Characteristic Curve Plotting: Measuring the current response of a device at different voltages to create its IV characteristic curve. This curve is critical to understanding the behavior of a device under different operating conditions. Where I is the current, V is the applied voltage, I0 is the saturation current, q is the electron charge, n is the ideality factor, k is the Boltzmann constant, and T is the absolute temperature;

[0070] Thermal stability test: Test the device through heating and cooling cycles, record the response of device performance to temperature changes, and ensure that the device works stably within the predetermined operating temperature range;

[0071] Environmental simulation algorithm: Develop an environmental simulation system based on physical and chemical models that can simulate the behavior of different devices under various environmental conditions based on their physical properties and test data, and predict their performance under different environmental settings. This model helps the test team understand how specific environmental variables affect device performance and optimize environmental settings accordingly.

[0072] P device =f(T, H, V; θ), (1)

[0073] Where, θ represents the device physical parameter;

[0074] Real-time data feedback and model adjustment: Integrate real-time data acquisition system to feedback the impact of environment on device performance. This data is used to continuously update the environmental simulation algorithm to improve its prediction accuracy and adaptability.

[0075] Furthermore, a test plan is developed for high-frequency devices, especially for those with high frequencies. A network analyzer is used to measure the response of the device at different frequencies to identify its frequency characteristics and possible resonance points. test =k f ·f max where f test is the test frequency, f max is the maximum operating frequency of the device, and kf is the safety factor, which is usually taken as 0.8. By continuously running the test set cycle, the performance stability of the device under long-term operation is evaluated, especially for those applications that require high reliability.

[0076] S200 , constructing a signal adjustment model according to semiconductor test data to dynamically generate a preliminary test signal.

[0077] Specifically, according to the response characteristics of the device (such as frequency response, load capacity, etc.), select the most suitable basic signal form. For example, for high-speed digital circuits, select square wave signals to simulate the fast switching characteristics of digital signals; for analog signal processing devices, select sine waves to test their performance under continuous signals. Set the basic parameters of the signal, such as frequency f, amplitude A and phase φ, based on the typical operating conditions and safe operating range of the device. For example, if an amplifier has an operating bandwidth of 1GHz, set the test signal frequency to 500MHz to detect its performance.

[0078] A parameter adjustment model is established to refine the frequency, amplitude, and phase of the signal. This model will be adjusted based on the following physical formulas and combined with the characteristics of the actual device:

[0079]

[0080] Where k represents the equivalent electrical stiffness obtained from step 1, m represents the equivalent mass, which is related to the capacitive load of the device, E represents the energy level of the predetermined input, and v0 and x0 represent the velocity and displacement under the initial conditions, respectively, which are used to calculate the phase.

[0081] Furthermore, dynamic modulation technology is used to adjust the detailed parameters of the signal (such as modulation depth and modulation rate) to adapt to the nonlinear characteristics of the device and the complex working environment. This process adjusts the signal through real-time feedback to ensure the effectiveness and safety of the test. Based on the preliminary test feedback, the signal is adjusted in real time. For example, if it is observed that the device responds too much at a certain frequency, the signal amplitude of that frequency is reduced or the phase is adjusted to avoid potential damage. The final signal is output through a high-precision signal generator to test the selected device samples.

[0082] Furthermore, a high-speed sampling oscilloscope and spectrum analyzer are used to record and analyze the output signals and device responses in detail to ensure that the output of each signal meets the design expectations and to verify whether the actual performance of the device is consistent with the theoretical model.

[0083] S300, design a sensor network to monitor the preliminary test signals and responses of semiconductor devices in real time, dynamically adjust sensor data acquisition parameters according to preset performance benchmarks, and design a data optimization acquisition strategy to collect parameters.

[0084] Specifically, a sensor network is used to monitor the test signal and device response in real time. Sensors include voltage, current, temperature and other measurement devices to ensure that all key performance indicators of the device under the test signal are fully captured. Data acquisition parameters such as acquisition frequency and resolution are dynamically adjusted according to pre-defined performance benchmarks. This adjustment is based on the following algorithm: sample =min(f max ,α·BW) where f sample is the sampling frequency, f max is the maximum sampling frequency allowed by the sensor, BW is the bandwidth of the device under test, and a is the oversampling factor, which is usually set to 1.5 to ensure adequate sampling.

[0085] Then design an adaptive data buffer to dynamically adjust storage according to the importance and specificity of the data. Automatically increase data collection density for critical moments (such as signal jumps, abnormal responses, etc.) to ensure that data of important events are not missed. Apply efficient data compression algorithms to reduce storage requirements while maintaining data integrity. Use the following compression ratio formula: Where CR is the compression ratio, Dorig is the original data size, D comp is the size of the compressed data.

[0086] Furthermore, perform preliminary quality control before data storage to remove obvious noise and irrelevant data. Use simple statistical analysis methods (such as standard deviation and mean filtering) to identify and process abnormal data points. Perform preliminary data analysis, such as fast Fourier transform (FFT) of the signal or simple machine learning models, to extract key features for subsequent analysis.

[0087] Based on the preliminary test results, the test setup and data acquisition strategy are adjusted to optimize the entire test process. The feedback mechanism optimizes the signal parameters and data acquisition strategy by analyzing the deviation of the device response from the expected value, ensuring continuous optimization of data acquisition.

[0088] S400, classifying the parameters into high priority and low priority according to their importance and change characteristics, dynamically compressing the parameters using corresponding data compression algorithms, performing fast Fourier transform on key signals, extracting main spectral components as features, and analyzing the features.

[0089] Specifically, according to the importance and change characteristics of the data, the data stream is divided into two categories: high priority (such as equipment extreme condition response) and low priority (such as steady-state operation data). A lower compression ratio is applied to high-priority data to retain details, and a high compression ratio is used for low-priority data to save storage space. High-priority data uses lossless compression technology, such as LZ77 or Huffman coding; low-priority data uses lossy compression technology, such as JPEG2000 or MPEG-4AVC.

[0090] Further, a dynamic compression algorithm is implemented, specifically including the following steps:

[0091] Differential coding is used for data with strong time-varying properties, and the difference between consecutive data points is calculated for compression to reduce data redundancy;

[0092] Use discrete cosine transform (DCT) to perform frequency domain compression on stable or periodic data, effectively compressing steady-state background data;

[0093] Design adaptive adjustment parameters:

[0094] Compression Factor=β·Var(D) (3)

[0095] Among them, β is the adjustment coefficient, Var(D) represents the variability of data D, and this formula is used to dynamically adjust the compression rate according to the variability of data.

[0096] Furthermore, real-time data flow monitoring is implemented to detect changes in data types and traffic, and automatically adjust compression strategies and parameters. Compression parameters are adjusted based on quality feedback after data compression to ensure that data quality is not lower than the preset threshold. Fast Fourier transform (FFT) is implemented on key signals to extract the main spectral components as features to support subsequent advanced data analysis. Basic statistical properties of data, such as mean, standard deviation, and skewness, are calculated to describe the basic behavior of the data. When detailed analysis is required, the data is reconstructed using inverse transform to ensure the integrity and availability of the decompressed data.

[0097] Specifically, the compressed data is decompressed and verified regularly to check whether errors are introduced during the compression process, thus ensuring the integrity of the data and the accuracy of the analysis.

[0098] This step not only achieves efficient compression and intelligent processing of test data, but also ensures data integrity and analysis effectiveness through real-time monitoring and adaptive technology. This solution is particularly suitable for dealing with the challenges of large amounts of data and high complexity in semiconductor testing, effectively supporting the entire process from data collection to analysis, and improving test data management efficiency and analysis accuracy.

[0099] S500: decompress and preprocess the compressed data, and then perform in-depth fault analysis and location.

[0100] Specifically, the following steps are included:

[0101] S5001. Decompress the compressed data obtained from step S400 and normalize the time series data to eliminate the influence of different devices and test environments. Use wavelet transform to extract key features from the time series, such as energy band and instantaneous phase information, to provide input data for the deep learning model.

[0102] S5002. Design a diffusion model. A diffusion model is usually used to simulate how a system diffuses from one state to another, and is particularly suitable for describing the development of a fault in a semiconductor device. In this solution, we use a probabilistic diffusion model to predict the dynamic development of a fault.

[0103] As an embodiment of the present invention, it is assumed that the distribution of the fault state at time t is represented by p t (x), where x is a vector describing the device state (including voltage, current, etc.). The diffusion process can be modeled by the following stochastic differential equation (SDE):

[0104]

[0105] where Dx,t is the state- and time-dependent diffusion coefficient, σx,t is the noise intensity, and dW t It is a Wiener process.

[0106] Furthermore, in practical applications, D and σ can be estimated from historical fault data. This usually involves maximum likelihood estimation or Bayesian methods, using fault data to fit the above SDE model. Once the model parameters are determined, numerical methods (such as the Euler-Maruyama method) can be used to simulate future fault diffusion paths to provide a basis for predicting fault development.

[0107] S5003, integrated converter model.

[0108] Specifically, the Transformer model uses its self-attention mechanism to process time series data, which can effectively capture long-term dependencies and complex temporal dynamics. The core of the Transformer model is the self-attention mechanism, which allows the model to process all positions in the input sequence simultaneously and calculate the dependencies between positions:

[0109]

[0110] Among them, Q (query), K (key), V (value) are linear transformations of the input data, d k is the dimension of the key vector.

[0111] Furthermore, the attention weights output by the transformer model can be used to identify key time points associated with the fault. By analyzing the distribution of attention weights, the moment and location of the fault can be accurately located. The model predicts the fault state of the device by learning complex relationships in the input sequence (such as features obtained from wavelet transform). This usually involves multi-head attention and position encoding to handle dependencies at different time scales:

[0112]

[0113] in, and W O is a learnable weight matrix.

[0114] By combining these two models, a powerful framework can be provided to dynamically monitor and predict the development of semiconductor device failures, improving the timeliness and accuracy of fault handling. This approach not only enhances the ability to interpret test data, but also improves the accuracy of predicting future fault behavior.

[0115] S5004. Integrate the prediction results of the diffusion model and the converter model, and use algorithms such as weighted average or voting system to optimize the final fault prediction results. This integrated approach improves the accuracy and robustness of the prediction, especially in the face of data noise and test condition changes.

[0116] S5005. Use advanced visualization tools, such as heat maps and dynamic charts, to display the dynamic process of fault development and the prediction results of the model. This helps engineers and researchers understand the nature and impact of faults more intuitively, and provides data support for formulating maintenance strategies and improving designs.

[0117] S600, optimizing the test data of the conductor, and then performing a secondary test to further verify the parameter adjustment result.

[0118] Specifically, the following steps are included:

[0119] S6001. Multi-agent reinforcement learning design.

[0120] Specifically, an agent is configured for each test parameter, for example, the frequency agent adjusts the frequency f, the amplitude agent adjusts the amplitude A, and the duration agent adjusts the pulse width τ. These agents operate independently during the test process, but their common goal is to optimize the overall test performance.

[0121] Design environment and reward mechanism, define the environment as semiconductor test platform, and the reward mechanism is as follows:

[0122] R=α·Coverage-β·Time-γ·Energy (7)

[0123] Among them, Coverage is the coverage of fault detection, Time is the test time, Energy is the energy consumed in the test process, and α, β, and γ are coefficients for weighing various aspects.

[0124] S6002. Application of integrated learning strategies.

[0125] Specifically, a performance-weighted ensemble method is used to combine the decision outputs of each agent to determine the optimal test parameters. The weights are determined by the following expression:

[0126]

[0127] Among them, Performance i is the performance of agent i, and κ is an adjustment factor used to amplify or reduce the impact of performance differences.

[0128] S6003. Parameter optimization and secondary testing.

[0129] Specifically, the test parameters, such as the frequency f, amplitude A, and pulse width τ of the signal, are dynamically adjusted according to the decision output by the ensemble learning. Data is collected after each test to evaluate the test effect and fed back to each agent to adjust its strategy.

[0130] After the initial test, a secondary test is performed based on the optimized parameters to further verify the effect of the parameter adjustment, and the iterative optimization is continued until the preset test goals are achieved or the performance is stable.

[0131] Furthermore, the present invention also includes steps S700-S800.

[0132] S700, design a physical model based on the working principle and physical characteristics of the semiconductor device to analyze the test data to obtain evaluation data, verify the model test results, generate a test report and optimize the test parameters.

[0133] Specifically, the following steps are included:

[0134] S7001. Establish a physical basic model.

[0135] Specifically, based on the working principle and physical characteristics of semiconductor devices, a basic physical model is established to describe the electrical behavior of the device. For example, for a transistor, the following transfer characteristic equation is used to describe its behavior under different test conditions:

[0136]

[0137] Among them, I D is the leakage current, μ is the electron mobility, C ox is the oxide capacitance, W and L are the width and length of the transistor respectively, V GS is the gate-source voltage, V th is the threshold voltage, λ is the channel length modulation parameter, V DS is the drain-source voltage.

[0138] S7002. Data matching and model verification.

[0139] Specifically, the electrical parameter data obtained from the test is matched with the physical model. The least squares method or other statistical fitting techniques are used to evaluate the fit between the model and the actual data to verify the accuracy and applicability of the model.

[0140]

[0141] Among them, y obs is the observed data, y model is the model prediction data, σ yobs is the standard error of the data.

[0142] S7003. Performance evaluation.

[0143] Furthermore, key performance parameters such as gain, frequency response, power margin, etc. are calculated based on the physical model and data fitting results. The calculation formulas for these parameters directly depend on the specific expressions of the physical model. Any abnormal or expected behavior that occurs during the test is identified and compared with the physical model prediction to find possible causes and solutions.

[0144] S7004. Feedback and optimization suggestions.

[0145] Specifically, generate a detailed test report, including the results of model validation, performance evaluation, and failure analysis. The report should detail the key findings and recommended improvements during the test. Based on the test results and feedback from the physical model, make recommendations for optimizing the test settings, such as adjusting the input voltage or changing the frequency of the test signal, in order to achieve better performance and higher reliability in future tests.

[0146] S800, calibrating physical model parameters according to the evaluation data of step S700, updating the optimization strategy, and updating the algorithm model using an online learning mechanism.

[0147] Specifically, the following steps are included:

[0148] S8001. Calibration and updating of physical models.

[0149] Specifically, the comprehensive result evaluation data obtained from step S700 is used to calibrate the physical model parameters. This involves adjusting the electrical parameters in the model, such as the electron mobility μ, the threshold voltage V th , to ensure that the model is consistent with the actual test data. The parameter update can be expressed as:

[0150] θ new =θ old +η·θ exp -θ old (11)

[0151] Among them, θ new and θ old are the parameters before and after the update, θ exp is a parameter derived from experimental data, and η is the learning rate.

[0152] S8002, based on the latest model parameters and previous test results, redefine the parameters such as the shape, frequency and amplitude of the test signal. This step is achieved through the objective function of minimizing the test error and maximizing the equipment performance:

[0153]

[0154] Among them, L is the objective function, Discrepancy represents the deviation between the test result and the expected result, Cost represents the test cost, and λ is the balance factor.

[0155] S8003. Compare the test results with the expected targets and adjust the test parameters and models according to the deviation. Implement a feedback control loop to ensure that each iteration is optimized based on the latest experimental data and model predictions. Use statistical analysis tools and failure analysis reports to guide the adjustment of test strategies and ensure the adaptability and flexibility of the test system.

[0156] S8004. Regularly evaluate the performance of the test system and monitor key performance indicators such as fault detection rate, test time and cost. Use these indicators to measure the effectiveness of the iteration process and provide improvement directions for future test cycles. Continuously update the operation manual and system documentation, record the learning points and improvement measures of each iteration to promote knowledge accumulation and information sharing across teams

[0157] In summary, the present invention intelligently configures the test environment by analyzing historical data and device characteristics, which ensures the pertinence and effectiveness of subsequent tests. This step can dynamically adjust the test conditions, provide the most suitable test environment for different types of semiconductor devices, and solve the one-size-fits-all problem in traditional test methods. Use deep learning technology to generate customized test signals for specific devices. Such signals more accurately match the characteristics of the device, improve the adaptability and coverage of the test signal, and thus improve the effectiveness of the test. Automation and accuracy directly affect the quality and efficiency of subsequent data analysis. The large amount of data collected is processed by advanced data compression technology, which not only improves the processing speed, but also reduces the storage requirements. The automatic encoder helps to extract key features and lays the foundation for in-depth analysis. Fault prediction and precise positioning are performed through machine learning algorithms. This step effectively solves the inaccuracy and time-consuming problems of fault diagnosis in traditional methods, and significantly improves the accuracy and efficiency of fault detection.

[0158] Adjust the test parameters for a more targeted secondary test. Reinforcement learning algorithms come into play here to optimize the test process and ensure that each round of testing is based on the best results of the previous round. Comprehensive result evaluation and feedback integrates all test data and analysis results, uses expert systems to evaluate and interpret test results, and provides clear test reports. This step provides a comprehensive evaluation of the overall test effect and supports the final quality control decision. Continuous learning and system iteration ensure that the system continues to learn and optimize its performance based on new test data. This online learning and adaptive update ensures the long-term effectiveness of the system and its ability to adapt to new challenges.

[0159] The above disclosures are only some preferred embodiments of the present invention, which certainly cannot be used to limit the scope of rights of the present invention. Ordinary technicians in this field can understand that all or part of the processes of the above embodiments and equivalent changes made according to the claims of the present invention are still within the scope of the invention.

Claims

1. A semiconductor device test optimization method, characterized in that: The method comprises the following steps: Set environmental parameters, initialize the test environment, obtain semiconductor test data, perform pre-test analysis based on the test data, and automatically configure the test environment; A signal adjustment model is constructed based on semiconductor test data to dynamically generate preliminary test signals; Design sensor networks to monitor preliminary test signals and responses of semiconductor devices in real time, dynamically adjust sensor data acquisition parameters according to preset performance benchmarks, and design data optimization acquisition strategies to collect parameters; Parameters are divided into high priority and low priority according to their importance and change characteristics, and the corresponding data compression algorithm is used to dynamically compress the parameters, and fast Fourier transform is performed on key signals to extract the main spectral components as features, and the features are analyzed; Decompress and preprocess the compressed data, and then conduct in-depth fault analysis and location; Optimize the test data of the conductor and then conduct a secondary test to further verify the parameter adjustment results.

2. A semiconductor device test optimization method according to claim 1, characterized in that: The method further comprises the following steps: Design physical models based on the working principles and physical characteristics of semiconductor devices to analyze test data and obtain evaluation data, verify model test results, generate test reports and optimize test parameters; The physical model parameters are calibrated according to the evaluation data, and the optimization strategy is updated at the same time, and the algorithm model is updated using the online learning mechanism.

3. A semiconductor device test optimization method according to claim 1, characterized in that: The environmental parameters include temperature, humidity and voltage.

4. A semiconductor device test optimization method according to claim 3, characterized in that: The step of constructing a signal adjustment model according to semiconductor test data to dynamically generate a preliminary test signal specifically includes: Select the most suitable basic signal form according to the response characteristics of the semiconductor device; The basic parameters of the signal are set, including frequency f, amplitude A and phase φ, and a signal parameter fine adjustment model is constructed, which is expressed as follows: Where k represents the equivalent electrical stiffness, m represents the equivalent mass, E represents the energy level of the predetermined input, v0 and x0 represent the velocity and displacement under the initial conditions, respectively, which are used to calculate the phase; Dynamically adjust the signal's detailed parameters and make real-time adjustments to the signal; The final signal is output through a high-precision signal generator to test the selected device samples, and the signal is adjusted in real time based on the preliminary test feedback.

5. A semiconductor device test optimization method according to claim 1, characterized in that: The data optimization acquisition strategy includes designing an adaptive data buffer to dynamically adjust storage according to the importance and specificity of the data.

6. A semiconductor device test optimization method according to claim 5, characterized in that: At the same time, an efficient data compression algorithm is applied to reduce storage requirements while maintaining data integrity, as shown below: Among them, CR represents the compression ratio, D orig Indicates the original data size, D comp Indicates the size of the compressed data.

7. A semiconductor device test optimization method according to claim 1, characterized in that: Parameters are divided into high priority and low priority according to their importance and change characteristics. High priority data adopts lossless compression technology, and low priority data adopts lossy compression technology. Low compression is applied to high priority data, and high compression is applied to low priority data.

8. A semiconductor device test optimization method according to claim 7, characterized in that: The method of dynamically compressing the parameters using the corresponding data compression algorithm comprises the following specific steps: Differential encoding is used for data with strong time-varying properties, and the difference between consecutive data points is calculated for compression; Frequency domain compression using discrete cosine transform for stationary or periodic data; Adaptive parameter adjustment is designed to dynamically adjust the compression rate according to data variability, as shown below: Compression Factor=β·Var(D) Among them, β represents the adjustment coefficient, and Var(D) represents the variability of data D.

9. A semiconductor device test optimization method according to claim 1, characterized in that: The compressed data is decompressed and preprocessed, and then in-depth fault analysis and location are performed. The specific steps include the following: Decompress the dynamically compressed parameters, normalize the time series data, and use wavelet transform to extract key features from the time series; A diffusion model is designed to predict the dynamic development of faults as follows: Assume that the distribution of fault states at time t is given by p t (x) where x is a vector describing the device state. The diffusion process is modeled by the following stochastic differential equation: Where D9x,t) represents the diffusion coefficient that depends on the state and time, σ(x,t) represents the noise intensity, and dWt represents the Wiener process; the diffusion coefficient and noise intensity that depend on the state and time are estimated based on the historical fault data. Once the model parameters are determined, numerical methods are used to simulate the future fault diffusion path; The self-attention mechanism is introduced to process all positions in the input sequence at the same time and calculate the dependencies between positions, which can be expressed as follows: Where Q represents query, K represents key, V represents value, Attention(Q,K,V) is a linear transformation of input data, d k is the dimension of the key vector; the attention weights output by the transformer model are used to identify the key time points related to the fault, and then the model predicts the fault state of the device by learning the complex relationship in the input sequence, which is expressed as follows: in, and W O is a learnable weight matrix.

10. A semiconductor device test optimization method according to claim 1, characterized in that: The test data of the optimized conductor comprises the following specific steps: Design multi-agent learning, configure an agent for each test parameter, define the environment as a semiconductor test platform, and the reward mechanism is expressed as follows: R=α·Coverage-β·Time-γ·Energy Among them, Coverage represents the coverage of fault detection, Time represents the test time, Energy represents the energy consumed during the test, and α, β, and γ represent the coefficients for weighing various aspects respectively; Using a performance-based weighted integration method, the decision outputs of each agent are integrated to determine the optimal test parameters. The weight ωi is determined by the following expression: Among them, Performancei is the performance of agent i, Performance j is the performance of agent j, κ is the adjustment coefficient used to amplify or reduce the impact of performance differences; The test parameters are adjusted according to the decision dynamic weight ωi output by the ensemble learning.

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