An automated screening method for DDR5 memory chips

Abstract

The present invention relates to the field of semiconductor screening technology, and discloses an automated screening method for DDR5 memory chips, comprising the following steps: S1, data acquisition: collecting test data of the frequency, voltage, timing, and power consumption of each batch of DDR5 memory chips through an automated test platform; S2, chip characteristic modeling: establishing a characteristic model for each batch of chips according to the collected test data; S3, multi-objective optimization: constructing multiple objective functions based on the test data, and performing multi-objective optimization on the frequency error, voltage error, timing error, and power consumption to obtain the Pareto optimal solution; S4, automatic parameter adjustment. Through multi-objective optimization, non-linear modeling, Bayesian inference, and fuzzy control, the present invention realizes the precision and automation of the chip screening process, significantly improves the screening accuracy and efficiency, and ensures the performance consistency and stability of the chips under high-load and special application environments.

Description

Technical Field

[0001] The present invention relates to the technical field of semiconductor screening, and specifically to an automated screening method for DDR5 memory chips. Background Art

[0002] With the continuous development of technology, memory chips are increasingly widely used in various electronic devices, especially in the fields of high-performance computing and data processing. As an important development direction of current memory technology, DDR5 memory chips are widely used in high-performance devices such as computers and servers. Due to the increasing requirements for performance in terms of frequency, power consumption, timing, etc. of DDR5 memory chips, higher requirements are also put forward for the accuracy of chip screening.

[0003] In the prior art, most chip screening methods adopt standard test parameters, mainly by detecting performance indicators such as the power consumption, frequency, and timing of the chips to evaluate whether they meet the basic working requirements. Through automated test equipment, the performance parameters of the chips can be preliminarily screened more efficiently. In the prior art, this screening method based on a single indicator or a small number of indicators has certain advantages: First, it is easy to operate, can quickly screen a large number of chips, and can effectively detect chips with poor performance, preventing unqualified chips from entering the market; Second, the existing automated test equipment can significantly reduce manual operations and improve production efficiency; Finally, some methods in the chip screening process can ensure that parameters such as the frequency and voltage of the chips operate stably within a certain range, ensuring the basic qualification of performance.

[0004] In the prior art, most screening methods rely on standard parameters set manually and cannot be flexibly adjusted and optimized according to the characteristics of each batch of chips, which results in less than ideal screening effects and performance consistency in high-load or special application environments; Second, the screening methods of the prior art often rely on only a few performance indicators and cannot fully consider the comprehensive performance of the chips under different working conditions; Finally, although automated testing has improved the screening efficiency, in applications with high performance and high timing requirements, the existing methods often have difficulty achieving fine performance adjustment. Summary of the Invention

[0005] Aiming at the deficiencies of the prior art, the present invention provides an automated screening method for DDR5 memory chips, which solves the problems in the prior art of being unable to flexibly adapt to different chip characteristics, insufficient performance adjustment accuracy, and inconsistent screening effects.

[0006] To achieve the above objectives, the present invention is realized through the following technical solutions: An automated screening method for DDR5 memory chips, comprising the following steps:

[0007] S1. Data collection: Collect the test data of frequency, voltage, timing, and power consumption of each batch of DDR5 memory chips through an automated test platform;

[0008] S2. Chip characteristic modeling: Establish the characteristic model of each batch of chips based on the collected test data;

[0009] S3. Multi-objective optimization: Construct multiple objective functions according to the test data, perform multi-objective optimization on frequency error, voltage error, timing error, and power consumption, and obtain the Pareto optimal solution;

[0010] S4. Automatic parameter adjustment: Automatically adjust the test parameters based on the Pareto optimal solution, including frequency, voltage, timing, and error;

[0011] S5. Nonlinear modeling and Bayesian inference: Use a nonlinear regression model to establish the nonlinear relationship between chip performance and test parameters, and update the posterior distribution of test parameters in combination with the Bayesian inference method;

[0012] S6. Fuzzy logic control and adaptive adjustment: Use fuzzy control rules to dynamically adjust the test parameters according to real-time test data to ensure the optimal state of the test process;

[0013] S7. Chip classification and BIN assignment: Classify the chips according to the test results, divide the chips into multiple BIN groups according to their performance indicators, and output the test parameters of each BIN group.

[0014] Preferably, the data collection includes:

[0015] Real-time collect the test parameters of the chip's frequency, voltage, timing, and power consumption;

[0016] Transmit the collected test data to the control system for subsequent analysis and optimization.

[0017] Preferably, the chip characteristic modeling includes:

[0018] Use a nonlinear regression method to model the relationship between the chip's performance and test parameters;

[0019] Perform performance modeling on each batch of chips and dynamically adjust according to actual data.

[0020] Preferably, the multi-objective optimization includes:

[0021] Define frequency error, voltage error, timing error, and power consumption as multiple objective functions;

[0022] Set the weights of each objective function according to actual needs, and use genetic algorithms and particle swarm optimization to search for the Pareto optimal solution to ensure the balance between all objectives.

[0023] Preferably, the automatic parameter adjustment includes:

[0024] Automatically updating test parameters based on Pareto optimal solutions, including frequency, voltage, and timing;

[0025] During each round of testing, dynamically update test parameters according to real-time data and test errors to ensure that each test parameter adjustment meets the target chip performance standard and optimize the real-time feedback mechanism during testing.

[0026] Preferably, the non-linear modeling and Bayesian inference include:

[0027] Use support vector regression method and decision tree regression method to model the non-linear relationship between chip performance and test parameters;

[0028] Use Bayesian inference method to update the posterior distribution of parameters by combining historical data and real-time data.

[0029] Preferably, the fuzzy logic control and adaptive adjustment include:

[0030] Fuzzify the test data of the chip and convert the input data of frequency, voltage, timing, and power consumption into fuzzy sets;

[0031] Based on the fuzzy control rules in the rule base, infer the test data and calculate the direction and amplitude of the test parameter adjustment;

[0032] Defuzzify the inference result to obtain the adjusted test parameters;

[0033] Through the automatic adjustment mechanism, adjust the test parameters according to the results of each round of testing to optimize the test performance of the chip.

[0034] Preferably, the chip classification and BIN assignment include:

[0035] Classify the chips based on the test results;

[0036] Divide the chips into multiple BIN groups according to performance indicators and output the test parameters of each BIN group.

[0037] Preferably, the automatic adjustment mechanism adjusts the test parameters through real-time feedback. If the test result of a certain chip deviates from the preset standard, the system automatically fine-tunes its test parameters to ensure that the chip undergoes the next round of testing under optimal conditions.

[0038] Preferably, the method is implemented through a computer system, and the computer system includes:

[0039] A data acquisition module, a feature modeling module, an optimization algorithm module, a Bayesian inference module, a fuzzy control module, and a classification module;

[0040] The computer system performs automated optimization and screening based on the real-time test data of the chip, and outputs the final chip classification and BIN assignment results.

[0041] The present invention provides an automated screening method for DDR5 memory chips. It has the following beneficial effects:

[0042] 1. The present invention adopts a method combining multi-objective optimization and fuzzy control to automatically adjust the working parameters of the chip in real time, ensuring that the chip performance always remains in the best state. Compared with traditional methods, it eliminates manual intervention, improves the accuracy and efficiency of testing, and can flexibly respond especially in high-frequency and high-load situations.

[0043] 2. The present invention accurately classifies the chips through multi-dimensional analysis and assigns the chips to different BIN groups. This assignment method not only dynamically adjusts according to the test results, but also can be flexibly optimized according to market demands, making the production line configuration more efficient.

[0044] 3. The present invention combines non-linear regression and Bayesian inference methods to finely model the performance of the chip, and can continuously optimize the prediction results in the process of continuously accumulating test data. Compared with traditional methods, this technology makes the chip performance prediction more accurate and the adaptability is greatly enhanced. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] Figure 1 It is a flowchart of the method of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0046] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the drawings in the specification of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0047] Please refer to the attached Figure 1 , the embodiments of the present invention provide an automated screening method for DDR5 memory chips, including the following steps:

[0048] S1. Data acquisition: Collect the test data of the frequency, voltage, timing, and power consumption of each batch of DDR5 memory chips through an automated test platform;

[0049] Data collection collects the key performance data of each batch of DDR5 memory chips through an automated test platform. This data provides a basis for subsequent chip feature modeling, optimization, and classification, ensuring the accuracy of the entire screening process. Data collection not only includes the frequency, voltage, timing, and power consumption of the chips but also involves the performance of the chips under various environmental conditions to comprehensively reflect the working characteristics of the chips.

[0050] Implementation process of data collection: In this embodiment, the automated test platform collects parameters such as the frequency, voltage, timing, and power consumption of each batch of chips by connecting to high-precision test equipment. Specifically, the test platform conducts repeated tests under various operating conditions by setting different test conditions (such as voltage, frequency, and load). After each round of testing, the collected test data is transmitted to the control system in real time and stored in the database for subsequent analysis and optimization.

[0051] Frequency collection: Frequency collection usually relies on high-precision frequency meters that can monitor the operating frequency of the chips in real time to ensure that the impact of frequency fluctuations during the test process is fully recorded. Generally, the test platform will conduct multiple tests on each chip at different frequencies and record the impact of frequency changes on chip performance.

[0052] Voltage collection: Voltage data is collected through a voltage monitoring module to ensure the stability of the chips under different voltages. The voltage collection system can accurately measure the voltage changes of the chips under different loads and operating conditions to evaluate the chip's adaptability to voltage fluctuations.

[0053] Timing collection: Timing is one of the key factors in the performance of DDR5 memory chips and is usually collected by precision equipment such as high-speed oscilloscopes. The focus of timing testing is to capture timing errors in data transmission. By frequently testing timing changes, it can be ensured that the chips do not have excessive timing errors during operation, which may affect system stability.

[0054] Power consumption collection: Power consumption is an important indicator affecting chip performance and energy efficiency. Power consumption data is usually collected through power meters and power consumption analyzers. These devices can accurately measure the power consumption of the chips under different operating conditions to help determine the power consumption performance under various conditions.

[0055] Real-time transmission and storage of test data: All test data during the data collection process is transmitted to the control system in real time through data transmission interfaces (such as TCP / IP protocol, dedicated hardware interfaces, etc.). When transmitting data, compression and encryption technologies are usually used to ensure the security and integrity of the data during transmission. All collected test data is stored in the database and is analyzed and processed through the system. In some embodiments, the data collection system also includes a data verification module to ensure the accuracy and validity of the data collected each time.

[0056] During the data acquisition process, the test data of parameters such as frequency, voltage, timing, and power consumption will be used for subsequent optimization and modeling. To ensure the accuracy of data acquisition, the following formula is used to describe the relationship between the chip power consumption and the operating frequency and voltage:

[0057] ;

[0058] Where: is the power consumption of the chip (unit: watt, W); is the capacitance of the chip (unit: farad, F); is the operating voltage of the chip (unit: volt, V); is the operating frequency of the chip (unit: hertz, Hz).

[0059] This formula indicates that the power consumption is proportional to the square of the voltage and the frequency. In this formula, the capacitance is determined by the physical structure and process of the chip, and it, together with the changes in frequency and voltage, determines the total power consumption of the chip. By adjusting the frequency and voltage, the power consumption of the chip can be effectively controlled, thereby affecting the energy efficiency performance of the chip.

[0060] In addition, to ensure the accuracy of power consumption acquisition, a power meter and a power consumption analyzer are also used in the test platform. These devices can estimate the power consumption according to the above formula and the real-time collected data to help optimize the energy efficiency performance of the chip.

[0061] After data acquisition, the system will automatically classify and store all the collected data according to certain rules. The data storage system adopts an efficient compression technology to reduce the storage space occupancy and ensure that the data can be efficiently called in subsequent analysis.

[0062] The data will be stored in a standard format to ensure seamless compatibility with subsequent modules (such as chip characteristic modeling, parameter optimization, etc.).

[0063] The accuracy of these data provides a reliable basis for subsequent steps (such as chip characteristic modeling, parameter optimization, etc.), ensuring that the performance of the chip under different operating conditions can be comprehensively evaluated. Through real-time data transmission and an efficient storage scheme, the real-time nature and security of the test data can be guaranteed, minimizing the risk of data loss or delay to the greatest extent, thus providing a solid foundation for the automatic screening and optimization of the chip.

[0064] S2. Chip characteristic modeling: Establish the characteristic model of each batch of chips according to the collected test data;

[0065] The core task of chip characteristic modeling is to use the collected data to establish a performance characteristic model for each batch of chips, so as to better understand the performance of each chip. Through characteristic modeling, we can analyze the impact of different test parameters (such as frequency, voltage, timing) on chip performance, and provide an accurate basis for subsequent parameter optimization, automatic adjustment, and classification.

[0066] In this embodiment, the chip characteristic modeling uses the non-linear regression method to describe the relationship between chip performance and its test parameters. In these implementation manners, advanced regression techniques such as support vector regression (SVR) and decision tree regression (DTR) are adopted. These methods can effectively capture non-linear relationships and accurately describe the variation law of chip performance. Compared with the traditional linear regression method, non-linear regression can better adapt to complex test data.

[0067] In the process of characteristic modeling, through the regression model, we can quantify the relationship between various test parameters of the chip (such as frequency , voltage , timing ) and its performance indicators (such as power consumption, stability, etc.). Chip performance can be represented by the following non-linear regression formula:

[0068] ;

[0069] where: is the performance indicator of the chip (unit: depending on the measured performance indicator, such as watt (W) for power consumption, stability index, etc.); is the operating frequency of the chip (unit: Hertz, Hz). Frequency is one of the key factors affecting the chip's processing ability and affects the chip's computing speed and power consumption; is the operating voltage of the chip (unit: volt, V). Voltage is a decisive factor for the chip's operating stability and power consumption. Generally, there is a positive correlation between voltage and power consumption; is the timing of the chip (unit: nanosecond, ns). Timing is the accuracy of the clock signal when the chip reads and transmits data. Timing errors directly affect the stability of data transmission; is the error term, representing the part that the regression model cannot explain. The error term is usually used to reflect the deviation and uncertainty of the model.

[0070] The core of this regression formula is to quantify the impact of factors such as frequency, voltage, and timing on chip performance by adjusting the parameters of the model. Non-linear regression techniques (such as support vector regression) can find the most suitable model for these data through optimization algorithms, thereby improving the prediction accuracy.

[0071] In chip feature modeling, we dynamically adjust the parameters of the regression model so that the test data of each batch of chips can reflect the latest performance characteristics of the chips. This dynamic adjustment enables us to optimize the weights and parameters of the model in real time according to the actual performance of each batch of chips. The dynamic adjustment can not only adapt to the differences between chip features but also update the model based on the latest test data during the testing process to ensure that the characteristics of each chip can be accurately predicted.

[0072] For example, assume that during a certain testing process, we find that a batch of chips can improve performance under high voltage. Then we can further optimize the performance prediction of the chips by dynamically adjusting the weight of the voltage term in the regression model. Dynamically updating the regression model helps to eliminate errors and improve the accuracy and pertinence of the testing.

[0073] In this step, support vector regression (SVR) and decision tree regression (DTR) are selected as non-linear modeling methods mainly because they can well handle the non-linear relationship between chip performance and test parameters.

[0074] Support vector regression (SVR): SVR constructs a hyperplane to minimize the regression error and maps it to a high-dimensional space through a kernel function to handle complex non-linear problems. SVR can find an optimal hyperplane to minimize the error and has strong generalization ability.

[0075] Decision tree regression (DTR): Decision tree regression divides the data into several regions and then fits a simple regression model in each region, which can very intuitively reveal the relationship between parameters. DTR is very effective in dealing with complex non-linear relationships and can handle missing values and noisy data.

[0076] To improve the prediction accuracy of chip performance, in this embodiment, a data dynamic adjustment method is also adopted. After each batch of chips completes a test, the test data will be input into the model in real time, and the parameters of the regression model are adjusted to ensure accurate modeling of chip features. For example, if the chips in a certain test batch show a high timing error, we can adjust the weight of the timing term to reflect this change and ensure that subsequent tests can be carried out based on a more accurate model.

[0077] In some embodiments, the model based on dynamic adjustment will further adjust the prediction results according to the actual data and feedback. For example, the parameters in the regression model are automatically corrected through machine learning methods, so that the test data of each chip can be quickly fed back into the model to ensure accurate prediction.

[0078] Through the characteristic modeling step, the relationship between various test parameters of the chip (such as frequency, voltage, timing, etc.) and the actual performance of the chip (such as power consumption, stability, etc.) can be accurately quantified, thus providing reliable data support for subsequent optimization and screening. In particular, the use of non-linear regression methods such as SVR and DTR can effectively handle complex non-linear relationships, making performance prediction more accurate.

[0079] In addition, by dynamically adjusting the parameters of the regression model, the characteristics of each batch of chips can be reflected in a timely and accurate manner. This feature ensures that the performance of each batch of chips can be optimally evaluated during the screening process.

[0080] S3. Multi-objective optimization: Construct multiple objective functions based on the test data, and perform multi-objective optimization on the frequency error, voltage error, timing error, and power consumption to obtain the Pareto optimal solution;

[0081] The core task of multi-objective optimization is to comprehensively analyze the relationship between each test parameter and its performance target through multi-objective optimization methods, and ensure to find the best balance point among multiple mutually influencing objectives. After the data collection in step S1 and the chip characteristic modeling in step S2, the relationship between various performance indicators and test parameters of the chip has been clarified. Next, we need to optimize these parameters to make them achieve the best values in each objective function.

[0082] In this embodiment, a method combining genetic algorithm (GA) and particle swarm optimization (PSO) is used for multi-objective optimization. These two optimization methods complement each other and can effectively find the best test parameter configuration under the constraints of multiple objectives. The goal is to optimize parameters such as the frequency, voltage, timing, and power consumption of the chip to meet the requirements in terms of performance, stability, and energy efficiency.

[0083] In order to find a balance among multiple objectives, the present invention first needs to define each objective function and convert it into a mathematical formula. Generally, the optimization objectives will include frequency error, voltage error, timing error, and power consumption, and these objective functions can be expressed by the following formulas:

[0084] ;

[0085] Where: represents the frequency error of the chip; is the target frequency (unit: Hertz, Hz); represents the voltage error of the chip. This is the actual operating voltage of the chip and the target voltage The deviation between them. Excessive voltage error will lead to a decrease in chip stability and may even cause chip damage; represents the timing error of the chip; is the target timing. The smaller the timing error, the higher the accuracy and efficiency the chip can maintain during data transmission, thus improving the stability of the system; represents the power consumption of the chip; is the power consumption of the chip.

[0086] In the multi-objective optimization process, genetic algorithm and particle swarm optimization algorithm are two commonly used optimization methods. In order to comprehensively utilize the advantages of both, this embodiment adopts their combined method:

[0087] Genetic Algorithm (GA): The genetic algorithm mimics the mechanism of natural selection and gradually optimizes the solution space through operations such as selection, crossover, and mutation to explore the optimal solutions of multiple objective functions. The genetic algorithm can search for potential optimal solutions in a large-scale search space and avoid being trapped in the dilemma of local optimal solutions.

[0088] Particle Swarm Optimization (PSO): Particle swarm optimization simulates the foraging behavior of bird flocks. Each particle represents a potential solution and searches for the optimal solution through communication with other particles. The PSO method is suitable for local search and can refine the optimization process of each objective while finding the global optimal solution.

[0089] The advantage of combining these two algorithms is that the genetic algorithm can efficiently search the entire solution space, while particle swarm optimization can quickly adjust the local solution, thus achieving precise optimization of multiple objective functions.

[0090] In order to find the balance point between multiple objective functions, we first set the weights of each objective function. In some embodiments, the weights are set according to the actual importance of each objective. For example, the error of frequency may require a higher weight than the error of power consumption. The optimization algorithm will minimize the errors of these objective functions by continuously adjusting the test parameters (such as frequency, voltage, timing), so as to achieve the global optimal solution.

[0091] During the optimization process, the system will adjust the test parameters according to the feedback data after each round of testing. For example, if the power consumption of the chip is too high during the test, the system will automatically adjust the frequency or voltage to reduce the power consumption and optimize the stability of the chip. Each feedback result is dynamically updated through the adaptive mechanism of the optimization algorithm to ensure that the performance of each chip can meet the optimal conditions.

[0092] During the multi-objective optimization process, the system will automatically balance the contradictions between various objective functions. For example, increasing the frequency of the chip may increase the power consumption, or adjusting the voltage will affect the accuracy of the timing. The optimization algorithm ensures to find a reasonable compromise solution among all objectives through continuous adjustment, so that the performance of the chip can be best exerted under different working conditions.

[0093] Through the multi-objective optimization in this embodiment, it is possible to ensure that the chip achieves the best performance balance under different test conditions. This optimization not only considers the chip's working efficiency (such as frequency, voltage, etc.), but also takes into account multiple aspects such as power consumption and stability. By combining genetic algorithms and particle swarm optimization, the optimization process can effectively avoid local optimal solutions and ensure obtaining the global optimal solution.

[0094] S4. Automatic parameter adjustment: Based on the Pareto optimal solution, automatically adjust the test parameters, including frequency, voltage, timing, and error;

[0095] In step S4, based on the optimization results obtained from the foregoing steps (especially the multi-objective optimization step S3), automatically adjusting the test parameters (such as frequency, voltage, and timing) is the key to ensuring that the chip is screened under the best working conditions. In this process, the system dynamically adjusts parameters such as the chip's frequency, voltage, and timing to ensure that the chip achieves the best balance in terms of performance, stability, and energy efficiency. The core of this adjustment process is based on the feedback information from the previous multi-objective optimization results and is automatically completed in combination with real-time test data.

[0096] In this embodiment, the automatic parameter adjustment is not a simple static configuration, but a dynamic adaptive process. The core basis for the automatic adjustment comes from the Pareto optimal solution obtained in the multi-objective optimization step S3. The optimal parameter solution obtained through optimization algorithms (such as genetic algorithms and particle swarm optimization) will be used as the adjustment basis for the system in each round of testing.

[0097] Dynamic update of frequency, voltage, and timing: During the testing process, according to the test results of the chip, the system will real-time adjust parameters such as frequency, voltage, and timing to ensure that the chip is tested under the best performance state.

[0098] Adaptive feedback mechanism: The system analyzes the chip performance through a real-time feedback mechanism after each round of testing. If the test results do not match the target, the system automatically adjusts the parameters to correct the objective function that does not meet the expectations (such as power consumption, frequency error, etc.).

[0099] Precise adjustment based on the optimization results: Use the optimization results and feedback data of step S3 to automatically fine-tune the working parameters of the chip through the control system to achieve the optimal configuration.

[0100] In the automatic adjustment process of step S4, the relationship between the chip power consumption and the test parameters is still crucial. We use the power consumption formula used in the previous steps to guide the automatic adjustment process. Especially when optimizing voltage, frequency, and timing, power consumption is a key measurement criterion.

[0101] Used in S1:

[0102] ;

[0103] Wherein: is the power consumption of the chip (unit: watt, W); is the capacitance of the chip (unit: farad, F); is the operating voltage of the chip (unit: volt, V); is the operating frequency of the chip (unit: hertz, Hz).

[0104] According to the above formula, the system will dynamically adjust the frequency, voltage and timing of the chip to reduce power consumption and improve chip performance. Each time an adjustment is made, the automated system will proceed according to the following steps:

[0105] Power consumption and voltage / frequency adjustment: If the test results show that the chip power consumption is too high, the system will adjust the frequency or voltage according to the formula to reduce the power consumption and ensure the stable operation of the chip.

[0106] Stability and timing adjustment: If the timing error is large, the system will adjust based on the target timing to ensure the accuracy of chip data transmission. The timing adjustment may require fine-tuning the operating frequency of the chip while maintaining stability.

[0107] Real-time feedback mechanism: After each round of testing, the system will evaluate the power consumption, timing error, frequency error, etc. based on the real-time collected data. If some parameters deviate from the preset targets, the system will fine-tune the relevant parameters in the next round of testing. This process is adaptive and not only relies on the optimized theoretical results but also adjusts in real-time according to the chip performance.

[0108] Through the parameter automatic adjustment method of this embodiment, precise adjustment of chip performance can be achieved, and it is ensured that each batch of chips is screened in the best state. This process has the following technical effects:

[0109] Improve efficiency: The automated adjustment process reduces manual intervention and improves the accuracy and efficiency of testing. After each test is completed, the system will quickly adjust according to the actual feedback to ensure rapid acquisition of the optimal parameter configuration.

[0110] Reduce power consumption: By precisely adjusting the voltage and frequency, power consumption can be reduced on the premise of ensuring performance, improving the energy efficiency of the chip.

[0111] Enhance chip stability: By adjusting the frequency and timing, the system can ensure the stability of the chip under different operating conditions, thus avoiding stability problems caused by too high frequency or voltage.

[0112] Optimize product consistency: The automatic adjustment process eliminates the errors of manual adjustment, ensuring the consistency of the performance of each batch of chips and ensuring the consistency of the final product in terms of stability, power consumption and performance.

[0113] The automatic adjustment in step S4 updates the test parameters of the chip automatically based on the feedback results of multi-objective optimization. By combining the power consumption formula and real-time data, the system can make precise adjustments in terms of frequency, voltage, timing, etc., ensuring that each chip can be screened under the optimal working conditions.

[0114] S5. Nonlinear Modeling and Bayesian Inference: Use a nonlinear regression model to establish the nonlinear relationship between chip performance and test parameters, and combine the Bayesian inference method to update the posterior distribution of the test parameters;

[0115] The combination of nonlinear modeling and Bayesian inference is used to further optimize the prediction of chip performance, especially to accurately model and dynamically adjust chip characteristics after multiple tests. This step is carried out on the basis of the foregoing data acquisition, characteristic modeling, and multi-objective optimization. By applying the nonlinear regression method and Bayesian inference technology, the accuracy and stability of chip performance prediction are further improved. Through accurate modeling, parameters such as frequency, voltage, and timing of each chip can be effectively optimized to achieve the best performance.

[0116] Technical Implementation of Nonlinear Regression and Bayesian Inference

[0117] In this embodiment, support vector regression (SVR) and decision tree regression (DTR) are adopted as nonlinear modeling methods, and Bayesian inference is combined for parameter optimization. Through the nonlinear regression method, the complex nonlinear relationship between parameters such as frequency, voltage, and timing and chip performance can be effectively processed, while Bayesian inference can dynamically optimize chip characteristics with the results of each test by continuously updating the posterior distribution of the model.

[0118] In this step, we construct a regression model to describe the relationship between chip performance and working parameters, and then combine Bayesian inference to adjust the model parameters to improve the prediction accuracy. Specifically, the data used in the regression model comes from the test results of the previous steps, and these test results include the relationships between chip frequency, voltage, timing, etc. and chip performance (such as stability, power consumption, etc.)

[0119] Bayesian inference plays a crucial role in the model update process. It dynamically updates the parameters of the regression model by combining prior knowledge and new test data. The formula of Bayes' theorem is as follows:

[0120] ;

[0121] Where: is the posterior distribution, representing the model parameters given the data after Probability distribution. The posterior distribution reflects the updated result of the model after new data is input; is the likelihood function, representing the probability of the data given the model parameters . This part measures the likelihood of observing the data under a specific model; is the prior distribution, representing the distribution of the model parameters in the absence of data. The prior distribution provides an initial assumption about the model parameters, which can be based on historical data or expert knowledge; is the marginal likelihood, representing the total probability of the data , usually used as a normalization constant to ensure that the sum of the probabilities of all possible parameters is 1.

[0122] Bayesian inference updates the model parameters so that the prediction of the model continuously improves in accuracy as new test data accumulates. The updated model can reflect the actual performance of the chip under different working conditions.

[0123] In this embodiment, the integration steps of nonlinear modeling and Bayesian inference are as follows:

[0124] Initial model establishment: First, use nonlinear regression methods such as support vector regression (SVR) or decision tree regression (DTR) to establish a chip performance model based on existing historical data and test results. After the model is initially established, it can roughly describe the relationship between parameters such as frequency, voltage, and timing and chip performance.

[0125] Bayesian inference update: After each new test data is collected, update the posterior distribution of the parameters in the regression model through Bayesian inference methods. Bayesian inference adjusts the parameters of the model based on the combination of prior knowledge and real-time data to accurately capture the dynamic changes between chip performance and test parameters.

[0126] Real-time adjustment and optimization: Combining the updated regression model, the system can automatically adjust the test parameters according to real-time feedback. After each round of testing, Bayesian inference will automatically correct the regression model to ensure that the prediction of chip performance becomes more and more accurate.

[0127] Through the nonlinear modeling and Bayesian inference methods in this embodiment, the accuracy of chip performance prediction can be significantly improved. The advantages of this technology include:

[0128] Accurate performance prediction: Through nonlinear regression methods, the complex nonlinear relationship between chip performance and test parameters can be accurately captured, improving the accuracy of performance prediction. Bayesian inference further improves the prediction accuracy by combining prior knowledge and real-time data to optimize the update of parameters.

[0129] Adaptive optimization ability: The introduction of Bayesian inference enables the regression model to be optimized adaptively as the test data increases, thereby dynamically adjusting parameters to ensure that each test can be carried out under the best conditions.

[0130] Reducing errors and biases: By combining Bayesian inference, the error term in the regression model can be processed, thereby reducing prediction biases and improving the accuracy of the screening process.

[0131] By combining the advantages of non-linear regression and Bayesian inference, it is ensured that the performance of DDR5 memory chips can be dynamically optimized through precise modeling. Non-linear regression provides the ability to model complex performance parameters, while Bayesian inference makes the prediction results more and more accurate by dynamically updating the model. This step provides strong data support for subsequent automated screening, parameter adjustment, and chip classification, and significantly improves the accuracy and efficiency of the entire screening process.

[0132] S6, Fuzzy logic control and adaptive adjustment: Use fuzzy control rules to dynamically adjust test parameters according to real-time test data to ensure the optimal state of the test process;

[0133] Fuzzy logic control provides flexibility and adaptability for chip performance adjustment, and can dynamically adjust parameters such as chip frequency, voltage, and timing according to the data of each round of testing. The adaptive adjustment mechanism enables the control system to respond to test feedback in real time, gradually optimizing the balance between performance and power consumption, and ensuring the stability and efficiency of the chip.

[0134] The core of fuzzy logic control is to convert the input test data into fuzzy values through a fuzzy rule base, and calculate the adjustment values of test parameters according to fuzzy inference rules. Different from traditional digital control methods, fuzzy control can handle imprecise or uncertain information, so it is very suitable for complex situations in chip performance adjustment.

[0135] In this embodiment, fuzzy control is used to dynamically adjust chip operating parameters such as frequency, voltage, and timing according to real-time test data. Through fuzzy processing, the test data is converted into a fuzzy set suitable for fuzzy inference. For example, the frequency may be fuzzified into low frequency, medium frequency, or high frequency, and the voltage may be fuzzified into low voltage, standard voltage, or high voltage.

[0136] In this embodiment, the fuzzy control system uses a rule base to guide the adaptive adjustment of the chip. The fuzzy control rules are formulated based on historical data and experimental experience, and the rule base may contain the following content:

[0137] If the frequency is high frequency and the power consumption is high power consumption, then the voltage should be reduced;

[0138] If the voltage is high and the timing error is large, reduce the voltage and adjust the timing;

[0139] If the timing error is small and the power consumption is low, increase the frequency.

[0140] These rules are inferred through a fuzzy inference engine to generate the adjustment direction and amplitude for each test parameter (such as frequency, voltage, timing).

[0141] Different from traditional static regulation, the adaptive adjustment mechanism in this embodiment relies on real-time feedback data. After each test is completed, the test data of the chip is input into the system through the feedback mechanism, and the control system re-evaluates the fuzzy control rules based on this feedback data and adjusts the parameters of the chip. Specifically:

[0142] Frequency adjustment: When the test results show that the chip frequency error is large, the system adjusts the frequency through fuzzy control. If the power consumption is high and the timing error is large, the frequency will be reduced to avoid instability.

[0143] Voltage adjustment: If the test results indicate that the chip power consumption is too high, the system will dynamically adjust the voltage through fuzzy control rules. Low voltage can usually reduce power consumption, but if the voltage is too low, it may cause instability in chip performance. Therefore, voltage adjustment needs to find a balance between stability and power consumption.

[0144] Timing adjustment: Timing error directly affects the stability of the chip and the accuracy of data transmission. When the timing error is too large, the system reduces the frequency or adjusts the timing parameters through fuzzy control rules to ensure higher accuracy of the chip during testing.

[0145] In actual operation, the fuzzy controller dynamically adjusts the working parameters of the chip according to the following steps:

[0146] Fuzzify the input data: Convert parameters such as frequency, voltage, and timing into fuzzy sets according to certain rules. The purpose of fuzzification is to convert continuous variables into discrete states for easy processing by fuzzy inference.

[0147] Inference stage: Based on the fuzzy control rules, calculate the adjustment values of each test parameter through the fuzzy inference engine. This process determines how to adjust parameters such as the working frequency, voltage, and timing of the chip through inference of the rule base.

[0148] Defuzzify the output data: The output values after fuzzy inference will be defuzzified to convert the fuzzy values into specific test parameter adjustment values to guide the chip to be optimized in the next round of testing.

[0149] Through the fuzzy logic control and adaptive adjustment method of this embodiment, the optimization ability of chip performance can be significantly improved. The specific technical effects and beneficial effects are as follows:

[0150] Improve system stability: Through fuzzy control, the system can adjust parameters such as frequency, voltage, and timing according to real-time feedback to ensure that the chip always maintains stability under different working conditions.

[0151] Reduce power consumption: By precisely adjusting voltage and frequency, the system can effectively reduce power consumption, improve energy efficiency, and avoid heat problems and unnecessary power consumption caused by excessive power consumption.

[0152] Enhance adaptability: The adaptive adjustment mechanism enables the system to dynamically respond to the test feedback of different chips, adjust and optimize parameters after each round of testing, and improve the flexibility and accuracy of testing.

[0153] Accelerate the screening process: Fuzzy control can reduce manual intervention. Through an automated feedback adjustment mechanism, it improves the efficiency and accuracy of the testing process and saves a large amount of human resources.

[0154] Through fuzzy control rules and adaptive feedback mechanisms, the system can optimize the balance between power consumption and performance to ensure the stable and efficient operation of the chip.

[0155] S7. Chip classification and BIN assignment: Classify the chips according to the test results, divide the chips into multiple BIN groups according to their performance indicators, and output the test parameters of each BIN group;

[0156] Chip classification and BIN assignment are based on various parameters (such as power consumption, frequency error, timing error, etc.) obtained from the tests in the previous steps. Through established performance standards, the chips are divided into different BIN groups. Each BIN group represents a performance level, and the chips are assigned to different BIN groups according to their performance for subsequent selective production according to market demand and product specifications.

[0157] In this embodiment, the process of chip classification and BIN assignment is based on the comprehensive results of the previous steps such as multi-objective optimization, non-linear modeling, Bayesian inference, and fuzzy control. After each test is completed, the system generates performance data including multiple indicators such as frequency, voltage, and timing, and then processes these data to determine the BIN group to which the chip belongs. The following is the detailed classification and assignment process:

[0158] Summary of performance data: The performance data such as power consumption, frequency, and timing error, which are collected and optimized through the previous steps (such as S1, S2, S3, etc.), are processed and summarized into the system after processing. These data provide the basis for classification and assignment.

[0159] Define performance thresholds: The system sets classification thresholds for each performance parameter. For example, the power consumption threshold can be divided into three levels: low power consumption, standard power consumption, and high power consumption. Classification criteria for timing error and frequency error can also be set in a similar manner. These thresholds help the system evaluate and group the chip performance.

[0160] Chip classification and allocation: The system assigns each chip to an appropriate BIN group by comparing the chip's performance data with the preset classification criteria. Chips in each BIN group will be custom-configured according to their performance during subsequent production processes to meet different market demands.

[0161] Output parameters and production guidance: Each BIN group contains not only chip classification information but also specific parameters of the chips in that group, such as frequency, voltage, and timing, etc., for appropriate configuration and processing on the production line.

[0162] To ensure the accurate classification and allocation of chips in terms of performance, the BIN allocation in this embodiment is based on test data such as power consumption, timing error, and frequency error. The following formula shows how to perform BIN allocation of chips based on power consumption and frequency error ( )

[0163] ;

[0164] Where: is the BIN group of the chip (e.g., low power consumption BIN, medium power consumption BIN, high power consumption BIN, etc.); is the power consumption of the chip; is the frequency error of the chip; is the function representing the mapping relationship between the power consumption and frequency error of the chip and its corresponding BIN group.

[0165] In some embodiments, the BIN allocation of chips may involve other metrics in addition to power consumption and frequency error, such as timing error, stability, etc. The following is the supplement and implementation method of this process:

[0166] Combination of timing error and frequency error: In some cases, timing error can have a significant impact on chip performance. Therefore, in addition to power consumption and frequency error, timing error can also be used as one of the classification bases. By setting the threshold of timing error, the system can further optimize the chip classification.

[0167] Adaptive adjustment: As the chip test data accumulates, the system can continuously adjust the performance classification criteria. For example, the threshold of frequency error can be dynamically adjusted according to the actual performance of the chip to ensure that the classification criteria always meet the latest production requirements.

[0168] Market demand-driven classification: The parameter settings for each BIN group take into account not only the performance of the chips but also the market demand. For example, in the market with low power consumption requirements, low-power chips will be preferentially selected, while in the high-performance market, chips with high frequency and low timing error will be chosen.

[0169] Through the chip classification and BIN assignment method in this embodiment, the accuracy and production efficiency of chip screening can be effectively improved. Specifically, the beneficial effects of this technical solution include:

[0170] Accurate chip classification: Through comprehensive evaluation in multiple dimensions such as power consumption, frequency error, and timing error, the system can accurately classify each chip to ensure that different chips can meet different market demands.

[0171] Optimize the production process: By grouping the chips according to their performance, the production line can be configured specifically according to the specific characteristics of the chips, improving production efficiency and reducing resource waste.

[0172] Enhance market adaptability: The accuracy of chip classification ensures that the market demand can be quickly responded to during the production process. For example, for the demand of the high-performance market, the system can preferentially select chips with high frequency and low timing error for production.

[0173] Improve test efficiency: The system can automatically complete the grouping of chips based on real-time test data and preset classification criteria, reducing the complexity of manual intervention and thus improving the overall test and screening efficiency.

[0174] Through accurate performance testing and data analysis, the system can accurately classify chips according to multiple indicators such as power consumption, frequency error, and timing error of the chips and assign them to different BIN groups. This process not only optimizes the configuration of the production line but also ensures that the chips can meet the demands of different markets, improving production efficiency and product consistency.

[0175] Although the embodiments of the present invention have been shown and described, for those of ordinary skill in the art, it can be understood that various changes, modifications, substitutions, and variations can be made to these embodiments without departing from the principles and spirit of the present invention. The scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. An automated screening method for DDR5 memory chips, characterized in that The steps include: S1. Data collection: Collect the test data of the frequency, voltage, timing, and power consumption of each batch of DDR5 memory chips through an automated test platform; S2. Chip characteristic modeling: Establish the characteristic model of each batch of chips according to the collected test data; Through characteristic modeling, analyze the influence of different test parameters on chip performance, and provide a basis for subsequent parameter optimization, automatic adjustment, and classification; S3. Multi-objective optimization: Construct multiple objective functions based on the test data, perform multi-objective optimization on the frequency error, voltage error, timing error, and power consumption, and obtain the Pareto optimal solution; S4. Parameter automatic adjustment: Automatically adjust the test parameters based on the Pareto optimal solution, including frequency, voltage, timing, and error; S5. Nonlinear modeling and Bayesian inference: Use a nonlinear regression model to establish the nonlinear relationship between chip performance and test parameters, and update the posterior distribution of test parameters in combination with the Bayesian inference method; S6. Fuzzy logic control and adaptive adjustment: Use fuzzy control rules to dynamically adjust the test parameters according to real-time test data to ensure the optimal state of the test process; S7. Chip classification and BIN assignment: Classify the chips according to the test results, divide the chips into multiple BIN groups according to their performance indicators, and output the test parameters of each BIN group.

2. The automated screening method for a DDR5 memory chip according to claim 1, wherein The data collection includes: Real-time collect the test parameters of the frequency, voltage, timing, and power consumption of the chips; Transmit the collected test data to the control system for subsequent analysis and optimization.

3. The automated screening method for a DDR5 memory chip according to claim 1, wherein The chip characteristic modeling includes: Use the nonlinear regression method to model the relationship between chip performance and test parameters; Perform performance modeling on each batch of chips and dynamically adjust according to actual data.

4. The automated screening method for a DDR5 memory chip according to claim 1, characterized in that, The multi-objective optimization includes: Define the frequency error, voltage error, timing error, and power consumption as multiple objective functions; Set the weights of each objective function according to actual needs, and use the genetic algorithm and particle swarm optimization to search for the Pareto optimal solution to ensure the balance between all objectives.

5. The automated screening method for a DDR5 memory chip according to claim 1, characterized in that, The parameter automatic adjustment includes: Automatically update the test parameters based on the Pareto optimal solution, including frequency, voltage, and timing; During each round of the test process, dynamically update the test parameters according to real-time data and test errors to ensure that each test parameter adjustment meets the target chip performance standard and optimize the real-time feedback mechanism during the test.

6. The automated screening method for a DDR5 memory chip according to claim 1, wherein The nonlinear modeling and Bayesian inference include: Adopt the support vector regression method and decision tree regression method to model the nonlinear relationship between chip performance and test parameters; Use the Bayesian inference method to update the posterior distribution of parameters in combination with historical data and real-time data.

7. The automated screening method for a DDR5 memory chip according to claim 1, wherein The fuzzy logic control and adaptive adjustment include: Perform fuzzy processing on the test data of the chips, and convert the input data of frequency, voltage, timing, and power consumption into fuzzy sets; Based on the fuzzy control rules in the rule base, perform inference on the test data, and calculate the direction and amplitude of the test parameter adjustment; Perform defuzzification processing on the inference result to obtain the adjusted test parameters; Through the automatic adjustment mechanism, adjust the test parameters according to the results of each round of the test to optimize the test performance of the chips.

8. The automated screening method for a DDR5 memory chip according to claim 1, wherein The chip classification and BIN assignment include: Classifying the chips based on the test results; Dividing the chips into multiple BIN groups according to the performance indicators, and outputting the test parameters of each BIN group.

9. The automated screening method for a DDR5 memory chip according to claim 7, characterized in that The automatic adjustment mechanism adjusts the test parameters through real-time feedback. If the test result of a certain chip deviates from the preset standard, the system automatically fine-tunes its test parameters to ensure that the chip undergoes the next round of tests under optimal conditions.

10. The automated screening method for a DDR5 memory chip according to claim 1, characterized in that, The method is implemented through a computer system, and the computer system includes: A data acquisition module, a characteristic modeling module, an optimization algorithm module, a Bayesian inference module, a fuzzy control module, and a classification module; The computer system performs automated optimization and screening based on the real-time test data of the chips, and outputs the final chip classification and BIN assignment results.

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