Automatic screening method for DDR5 memory chips

Abstract

The invention relates to the technical field of semiconductor screening, and discloses an automatic screening method for DDR5 memory chips, which comprises the following steps: S1, data acquisition: acquiring test data of frequency, voltage, time sequence and power consumption of each batch of DDR5 memory chips through an automatic test platform; s2, chip characteristic modeling: establishing a characteristic model of each batch of chips according to the collected test data; s3, multi-objective optimization: constructing a plurality of objective functions according to the test data, and performing multi-objective optimization on the frequency error, the voltage error, the time sequence error and the power consumption to obtain a Pareto optimal solution; and S4, automatically adjusting parameters. Through multi-objective optimization, nonlinear modeling, Bayesian reasoning and fuzzy control, the precision and automation of the chip screening process are realized, the screening precision and efficiency are remarkably improved, and the performance consistency and stability of the chip in high-load and special application environments are ensured.

Description

Technical Field

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

[0002] With the continuous development of science and technology, memory chips are increasingly 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. As DDR5 memory chips have higher and higher performance requirements in terms of frequency, power consumption, timing, etc., higher requirements are also placed on the accuracy of chip screening.

[0003] In the prior art, most chip screening methods use standard test parameters, mainly by testing the chip's power consumption, frequency, timing and other performance indicators to evaluate whether it meets the basic working requirements. Through automated testing equipment, the performance parameters of the chip 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 find chips with poor performance to prevent unqualified chips from entering the market; second, the existing automated testing equipment can significantly reduce manual operations and improve production efficiency; finally, some methods in the chip screening process can ensure that the chip's frequency, voltage and other parameters operate stably within a certain range to ensure that the performance is basically qualified.

[0004] Most screening methods in existing technologies rely on manually set standard parameters and cannot be flexibly adjusted and optimized according to the characteristics of each batch of chips, which leads to less than ideal screening effects and performance consistency under high load or special application environments. Secondly, the screening methods of existing technologies often rely on only a few performance indicators and cannot fully consider the comprehensive performance of the chip under different working conditions. Finally, although automated testing improves screening efficiency, in applications with high performance and high timing requirements, existing methods often find it difficult to achieve fine performance adjustments. Summary of the invention

[0005] In view of the shortcomings 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 the characteristics of different chips, insufficient performance adjustment accuracy, and inconsistent screening effects.

[0006] To achieve the above objectives, the present invention is implemented through the following technical solutions: a method for automatic screening of DDR5 memory chips, comprising the following steps: S1. Data collection: collect the test data of frequency, voltage, timing and power consumption of each batch of DDR5 memory chips through the automated test platform; S2. Chip characteristic modeling: Establish the characteristic model of each batch of chips based on the collected test data; S3, multi-objective optimization: construct multiple objective functions based on test data, perform multi-objective optimization on frequency error, voltage error, timing error and power consumption, and obtain the Pareto optimal solution; S4, automatic parameter adjustment: based on the Pareto optimal solution, automatically adjust the test parameters, including frequency, voltage, timing and error; S5. Nonlinear modeling and Bayesian reasoning: Use nonlinear regression models to establish the nonlinear relationship between chip performance and test parameters, and combine Bayesian reasoning methods to update the posterior distribution of test parameters; 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; S7, chip classification and BIN allocation: 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.

[0007] Preferably, the data collection includes: Collect the test parameters of the chip's frequency, voltage, timing and power consumption in real time; The collected test data is transmitted to the control system for subsequent analysis and optimization.

[0008] Preferably, the chip characteristic modeling includes: Use nonlinear regression methods to model the relationship between chip performance and test parameters; Performance modeling is performed for each batch of chips, and dynamic adjustments are made based on actual data.

[0009] Preferably, the multi-objective optimization includes: Define frequency error, voltage error, timing error and power consumption as multiple objective functions; The weight of each objective function is set according to actual needs, and the genetic algorithm and particle swarm optimization are used to search for the Pareto optimal solution to ensure the balance between all objectives.

[0010] Preferably, the automatic parameter adjustment includes: Automatically update test parameters including frequency, voltage and timing based on Pareto optimal solution; During each round of testing, the test parameters are dynamically updated based on real-time data and test errors to ensure that each test parameter adjustment meets the target chip performance standards and optimize the real-time feedback mechanism during the test process.

[0011] Preferably, the nonlinear modeling and Bayesian reasoning include: Support vector regression and decision tree regression methods are used to model the nonlinear relationship between chip performance and test parameters; The posterior distribution of parameters is updated using Bayesian inference methods combining historical data and real-time data.

[0012] Preferably, the fuzzy logic control and adaptive adjustment include: Perform fuzzy processing on the chip test data, converting the input data of frequency, voltage, timing and power consumption into fuzzy sets; Based on the fuzzy control rules in the rule base, the test data is inferred to calculate the direction and amplitude of the test parameter adjustment; Defuzzify the inference results to obtain the adjusted test parameters; Through the automatic adjustment mechanism, the test parameters are adjusted according to the results of each round of testing to optimize the test performance of the chip.

[0013] Preferably, the chip classification and BIN allocation include: Classify chips based on test results; The chips are divided into multiple BIN groups according to performance indicators, and the test parameters of each BIN group are output.

[0014] Preferably, the automatic adjustment mechanism adjusts the test parameters through real-time feedback. If the test result of a 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.

[0015] Preferably, the method is implemented by a computer system, and the computer system comprises: Data acquisition module, characteristic modeling module, optimization algorithm module, Bayesian reasoning module, fuzzy control module and classification module; The computer system automatically optimizes and screens the chip's real-time test data, and outputs the final chip classification and BIN allocation results.

[0016] The present invention provides a method for automatic screening of DDR5 memory chips. It has the following beneficial effects: 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 to ensure that the chip performance is always kept in the best state. Compared with the traditional method, it eliminates manual intervention, improves the accuracy and efficiency of the test, and can respond flexibly, especially under high frequency and high load conditions.

[0017] 2. The present invention uses multi-dimensional analysis to accurately classify chips and allocate them to different BIN groups. This allocation method not only dynamically adjusts according to test results, but also can be flexibly optimized according to market demand, making production line configuration more efficient.

[0018] 3. The present invention combines nonlinear regression and Bayesian reasoning 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 chip performance prediction more accurate and adaptable. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 The present invention is a flow chart of the method. DETAILED DESCRIPTION

[0020] The following will be combined with the drawings in the specification of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0021] Please see attached Figure 1 , an embodiment of the present invention provides a method for automatic screening of DDR5 memory chips, comprising the following steps: S1. Data collection: collect the test data of frequency, voltage, timing and power consumption of each batch of DDR5 memory chips through the automated test platform; Data collection collects key performance data of each batch of DDR5 memory chips through an automated testing platform. These data provide the 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 chip, but also involves the performance of the chip under various environmental conditions to ensure that the working characteristics of the chip are fully reflected.

[0022] Implementation process of data collection: In this embodiment, the automated test platform collects parameters such as frequency, voltage, timing, and power consumption of each batch of chips by connecting to high-precision test equipment. Specifically, the test platform performs 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 will be transmitted to the control system in real time and stored in the database for subsequent analysis and optimization.

[0023] Frequency acquisition: Frequency acquisition usually relies on high-precision frequency meters that can monitor the operating frequency of the chip in real time to ensure that the impact of frequency fluctuations during the test is fully recorded. In general, the test platform will test each chip multiple times at different frequencies and record the impact of frequency changes on chip performance.

[0024] Voltage acquisition: Voltage data is collected through the voltage monitoring module to ensure the stability of the chip under different voltages. The voltage acquisition system can accurately measure the voltage changes of the chip under different loads and working conditions to evaluate the chip's adaptability to voltage fluctuations.

[0025] Timing acquisition: Timing is one of the key factors in the performance of DDR5 memory chips, and is usually acquired 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 chip will not have excessive timing errors when working, affecting system stability.

[0026] Power consumption collection: Power consumption is an important indicator that affects chip performance and energy efficiency. Power consumption data is usually collected through power meters and power analyzers. These devices can accurately measure the power consumption of chips under different working conditions and help determine the power consumption performance under various conditions.

[0027] Real-time transmission and storage of test data: All test data during the data acquisition process is transmitted to the control system in real time through a data transmission interface (such as TCP / IP protocol, dedicated hardware interface, etc.). When transmitting data, compression and encryption technology are usually used to ensure the security and integrity of the data during transmission. All collected test data will be stored in the database and subsequently analyzed and processed by the system. In some embodiments, the data acquisition system also includes a data verification module to ensure the accuracy and validity of each collected data.

[0028] During the data collection process, the test data of parameters such as frequency, voltage, timing and power consumption will be used for subsequent optimization and modeling. In order to ensure the accuracy of data collection, the following formula is used to describe the relationship between chip power consumption and operating frequency and voltage: ; in: 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).

[0029] This formula shows that power dissipation is proportional to the square of the voltage and the frequency. In this formula, the capacitor It is determined by the physical structure and process of the chip, and together with the changes in frequency and voltage, it 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.

[0030] In addition, in order to ensure the accuracy of power consumption collection, power meters and power consumption analyzers are also used in the test platform. These devices can estimate power consumption based on the above formula and real-time collected data to help optimize the energy efficiency performance of the chip.

[0031] After data collection, the system will automatically classify and store all collected data according to certain rules. The data storage system uses efficient compression technology to reduce storage space and ensure that data can be efficiently called in subsequent analysis.

[0032] The data will be stored in a standard format to ensure seamless compatibility with subsequent modules (such as chip characteristic modeling, parameter optimization, etc.).

[0033] 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 working conditions can be fully evaluated. Through real-time data transmission and efficient storage solutions, the real-time and security of test data can be guaranteed, minimizing the risk of data loss or delay, thus providing a solid foundation for the automated screening and optimization of chips.

[0034] S2. Chip characteristic modeling: Establish the characteristic model of each batch of chips based on the collected test data; The core task of chip characteristic modeling is to use the collected data to build a performance characteristic model for each batch of chips in order 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.

[0035] In this embodiment, the chip characteristic modeling uses a nonlinear regression method to describe the relationship between chip performance and its test parameters. In these embodiments, advanced regression techniques such as support vector regression (SVR) and decision tree regression (DTR) are used. These methods can effectively capture nonlinear relationships and accurately describe the changing laws of chip performance. Compared with traditional linear regression methods, nonlinear regression can better adapt to complex test data.

[0036] In the process of feature modeling, we can use the regression model to calculate the various test parameters of the chip (such as frequency ,Voltage , Timing ) and its performance indicators (such as power consumption, stability, etc.). Chip performance It can be expressed by the following nonlinear regression formula: ; in: is the performance index of the chip (unit: depends on the performance index measured, such as Watt (W) for power consumption, stability index, etc.); The operating frequency of the chip (unit: Hertz, Hz). Frequency is one of the key factors in chip processing capabilities, affecting the chip's computing speed and power consumption; is the operating voltage of the chip (unit: volt, V). Voltage is the determining factor of chip operating stability and power consumption. Generally, there is a positive correlation between voltage and power consumption. The timing of the chip (unit: nanosecond, ns). The 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, which represents the part that cannot be explained by the regression model. The error term is usually used to reflect the bias and uncertainty of the model.

[0037] 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. Nonlinear regression techniques (such as support vector regression) can find the most suitable model for these data through optimization algorithms, thereby improving prediction accuracy.

[0038] 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 chip. This dynamic adjustment allows us to optimize the weights and parameters of the model in real time based on the actual performance of each batch of chips. Dynamic adjustment can not only adapt to the differences between chip characteristics, but also update the model according to the latest test data during the test process to ensure that the characteristics of each chip can be accurately predicted.

[0039] For example, if we find that a batch of chips can improve performance at high voltage during a test, we can further optimize the chip performance prediction by dynamically adjusting the weight of the voltage term in the regression model. Dynamically updating the regression model helps eliminate errors and improve the accuracy and pertinence of the test.

[0040] In this step, support vector regression (SVR) and decision tree regression (DTR) are selected as nonlinear modeling methods, mainly because they can handle the nonlinear relationship between chip performance and test parameters well.

[0041] Support Vector Regression (SVR): SVR minimizes the regression error by constructing a hyperplane and maps it to a high-dimensional space through a kernel function to handle complex nonlinear problems. SVR can find an optimal hyperplane that minimizes the error and has strong generalization capabilities.

[0042] Decision Tree Regression (DTR): Decision tree regression can intuitively reveal the relationship between parameters by dividing the data into several regions and then fitting a simple regression model in each region. DTR is very effective in dealing with complex nonlinear relationships and can handle missing values ​​and noisy data.

[0043] In order to improve the prediction accuracy of chip performance, this embodiment also adopts a method of dynamic data adjustment. 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 will be adjusted to ensure accurate modeling of chip characteristics. For example, if a chip in a test batch shows a high timing error, we can adjust the weight of the timing item to reflect this change, ensuring that subsequent tests can be performed on the basis of a more accurate model.

[0044] In some embodiments, the dynamically adjusted model may further adjust the prediction results based on actual data and feedback. For example, the parameters in the regression model may be automatically corrected through machine learning methods, so that each chip test data can be quickly fed back to the model to ensure accurate prediction.

[0045] Through the characteristic modeling step, the relationship between the chip's various test parameters (such as frequency, voltage, timing, etc.) and the chip's actual performance (such as power consumption, stability, etc.) can be accurately quantified, thereby providing reliable data support for subsequent optimization and screening. In particular, the use of nonlinear regression methods such as SVR and DTR can effectively handle complex nonlinear relationships, making performance predictions more accurate.

[0046] 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.

[0047] S3, multi-objective optimization: construct multiple objective functions based on test data, perform multi-objective optimization on frequency error, voltage error, timing error and power consumption, and obtain the Pareto optimal solution; 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 to ensure that the best balance point is found among multiple mutually influencing targets. After data collection in step S1 and chip characteristic modeling in step S2, the relationship between the various performance indicators and test parameters of the chip has been clarified. Next, we need to optimize these parameters to achieve the best value in each objective function.

[0048] In this embodiment, a combination of 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 the chip's frequency, voltage, timing, power consumption and other parameters to meet the requirements of performance, stability and energy efficiency.

[0049] In order to find a balance between multiple objectives, the present invention first needs to define each objective function and convert it into a mathematical formula. Generally, the optimization objectives include frequency error, voltage error, timing error and power consumption, and these objective functions can be expressed by the following formula: ; in: Indicates the frequency error of the chip; is the target frequency (unit: Hertz, Hz); Indicates the voltage error of the chip. This is the actual working voltage of the chip With target voltage The voltage error is too large, which will lead to the decrease of chip stability and may even cause chip damage; Indicates the timing error of the chip; is the target timing. The smaller the timing error, the higher the accuracy and efficiency of the chip during data transmission, thereby improving the stability of the system. Indicates the power consumption of the chip; is the power consumption of the chip.

[0050] 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 combination: Genetic Algorithm (GA): Genetic Algorithm imitates the mechanism of natural selection, gradually optimizes the solution space through selection, crossover, mutation and other operations, and explores the optimal solution of multiple objective functions. Genetic Algorithm can find potential optimal solutions in a large-scale search space and avoid falling into the dilemma of local optimal solution.

[0051] Particle Swarm Optimization (PSO): Particle swarm optimization simulates the foraging behavior of bird flocks. Each particle represents a potential solution and communicates with other particles to find the optimal solution. The PSO method is suitable for local search and can refine the optimization process of each objective while finding the global optimal solution.

[0052] The advantage of combining these two algorithms is that the genetic algorithm can efficiently search the entire solution space, while the particle swarm optimization can quickly adjust the local solution, thereby achieving accurate optimization of multiple objective functions.

[0053] In order to find the balance between multiple objective functions, we first set the weight of each objective function. In some embodiments, the weight is set according to the actual importance of each objective. For example, the frequency error may require a higher weight than the power error. The optimization algorithm will minimize the errors of these objective functions by continuously adjusting the test parameters (such as frequency, voltage, and timing) to achieve the global optimal solution.

[0054] During the optimization process, the system adjusts the test parameters based on 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 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 meets the optimal conditions.

[0055] During the multi-objective optimization process, the system automatically balances the contradictions between the various objective functions. For example, increasing the frequency of the chip may increase power consumption, or adjusting the voltage may affect the accuracy of the timing. The optimization algorithm continuously adjusts to ensure that a reasonable compromise solution is found between all objectives, so that the performance of the chip can be optimized under different working conditions.

[0056] 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 takes into account the working efficiency of the chip (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 that the global optimal solution is obtained.

[0057] S4, automatic parameter adjustment: based on the Pareto optimal solution, automatically adjust the test parameters, including frequency, voltage, timing and error; In step S4, based on the optimization results obtained in the previous steps (especially the multi-objective optimization step S3), automatically adjusting the test parameters (such as frequency, voltage, and timing) is the key to ensure that the chip is screened in the best working state. In this process, the system dynamically adjusts the chip's frequency, voltage, timing and other parameters to ensure that the chip achieves the best balance in performance, stability and energy efficiency. The core of this adjustment process is based on the feedback information of the previous multi-objective optimization results, and is automatically completed in combination with real-time test data.

[0058] In this embodiment, the automatic parameter adjustment is not a simple static configuration, but a dynamic adaptive process. The core basis of the automatic adjustment comes from the Pareto optimal solution obtained in the multi-objective optimization step S3. The optimal parameter solution obtained by the optimization algorithm (genetic algorithm, particle swarm optimization, etc.) will serve as the basis for the adjustment of the system in each round of testing.

[0059] Dynamic update of frequency, voltage, and timing: During the test process, the system will adjust the frequency, voltage, timing and other parameters in real time according to the test results of the chip to ensure that the chip is tested under the best performance state.

[0060] Adaptive feedback mechanism: The system uses a real-time feedback mechanism to analyze chip performance after each round of testing. If the test results do not meet the target, the system automatically adjusts the parameters to correct the target function that does not meet the expectations (such as power consumption, frequency error, etc.).

[0061] Precise adjustment based on optimization results: Using the optimization results and feedback data from step S3, the control system automatically fine-tunes the chip's operating parameters to achieve the optimal configuration.

[0062] In the automatic adjustment process of step S4, the relationship between chip power consumption and test parameters is still crucial. We adopted the power consumption formula used in the previous step to guide the automatic adjustment process, especially when optimizing voltage, frequency and timing, power consumption is a key metric.

[0063] Using S1: ; in: 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).

[0064] According to the above formula, the system will dynamically adjust the chip's frequency, voltage and timing to reduce power consumption and improve chip performance. Each time an adjustment is made, the automated system will follow these steps: Power consumption, voltage and frequency adjustment: If the test results show that the chip power consumption is too high, the system will adjust the power consumption according to the formula Readjust the frequency or voltage to reduce power consumption and ensure stable chip operation.

[0065] 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. Timing adjustment may require fine-tuning the chip's operating frequency while maintaining stability.

[0066] Real-time feedback mechanism: After each round of testing, the system will evaluate power consumption, timing error, frequency error, etc. based on real-time collected data. If some parameters deviate from the preset target, the system will fine-tune the relevant parameters in the next round of testing. This process is adaptive and does not only rely on the theoretical results of optimization, but also adjusts in real time according to chip performance.

[0067] The automatic parameter adjustment method of this embodiment can achieve accurate adjustment of chip performance and ensure that each batch of chips is screened in the best state. This process has the following technical effects: Improve efficiency: The automated adjustment process reduces manual intervention and improves the accuracy and efficiency of the test. After each test is completed, the system will quickly adjust according to actual feedback to ensure that the best parameter configuration is quickly obtained.

[0068] Reduce power consumption: By precisely adjusting voltage and frequency, power consumption can be reduced while ensuring performance, thereby improving the energy efficiency of the chip.

[0069] Enhanced chip stability: By adjusting frequency and timing, the system can ensure the stability of the chip under different operating conditions, thus avoiding stability issues caused by excessive frequency or voltage.

[0070] Optimize product consistency: The automatic adjustment process eliminates the errors caused by manual adjustment, ensuring the consistency of the performance of each batch of chips and ensuring the consistency of the final product in stability, power consumption and performance.

[0071] The automatic adjustment in step S4 automatically updates the test parameters of the chip 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 frequency, voltage, timing, etc. to ensure that each chip can be screened under the best working conditions.

[0072] S5. Nonlinear modeling and Bayesian reasoning: Use nonlinear regression models to establish the nonlinear relationship between chip performance and test parameters, and combine Bayesian reasoning methods to update the posterior distribution of test parameters; Nonlinear modeling and Bayesian reasoning are combined to further optimize the prediction of chip performance, especially to accurately model and dynamically adjust chip characteristics after multiple tests. This step is based on the aforementioned data collection, characteristic modeling, and multi-objective optimization. By applying nonlinear regression methods and Bayesian reasoning technology, the accuracy and stability of chip performance prediction are further improved. Through accurate modeling, the frequency, voltage, timing and other parameters of each chip can be effectively optimized to achieve the best performance.

[0073] Technical Implementation of Nonlinear Regression and Bayesian Inference In this embodiment, support vector regression (SVR) and decision tree regression (DTR) are used as nonlinear modeling methods, combined with Bayesian reasoning for parameter optimization. The nonlinear regression method can effectively handle the complex nonlinear relationship between parameters such as frequency, voltage, timing and chip performance, while Bayesian reasoning can dynamically optimize chip characteristics with the results of each test by continuously updating the posterior distribution of the model.

[0074] In this step, we construct a regression model to describe the relationship between chip performance and operating parameters, and then adjust the model parameters in combination with Bayesian reasoning to improve the accuracy of the prediction. Specifically, the data used by the regression model comes from the test results of the previous step. These test results include the relationship between the chip's frequency, voltage, timing, etc. and the chip's performance (such as stability, power consumption, etc.) Bayesian reasoning plays a vital role in the model updating process. It dynamically updates the parameters of the regression model by combining previous knowledge with new test data. The formula of Bayes' theorem is as follows: ; in: is the posterior distribution, indicating that given data After that, the model parameters The posterior distribution reflects the updated result of the model after the new data is input; is the likelihood function, which indicates that given the model parameters When, data This part measures the likelihood of observing data under a specific model; is the prior distribution, representing the model parameters The distribution when there is no data. The prior distribution provides an initial assumption about the model parameters and can be based on historical data or expert knowledge; is the marginal likelihood, which means the data The total probability of , which is often used as a normalizing constant to ensure that the sum of the probabilities of all possible parameters is 1.

[0075] Bayesian inference updates model parameters , so that the model's predictions continue to improve as new test data accumulates. The updated model can reflect the actual performance of the chip under different working conditions.

[0076] In this embodiment, the integration steps of nonlinear modeling and Bayesian reasoning are as follows: Initial model establishment: First, nonlinear regression methods such as support vector regression (SVR) or decision tree regression (DTR) are used 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, timing and chip performance.

[0077] Bayesian reasoning update: After each new test data is collected, the posterior distribution of the parameters in the regression model is updated through the Bayesian reasoning method. Bayesian reasoning 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.

[0078] Real-time adjustment and optimization: Combined with the updated regression model, the system can automatically adjust the test parameters based on real-time feedback. After each round of testing, Bayesian reasoning will automatically correct the regression model, ensuring that the prediction of chip performance is increasingly accurate.

[0079] The nonlinear modeling and Bayesian reasoning method in this embodiment can significantly improve the accuracy of chip performance prediction. The advantages of this technology include: 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 reasoning combines prior knowledge with real-time data to optimize parameter updates and further improve prediction accuracy.

[0080] Adaptive optimization capability: The introduction of Bayesian reasoning enables the regression model to be adaptively optimized as the test data increases, thereby dynamically adjusting parameters to ensure that each test is performed under optimal conditions.

[0081] Reducing Error and Bias: By incorporating Bayesian inference, it is possible to handle the error terms in the regression model, thereby reducing prediction bias and improving the accuracy of the screening process.

[0082] By combining the advantages of nonlinear regression and Bayesian reasoning, the performance of DDR5 memory chips can be dynamically optimized through accurate modeling. Nonlinear regression provides the ability to model complex performance parameters, while Bayesian reasoning 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.

[0083] 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; Fuzzy logic control provides flexibility and adaptability for chip performance adjustment, and can dynamically adjust the chip's frequency, voltage, timing and other parameters 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 optimize the balance between performance and power consumption, and ensure the stability and efficiency of the chip.

[0084] The core of fuzzy logic control is to convert the input test data into fuzzy values ​​through the fuzzy rule base, and calculate the adjustment value of the test parameter according to the fuzzy inference rules. Unlike traditional digital control methods, fuzzy control can handle imprecise or uncertain information, so it is very suitable for complex situations in chip performance adjustment.

[0085] 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 fuzzification, the test data is converted into a fuzzy set suitable for fuzzy reasoning. For example, frequency may be fuzzified into low frequency, medium frequency or high frequency, and voltage may be fuzzified into low voltage, standard voltage or high voltage.

[0086] 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 include the following: If the frequency is high frequency and the power consumption is high power consumption, the voltage should be reduced; If the voltage is high and the timing error is a large timing error, then reduce the voltage and adjust the timing; If the timing error is small and the power consumption is low, then increase the frequency.

[0087] These rules are inferred through a fuzzy inference engine to generate the adjustment direction and magnitude of each test parameter (such as frequency, voltage, and timing).

[0088] Unlike traditional static regulation, the adaptive adjustment mechanism in this embodiment relies on real-time feedback data. After each test is completed, the chip test data will be input into the system through the feedback mechanism. The control system re-evaluates the fuzzy control rules based on these feedback data and adjusts the chip parameters. Specifically: Frequency adjustment: When the test results show that the chip frequency error is large, the system will adjust 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.

[0089] Voltage adjustment: If the test results show 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 unstable chip performance, so voltage adjustment needs to find a balance between stability and power consumption.

[0090] Timing adjustment: Timing error will directly affect 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 that the chip performs more accurately during the test.

[0091] In actual operation, the fuzzy controller dynamically adjusts the chip's operating parameters according to the following steps: Fuzzify 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 to facilitate fuzzy reasoning.

[0092] Reasoning stage: Based on fuzzy control rules, the fuzzy reasoning engine calculates the adjustment value of each test parameter. This process determines how to adjust the chip's operating frequency, voltage, timing and other parameters by reasoning on the rule base.

[0093] Defuzzification of output data: The output value after fuzzy inference will be defuzzified, and the fuzzy value will be converted into a specific test parameter adjustment value to guide the chip to optimize in the next round of testing.

[0094] The fuzzy logic control and adaptive adjustment method of this embodiment can significantly improve the optimization capability of chip performance. The specific technical effects and beneficial effects are as follows: 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.

[0095] Reduce power consumption: By precisely adjusting voltage and frequency, the system can effectively reduce power consumption, improve energy efficiency, and avoid thermal problems and unnecessary power consumption caused by excessive power consumption.

[0096] Enhanced adaptive capability: The adaptive adjustment mechanism enables the system to dynamically respond to test feedback from different chips, adjust optimization parameters after each round of testing, and improve test flexibility and accuracy.

[0097] Accelerate the screening process: Fuzzy control can reduce manual intervention and improve the efficiency and accuracy of the testing process through an automated feedback adjustment mechanism, saving a lot of human resources.

[0098] Through fuzzy control rules and adaptive feedback mechanisms, the system can optimize the balance between power consumption and performance to ensure stable and efficient operation of the chip.

[0099] S7, chip classification and BIN allocation: 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; Chip classification and BIN allocation Based on the various parameters (such as power consumption, frequency error, timing error, etc.) tested in the previous steps, the chips are divided into different BIN groups according to the established performance standards. Each BIN group represents a performance level, and the chips are allocated in different BIN groups according to their performance, so that they can be selectively produced according to market demand and product specifications.

[0100] In this embodiment, the chip classification and BIN allocation process is based on the comprehensive results of the aforementioned multi-objective optimization, nonlinear modeling, Bayesian reasoning, fuzzy control and other steps. After each test is completed, the system will generate performance data including frequency, voltage, timing and other indicators, and then process these data to determine the BIN group to which the chip belongs. The following is a detailed classification and allocation process: Performance data aggregation: The performance data collected and optimized through the above steps (such as S1, S2, S3, etc.), such as power consumption, frequency, timing error, etc., are aggregated into the system after processing. These data provide a basis for classification and allocation.

[0101] 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. Timing error and frequency error can also be classified in a similar way. These thresholds help the system evaluate and group chip performance.

[0102] Chip classification and allocation: The system allocates each chip to the appropriate BIN group by comparing the chip's performance data with the preset classification criteria. The chips in each BIN group will be customized according to their performance in the subsequent production process to meet different market needs.

[0103] Output parameters and production guidance: Each BIN group contains not only chip classification information, but also specific parameters of the group of chips, such as frequency, voltage and timing, so that they can be properly configured and processed on the production line.

[0104] In order to ensure accurate classification and allocation of chip 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 use power consumption and frequency error ( ) to allocate BIN of the chip: ; in: BIN group of the chip (for example: low power BIN, medium power BIN, high power BIN, etc.); is the power consumption of the chip; is the frequency error of the chip; This function represents the mapping relationship between the power consumption and frequency error of the chip and its corresponding BIN group.

[0105] In some embodiments, the BIN allocation of the chip is not only based on power consumption and frequency error, but may also involve other indicators, such as timing error, stability, etc. The following is a supplement and implementation of the process: 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 criteria. By setting the threshold of timing error, the system can further optimize the classification of chips.

[0106] Adaptive adjustment: As 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, ensuring that the classification criteria always meet the latest production requirements.

[0107] Classification driven by market demand: The parameter setting of each BIN group not only takes into account the performance of the chip, but also takes into account the market demand. For example, for the market with low power consumption requirements, low-power chips will be given priority, while the high-performance market will choose chips with high frequency and low timing error.

[0108] The chip classification and BIN allocation method in this embodiment can effectively improve the accuracy and production efficiency of chip screening. Specifically, the beneficial effects of this technical solution include: Accurate chip classification: Through comprehensive evaluation of multiple dimensions such as power consumption, frequency error, and timing error, the system can accurately classify each chip to ensure that different chips can adapt to different market needs.

[0109] Optimize the production process: By grouping the chips according to their performance, the production line can be configured according to the specific characteristics of the chips, thereby improving production efficiency and reducing resource waste.

[0110] Enhanced market adaptability: The accuracy of chip classification ensures that the market demand can be quickly responded to during the production process. For example, in response to the needs of the high-performance market, the system can give priority to the production of high-frequency and low-timing error chips.

[0111] Improve test efficiency: The system can automatically group chips according to real-time test data and preset classification standards, reducing the complexity of manual intervention and thus improving overall test and screening efficiency.

[0112] Through precise performance testing and data analysis, the system can accurately classify chips according to multiple indicators such as power consumption, frequency error, timing error, etc., 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 needs of different markets, improving production efficiency and product consistency.

[0113] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for automatic screening of DDR5 memory chips, characterized in that: The following steps are involved: S1. Data collection: collect the test data of frequency, voltage, timing and power consumption of each batch of DDR5 memory chips through the automated test platform; S2. Chip characteristic modeling: Establish the characteristic model of each batch of chips based on the collected test data; S3, multi-objective optimization: construct multiple objective functions based on test data, perform multi-objective optimization on frequency error, voltage error, timing error and power consumption, and obtain the Pareto optimal solution; S4, automatic parameter adjustment: based on the Pareto optimal solution, automatically adjust the test parameters, including frequency, voltage, timing and error; S5. Nonlinear modeling and Bayesian reasoning: Use nonlinear regression models to establish the nonlinear relationship between chip performance and test parameters, and combine Bayesian reasoning methods to update the posterior distribution of test parameters; 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; S7, chip classification and BIN allocation: 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 method for automatic screening of DDR5 memory chips according to claim 1, characterized in that: The data collection includes: Collect the test parameters of the chip's frequency, voltage, timing and power consumption in real time; The collected test data is transmitted to the control system for subsequent analysis and optimization.

3. The method for automatic screening of DDR5 memory chips according to claim 1, characterized in that: The chip characteristic modeling includes: Use nonlinear regression methods to model the relationship between chip performance and test parameters; Performance modeling is performed for each batch of chips, and dynamic adjustments are made based on actual data.

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

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

6. The method for automatic screening of DDR5 memory chips according to claim 1, characterized in that: The nonlinear modeling and Bayesian reasoning include: Support vector regression and decision tree regression methods are used to model the nonlinear relationship between chip performance and test parameters; The posterior distribution of parameters is updated using Bayesian inference methods combining historical data and real-time data.

7. The method for automatic screening of DDR5 memory chips according to claim 1, characterized in that: The fuzzy logic control and adaptive adjustment include: Perform fuzzy processing on the chip test data, converting the input data of frequency, voltage, timing and power consumption into fuzzy sets; Based on the fuzzy control rules in the rule base, the test data is inferred to calculate the direction and amplitude of the test parameter adjustment; Defuzzify the inference results to obtain the adjusted test parameters; Through the automatic adjustment mechanism, the test parameters are adjusted according to the results of each round of testing to optimize the test performance of the chip.

8. The method for automatic screening of DDR5 memory chips according to claim 1, characterized in that: The chip classification and BIN allocation include: Classify chips based on test results; The chips are divided into multiple BIN groups according to performance indicators, and the test parameters of each BIN group are output.

9. The method for automatic screening of DDR5 memory chips 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 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.

10. The method for automatic screening of DDR5 memory chips according to claim 1, characterized in that: The method is implemented by a computer system, and the computer system comprises: Data acquisition module, characteristic modeling module, optimization algorithm module, Bayesian reasoning module, fuzzy control module and classification module; The computer system automatically optimizes and screens the chip's real-time test data, and outputs the final chip classification and BIN allocation results.

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