Dynamic adaptive multi-parameter tuning optimization method and system

Abstract

The application provides a dynamic adaptive multi-parameter trimming optimization method and system, which comprises the following steps: obtaining a high-correlation parameter combination through correlation analysis; performing joint trimming to obtain a joint trimming parameter group; setting an initial vertex for searching according to the joint trimming parameter group, and performing initial trimming code combination; calculating a process deviation function for the initial trimming code combination, calculating a vertex process deviation value and sorting to obtain a sorting result; designing an adaptive search algorithm, fitting a quadratic deviation function based on data driving, dynamically adjusting a search starting point, finding an optimal trimming parameter set, and obtaining a dynamic centroid; and performing reflection, expansion and contraction operations according to the sorting result and the dynamic centroid, and iteratively searching for an applicable trimming parameter combination. The application solves the technical problems of parameter mutual interference in the trimming process, complicated trimming process and large time consumption, difficulty in quickly finding a global optimal solution, and lack of dynamic adaptability.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of integrated circuit testing technology, and particularly relates to a dynamic adaptive multi-parameter trimming optimization method and system. BACKGROUND

[0002] Process variation refers to the difference between the actual circuit parameters, such as the threshold voltage of a transistor, the resistance value, and the capacitance value, and the ideal design value due to the imperfection of the manufacturing process. With the increasing complexity of integrated circuits (ICs), the fine and small process variations of the manufacturing process have a significant impact on the performance and yield of the circuit. In order to ensure that the circuit meets the design specifications and optimizes its performance, the parameters in the circuit must be trimmed to compensate for the process variations in the manufacturing process, so as to ensure that the circuit performance meets the final design requirements.

[0003] In integrated circuits, there is a dependency relationship between multiple trimming parameters, that is, the adjustment of one parameter may affect the performance of other parameters, thereby causing mutual interference in the trimming process and affecting the final optimization result. The existing trimming method is mainly single-point trimming, that is, each trimming parameter is adjusted separately. By applying different trimming codes on the automatic test equipment (ATE), the circuit output is observed and the error is calculated until the error is within the tolerance range. This method is simple to operate, but since the trimming is performed one by one, it often needs to correct the previous parameter when adjusting one parameter, resulting in a tedious and inefficient trimming process. Therefore, how to efficiently handle the dependency relationship between trimming parameters and design a more intelligent optimization algorithm is a technical problem to be solved in the current integrated circuit trimming field.

[0004] The existing trimming method usually ignores the dependency relationship between multiple trimming parameters, resulting in mutual interference between parameters in the trimming process. Secondly, the way of adjusting one parameter at a time is simple to operate, but since the trimming is performed one by one, when adjusting one parameter, it often needs to correct other adjusted parameters, resulting in a tedious trimming process and large time consumption. In addition, the existing method for multi-parameter trimming has a slow convergence speed, is easy to fall into local optimum, is difficult to quickly find the global optimal solution, and lacks dynamic adaptability, and cannot adjust according to the real-time changes of process variations.

[0005] The existing patent application for invention with publication number CN118016132A discloses a chip tuning method, device, system and storage medium. The existing method includes: determining a first target tuning item from at least two tuning items, and determining a second target tuning item from at least two tuning items that satisfies a first condition with the first target tuning item, the first condition being used to indicate that there is a tuning influence between the first target tuning item and the second target tuning item; and tuning the first target tuning item and the second target tuning item based on a test value of the first target tuning item. However, the foregoing prior art obtains a tuning value by judging the interval in which the current parameter is located, such as a high interval or a low interval, and considers the same trend / inverse trend relationship of related parameters to form a tuning strategy and realize linkage tuning, but does not have real-time adaptive capability; lacks modeling of parameter change trend, and one-time tuning may cause excessive or insufficient deviation, and cannot handle nonlinear or non-deterministic parameter coupling.

[0006] The existing patent application for invention with publication number CN119355482A discloses a chip parameter tuning method, device, equipment and medium. The existing method includes: obtaining multiple register data of a to-be-tested chip and corresponding multiple to-be-tuned parameters; wherein the multiple to-be-tuned parameters corresponding to the multiple register data have a change relationship; sorting the multiple to-be-tuned parameters according to a set order to obtain a sorted to-be-tuned parameter list; establishing a linear correspondence relationship based on the sorted to-be-tuned parameter list and a set identification list; wherein the set identification list carries register data; and determining a target tuning parameter according to the linear correspondence relationship and a set standard parameter. However, the foregoing prior art uses a neural network model to perform tuning prediction, inputs a starting parameter and a target value, outputs a predicted tuning value, and writes the tuning value into a chip at one time. However, the foregoing prior art relies on a large number of samples for training, and when there is a large difference between chips and there are insufficient samples, the effect is poor, and the foregoing prior art cannot adapt to customized chips or few-sample problems; the output is a single-step tuning, and lacks a path evaluation and iterative optimization mechanism.

[0007] The prior art patent application document with the publication number CN118914807A discloses a self-adaptive chip parameter tuning method based on convolution operation calculation, which comprises the following steps: collecting time domain response data of a chip under different chip parameter settings; decomposing the time domain response data of the chip into basis functions by using a singular value decomposition method, and reconstructing the time domain response data through convolution operation; comparing the reconstructed time domain response data with preset standard time domain response data, optimizing the chip parameters through an adaptive combined search algorithm, and determining an optimal parameter combination; and performing tuning processing on the chip parameters through a tuning method according to the determined optimal parameter combination. However, the prior art is limited by the initial test combination accuracy, and if the test point distribution is poor, the combination may be selected incorrectly, and the prior art does not have feedback correction capability; the prior art cannot dynamically adjust the path or introduce new tuning points, and has weak expansibility; and the convolution method is only suitable for regular change trends, and is difficult to cope with situations with large noise or non-continuous characteristics.

[0008] In summary, the prior art has the technical problems of mutual interference of parameters during the tuning process, complicated tuning process and large time consumption, difficulty in quickly finding a global optimal solution, and lack of dynamic adaptability. SUMMARY

[0009] The technical problem to be solved by the present application is how to solve the technical problems of mutual interference of parameters during the tuning process, complicated tuning process and large time consumption, difficulty in quickly finding a global optimal solution, and lack of dynamic adaptability in the prior art.

[0010] The present application solves the above technical problems by adopting the following technical scheme: a dynamic adaptability multi-parameter tuning optimization method comprises:

[0011] S1, correlation modeling of chip tuning parameters is performed through correlation analysis to obtain a high correlation parameter combination;

[0012] S2, joint tuning is performed according to the high correlation parameter combination to obtain a joint tuning parameter group;

[0013] S3, the initial vertices of a Simplex search are set according to the joint tuning parameter group, and initial tuning code combination is performed; wherein, the Simplex search is dynamically optimized; a quadratic deviation function is fitted through data driving to capture the change trend of the process deviation value, the best position of the centroid is speculated, and the Simplex search is guided so that the Simplex search is concentrated in the potential optimal solution region;

[0014] S4, process deviation function calculation is performed on the initial tuning code combination, the vertex process deviation values are sorted to obtain a sorting result;

[0015] S5, design an adaptive search algorithm, based on data driving, fit a quadratic deviation function, dynamically adjust the search starting point, find the optimal set of tuning parameters, and obtain the Simplex dynamic centroid;

[0016] S6, according to the sorting result and the Simplex dynamic centroid, perform reflection, expansion and contraction operations, and iteratively search for suitable tuning parameter combinations.

[0017] The present application performs dynamic adaptive multi-parameter tuning optimization, aiming to solve the problems of insufficient dependency processing, low tuning efficiency and slow convergence speed in the existing tuning process. By introducing parameter dependency relationship identification and joint tuning mechanism, using adaptive search algorithm, dynamically adjusting the search starting point, optimizing the search direction, accelerating the convergence of the tuning process, ensuring that the tuning process can flexibly respond to the dynamic changes of process deviation, and improving the efficiency and accuracy of tuning.

[0018] In a more specific technical solution, in S1, the Spearman correlation coefficient is used to calculate the correlation coefficient between chip tuning parameters, a correlation matrix is generated, correlation analysis is performed, the dependency relationship between chip tuning parameters is obtained, and a high correlation parameter combination is obtained according to the dependency relationship processing.

[0019] In a more specific technical solution, in S1, a preset correlation threshold is used to determine the correlation degree between parameters according to the correlation coefficient between chip tuning parameters, high correlation, indirect dependency parameters and low correlation parameters are obtained, parameter grouping is performed based on the correlation matrix between parameters, a high correlation parameter combination is obtained by jointing high correlation, indirect dependency parameters, and low correlation parameters are independently tuned and tested.

[0020] The present application adopts Spearman correlation (Spearman), which mainly investigates the strength of the monotonic relationship between variables, and does not depend on the specific distribution form of the data, and is suitable for processing various numerical data. The Spearman correlation coefficient is used to calculate the correlation coefficient between chip tuning parameters, which can effectively reveal the dependency relationship between chip tuning parameters.

[0021] The present application is suitable for any chip type, especially suitable for scenes with limited samples or large chip differences. At the same time, through the feedback of each step of tuning, it is judged whether the running deviation meets the tolerance requirement, supporting path correction, dynamic termination and reconstruction, so that the tuning process has openness, controllability and traceability, and overcomes the black box problem of traditional model prediction method.

[0022] In a more specific technical solution, in S3, the problem modeling operation is performed; for the identified joint tuning parameter combination, the tuning code corresponding to each chip tuning parameter is set;

[0023] construction a Simplex search of vertices, wherein each vertex represents a combination of different trim codes of different chip trim parameters in a joint trim parameter set;

[0024] based on the current joint trim parameter set, calculating an actual output of the circuit, setting a process deviation function according to the actual output and the expected output, and obtaining a minimized process deviation function by adjusting the trim codes of the chip trim parameters so that the actual output approaches the expected output .

[0025] The application does not rely on fixed trend preset rules, but generates a differentiated trim path according to real-time running parameters, target values, parameter boundaries and historical trim results of each chip, and performs feedback detection after each step of trim to dynamically decide whether to continue, adjust the direction or terminate.

[0026] In a more specific technical solution, in S4, the process deviation value of the vertex is obtained by calculating a process deviation function according to the output corresponding to the first vertex and the expected output based on the initial trim code combination; the process deviation value combination is obtained by combining the process deviation values of the respective vertices based on the initial trim code combination; and the sorting operation is performed according to the process deviation value combination.

[0027] In a more specific technical solution, in S5, the quadratic deviation function is fitted, the mode of change of the process deviation value of the vertex with the parameter is predicted according to the current data point, and the centroid is calculated, wherein the position of the Simplex dynamic centroid in the Simplex search is obtained by solving the minimum point of the fitted quadratic deviation function.

[0028] The application captures the change trend of the process deviation value of the vertex by fitting the quadratic deviation function based on data, and speculates the best position of the centroid to effectively guide the search to focus on the potential optimal solution region, thereby optimizing the reflection, expansion and contraction operations.

[0029] The quadratic deviation function fitting process of the application is simple, and can accurately capture the trend of change of the process deviation value of the vertex with the parameter, which helps to reduce the number of iterations and quickly guide the search towards the optimal value.

[0030] The position of the centroid is obtained by solving the minimum point of the fitted quadratic deviation function, which takes into account the influence weight of the current point and also considers the distribution trend of the entire parameter space, which helps the search process to jump out of the local optimum and approach the global optimal solution.

[0031] The application can search a multi-parameter space more efficiently to find the optimal combination of trimming parameters.

[0032] In a more specific technical solution, in the contraction operation of S6, if the reflection or expansion does not realize the improved solution operation, the contraction operation is performed, the contraction point is calculated according to the Simplex dynamic centroid, the worst point in the sorting result and the contraction coefficient, and if the objective function value of the contraction point is better than that of the worst point, the worst point is updated to the contraction point.

[0033] In a more specific technical solution, in S6, when the reflection, expansion and contraction operations do not realize the process deviation value improvement operation, the reduction operation is performed to narrow the search range, so that all the vertices except the optimal point in the sorting result are reduced in distance to the optimal point.

[0034] In a more specific technical solution, in S6, the reflection point is calculated, if the objective function value of the reflection point is better than that of the optimal point in the sorting result, the expansion point is calculated according to the Simplex dynamic centroid, the worst point in the sorting result and the expansion coefficient , the search area is expanded, and if the objective function value of the expansion point meets the preset applicable condition, the worst point is updated to the expansion point.

[0035] The application dynamically constructs the adjustment path by combining point-by-point trimming and instant feedback, does not depend on the global characteristics of the combination space, but implements fine trimming for specific chip states. Each step determines whether to continue or give up according to the trimming result, which improves robustness and avoids error accumulation. At the same time, historical trimming experience is used to improve reasoning accuracy, which is more stable and reliable when dealing with complex and irregular trends.

[0036] In a more specific technical solution, the dynamic adaptive multi-parameter trimming optimization system comprises:

[0037] The correlation analysis module is used to perform correlation modeling on the chip trimming parameters through correlation analysis to obtain a high-correlation parameter combination.

[0038] The joint trimming module is used to perform joint trimming according to the high-correlation parameter combination to obtain a joint trimming parameter group, and the joint trimming module is connected with the correlation analysis module.

[0039] The dynamic optimization Simplex search module is used to set the initial vertex of the Simplex search according to the joint adjustment parameter group, and perform initial adjustment code combination, wherein the Simplex search is dynamically optimized; a quadratic deviation function is fitted through data driving, the change trend of the process deviation value is captured, the best position of the centroid is speculated, the Simplex search is guided, the Simplex search is concentrated in the potential optimal solution area, and the dynamic optimization Simplex search module is connected with the joint adjustment module.

[0040] The deviation computer sorting module is used to perform process deviation function calculation on the initial adjustment code combination, sort the vertex process deviation values, and obtain a sorting result, and the deviation computer sorting module is connected with the dynamic optimization Simplex search module.

[0041] The quadratic deviation function fitting module is used to design an adaptive search algorithm, fit a quadratic deviation function based on data driving, dynamically adjust a search starting point, find an optimal adjustment parameter set, and obtain a Simplex dynamic centroid.

[0042] The iterative search module is used to perform reflection, expansion and contraction operations according to the sorting result and the Simplex dynamic centroid, and iteratively search for an applicable adjustment parameter combination, and the iterative search module is connected with the quadratic deviation function fitting module and the deviation computer sorting module.

[0043] Compared with the prior art, the present application has the following advantages:

[0044] The present application performs dynamic adaptive multi-parameter adjustment optimization, aims to solve the problems of insufficient dependency processing, low adjustment efficiency and slow convergence speed in the existing adjustment process. By introducing parameter dependency relationship identification and joint adjustment mechanism, using an adaptive search algorithm, dynamically adjusting a search starting point, optimizing a search direction, accelerating the convergence of the adjustment process, and ensuring that the adjustment process can flexibly cope with the dynamic changes of the process deviation, the efficiency and precision of the adjustment are improved.

[0045] The Spearman correlation adopted in the present application mainly investigates the strength of the monotonic relationship between variables, and does not depend on the specific distribution form of the data, and is suitable for processing various numerical data. The Spearman correlation coefficient is used to calculate the correlation coefficient between chip adjustment parameters, which can effectively reveal the dependency relationship between chip adjustment parameters.

[0046] The present application fits a quadratic deviation function through data driving, captures the change trend of the vertex process deviation value, and speculates the best position of the centroid, so as to effectively guide the search to be concentrated in the potential optimal solution area, thereby optimizing the reflection, expansion and contraction operations.

[0047] The quadratic deviation function fitting process of this invention is simple and can accurately capture the trend of the vertex process deviation value as the parameters change, which helps to reduce the number of iterations and quickly guide the search toward the optimal value.

[0048] The position of the centroid is obtained by solving for the minimum point of the fitted quadratic deviation function. This takes into account both the influence weight of the current point and the distribution trend of the entire parameter space, which helps the search process to escape local optima and approach the global optimum.

[0049] This invention enables a more efficient search of the multi-parameter space to find the optimal combination of tuning parameters. The dynamic calculation of the centroid avoids the inefficiencies and local minima problems that may be encountered in traditional methods, thereby improving the algorithm's convergence speed and tuning accuracy.

[0050] This invention solves the technical problems existing in the prior art, such as mutual interference of parameters during the adjustment process, cumbersome and time-consuming adjustment process, difficulty in quickly finding the global optimal solution, and lack of dynamic adaptability. Attached Figure Description

[0051] Figure 1 This is a schematic diagram of the basic steps of the dynamic adaptive multi-parameter tuning and optimization method in Embodiment 1 of the present invention;

[0052] Figure 2 This is a schematic diagram illustrating the specific implementation steps of the iterative search for the optimal combination of adjustment parameters in the reflection, expansion, and contraction operations of Embodiment 1 of the present invention.

[0053] Figure 3 This is a comparison chart of the average number of iterations for code finding in sample analysis of Embodiment 1 of the present invention. Detailed Implementation

[0054] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below in conjunction with the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0055] Example 1

[0056] like Figure 1 As shown, the dynamic adaptive multi-parameter tuning and optimization method provided by this invention includes the following basic steps:

[0057] S1. Based on the combination of highly correlated parameters, perform joint adjustment to obtain a joint adjustment parameter set;

[0058] In the embodiment, the parameter-dependent relationship analysis is performed; specifically, the correlation modeling of the chip tuning parameters is performed through the correlation analysis, and the dependent relationship between the parameters is identified;

[0059] In the embodiment, the process deviation is the circuit parameter caused by the imperfection of the manufacturing process, for example, the difference between the threshold voltage, resistance value, and capacitance value of the transistor and the ideal design value. The process deviation is one of the key factors affecting the circuit performance and yield. In order to compensate for the process deviation, the tuning operation adjusts the variable parameters in the circuit of the integrated chip, optimizes the circuit performance, and restores the circuit to the design specification range.

[0060] In the joint tuning process of the multiple parameters of the integrated chip, the circuit parameters are not independent, and there is a dependent relationship. If the dependent relationship is ignored, the adjustment of some circuit parameters in the tuning process may interfere with each other, thereby affecting the optimization result. In the embodiment, the optimization of the joint tuning of the multiple parameters is taken as the target.

[0061] In the embodiment, the Spearman correlation coefficient is used to calculate the correlation coefficient between the chip tuning parameters, generate the correlation matrix between the parameters, perform the correlation analysis, obtain the dependent relationship between the chip tuning parameters, and obtain the high-correlation parameter combination according to the dependent relationship.

[0062] In the foregoing parameter-dependent relationship analysis process, the correlation between the evaluation variables is a key link for understanding the dependent relationship of the tuning parameters. In the embodiment, the correlation analysis method includes the Chi-Square Test, the Pearson Correlation Coefficient, the Spearman’s Rank Correlation Coefficient, and the Kendall’s Tau-b Correlation Coefficient. Unlike the rest of the methods, the Spearman correlation coefficient (Spearman) examines the strength of the monotonic relationship between variables, and does not depend on the specific distribution form of the tuning parameters, and is suitable for processing numerical values and data in various tuning parameters. Therefore, the present application plans to use the Spearman correlation coefficient to calculate the correlation coefficient between the parameters, and effectively reveal the dependent relationship between the tuning parameters.

[0063] In the embodiment, the preset correlation threshold is used to determine the correlation degree between the tuning parameters according to the correlation coefficient between the tuning parameters of the integrated chip, obtain the high-correlation, indirect-dependent parameter, and low-correlation parameter, group the parameters based on the correlation matrix between the parameters, obtain the high-correlation parameter combination by combining the high-correlation, indirect-dependent parameter, and independently tune the low-correlation parameter.

[0064] In the embodiment, Spearman correlation coefficients between two adjustment parameters, such as A and B, are calculated to generate a parameter correlation matrix of the adjustment parameters, and the correlation coefficients r The calculation can be performed by the following formula:

[0065]

[0066] wherein, is the rank difference of the i th sample in two adjustment parameters, is the number of samples. The sample is an adjustment code.

[0067] In the embodiment, the dependence between parameters is identified by calculating the Spearman correlation coefficients between the adjustment parameters. High correlation, for example, indicates that the correlation between the adjustment parameters is strong, and low correlation, for example, indicates that the correlation between the adjustment parameters is weak. Based on the parameter correlation matrix, the adjustment parameters are grouped: the parameters with high correlation and indirect dependence are combined into a group for joint adjustment, and the low correlation parameters are independently adjusted and tested.

[0068] S2, according to the high correlation parameter combination, joint adjustment is performed to obtain a joint adjustment parameter group;

[0069] In the embodiment, the high correlation parameter combination is extracted according to the parameter correlation matrix, and the high correlation parameter combination is combined for joint adjustment.

[0070] S3, according to the joint adjustment parameter group, the initial vertex of the Simplex search is set, and the initial adjustment code combination is performed; wherein, the Simplex search is dynamically optimized; by data-driven fitting of the quadratic deviation function, the change trend of the process deviation value is captured, the best position of the centroid is inferred, and the Simplex search is guided, so that the Simplex search is concentrated in the potential optimal solution region;

[0071] In the embodiment, the Simplex search is based on dynamic optimization; specifically, the Simplex algorithm is used to optimize the search strategy of the multi-parameter adjustment process. The traditional centroid calculation removes the worst point and calculates the average position, which is used as the starting point of the search, and provides a reasonable direction for the next search. However, this method only considers the average position of all points, which may lead to excessive dependence on poor performing points. The present application aims to effectively guide the search to concentrate in the potential optimal solution region by data-driven fitting of the quadratic deviation function, capturing the change trend of the process deviation value, and inferring the best position of the centroid, and optimizing the reflection, expansion and contraction operations.

[0072] ​In this embodiment, a problem modeling operation is performed; for the identified joint tuning parameter group, a tuning code corresponding to the tuning parameter of each chip is set;

[0073] Build The simplex search of vertices, where each vertex represents a combination of different tuning codes for different chip tuning parameters in the joint tuning parameter group;

[0074] Based on the current joint tuning parameter set, the actual output of the calculation circuit is determined. Then, based on the actual output and the desired output, a process deviation function is set: by adjusting the tuning codes of the chip tuning parameters, the actual output is made closer to the desired output, thus obtaining the minimized process deviation function. .

[0075] In this embodiment, problem modeling is performed; specifically, for the identified joint tuning parameter group, for example: Set the trimming code corresponding to each chip trimming parameter. , build The simplex of a vertex, where each vertex is represented as: …, , where each vertex This represents a combination of different tuning codes for different chip tuning parameters in the joint tuning parameter group, for example: vertex. The three modifier parameters may use the following modifier codes: . n Represents the number of vertices. P This indicates a joint adjustment parameter group. X Represents a vertex. m This indicates the modifier code.

[0076] Set the current joint tuning parameter group i process deviation function ,in, Based on the current adjustment parameter group i The actual output of the calculation, such as the system's operating frequency and power. To achieve the desired output, the adjustment codes of the adjustment parameters are adjusted to make the actual output of the circuit as close as possible to the desired output, i.e., to minimize the process deviation function. . O This indicates the output.

[0077] S4. Calculate the process deviation function for the initial adjustment code combination, obtain the vertex process deviation value and sort it to get the sorting result;

[0078] In the embodiment, for the initial adjustment code combination, the process deviation function is calculated according to the output corresponding to the first vertex, the expected output, to obtain the vertex process deviation value; the respective vertex process deviation values are combined through the initial adjustment code combination to obtain the process deviation value combination, and the sorting operation is performed according to the process deviation value combination;

[0079] In the embodiment, for the initial joint adjustment parameter set corresponding to the vertex …, The process deviation function is calculated as follows:

[0080]

[0081] Among them, indicates the output corresponding to the adjustment code corresponding to each chip adjustment parameter; the vertex …, The respective process deviation values are sorted, for example: the worst point , the second worst point , and the optimal point .

[0082] S5, design an adaptive search algorithm, based on data driving, fit a quadratic deviation function, dynamically adjust the search starting point, find the optimal adjustment parameter set, and obtain the Simplex dynamic centroid;

[0083] In the embodiment, the quadratic deviation function is fitted, the mode of change of the vertex process deviation value with the parameter is predicted according to the current data point, and the centroid calculation is performed, wherein the position of the Simplex dynamic centroid in the Simplex search is obtained by solving the minimum point of the fitted quadratic deviation function.

[0084] In the embodiment, the quadratic deviation function is fitted based on the vertex corresponding to the current data, and the Simplex dynamic centroid is obtained.

[0085] In the embodiment, the quadratic deviation function is fitted; specifically, since the process deviation value i of each joint adjustment parameter set can only describe the deviation of the vertex, it cannot comprehensively reflect the change trend of the process deviation value in the current parameter space, therefore, the quadratic deviation function is fitted to predict the mode of change of the process deviation value with the parameter through the current data point.

[0086]

[0087] Among them, f indicates the reflection parameter, indicates the first fitting coefficient, represents a second fitting coefficient, represents a third fitting coefficient, represents a first tuning code, i represents a first tuning code, represents a first tuning code. j represents a first tuning code.

[0088] The quadratic deviation function fitting is relatively simple, and can better capture the trend of the process deviation value changing with the parameter, thereby helping to reduce the iteration number and quickly guide the search to proceed towards the optimal value.

[0089] In the embodiment, the calculation of the centroid is performed; specifically, the conventional centroid calculation method is performed by taking the average of all vertices except the worst point, however, in actual optimization, especially in a multi-dimensional parameter space, the simple average calculation cannot fully reflect the complex error distribution and mutual dependence relationship.

[0090] The present application obtains the position of the centroid by solving the quadratic deviation function after fitting , which takes into account the influence weight of the current point and also considers the distribution trend of the entire parameter space, thereby helping to jump out of the local optimum and approach the global optimal solution.

[0091] S6, according to the sorting result and the Simplex dynamic centroid, reflection, expansion, and contraction operations are performed to iteratively search for a suitable tuning parameter combination.

[0092] In the embodiment, according to the sorting result and the calculation of the centroid, reflection, expansion, contraction, and reduction operations can be performed to optimize the search process;

[0093] As shown in the foregoing step S6, the specific implementation steps include but are not limited to: Figure 2 S61, initializing the Simplex vertex,

[0094] 1, X 2,..., X n+1 , calculating each process deviation value X ;

[0095] S62, sorting the vertices X h , X s ,..., and calculating the dynamic centroid X centroid ;

[0096] S63, calculating the reflection point X r and the reflection point X ​r corresponding objective function f(X r ) ;

[0097] Reflection: calculate the reflection point , update the search direction by reflecting the worst point to the other side of the centroid:

[0098]

[0099] where, is the centroid, is the worst point, is the reflection coefficient, usually taken as 1.

[0100] S64, determine whether: ;

[0101] In this embodiment, if the reflection point X r is better than the optimal point f(X r ) , replace the worst point with the reflection point;

[0102] S65, if not, determine whether: ;

[0103] S66, if yes, perform the contraction operation: calculate the contraction point X c and the contraction point X c corresponding objective function f(X c ) ;

[0104] In the contraction operation, if reflection or expansion fails to improve the solution, perform the contraction operation, calculate the contraction point according to the Simplex dynamic centroid, the worst point in the sorting result, and the contraction coefficient; if the objective function value of the contraction point is better than that of the worst point, update the worst point to the contraction point;

[0105] Contraction: if reflection or expansion fails to effectively improve the solution, perform the contraction operation to calculate the contraction point :

[0106]

[0107] where, is the contraction coefficient, which can take values such as: ​​0. If the objective function value at the contraction point is better than that at the worst point, then update the worst point to the contraction point;

[0108] S67. Determine if the following conditions are met: f(X c ) < f(X h ) ;in, f(X c ) Indicates the point of contraction X c The objective function, f(X h ) Indicates the worst point X h The objective function;

[0109] S68. If so, then all vertices will shrink toward the optimal point;

[0110] In this embodiment, when the reflection, expansion, and contraction operations fail to improve the process deviation value, a reduction operation is performed to narrow the search range, causing all vertices other than the optimal point in the sorting results to reduce their distance to the optimal point:

[0111]

[0112] Wherein, it represents the first i vertices , It is the best option. To reduce the coefficient, it can be set as follows: ;

[0113] S69. When not satisfied: At that time, perform the expansion operation: calculate the expansion point. X e and f(X e ) ; The optimal point of representation The objective function;

[0114] Find the reflection point. If the objective function value of the reflection point is better than the best point in the ranking result, then calculate the expansion point based on the dynamic centroid of the Simplex, the worst point in the ranking result, and the expansion coefficient. Expand the search area. If the objective function value of the expanded point meets the preset applicable conditions, then update the worst point as the expanded point.

[0115] Expansion: If the objective function value at the reflection point is better than the optimal point, then calculate the expansion point. Further expand the search area:

[0116]

[0117] wherein is an expansion coefficient, which can be set as, for example, If the objective function value of the expansion point is better, the worst point is updated as the expansion point;

[0118] S610, determine whether the following condition is met: f(X e ) f(X r ) ; wherein f(X e ) represents the objective function of the expansion point X e .

[0119] S611, if yes, replace the worst point with the expansion point X h

[0120] S612, if no, replace the worst point with the reflection point.

[0121] When the process deviation value is within the preset tolerance range or the maximum number of iterations is reached, the algorithm terminates and outputs the final tuning parameter combination.

[0122] Through the foregoing improvement, the multi-parameter space can be searched more efficiently to find the optimal tuning parameter combination. The dynamic calculation of the centroid avoids the inefficiency and local minimum problem that may be encountered in the traditional method, thereby improving the convergence speed and tuning accuracy of the algorithm.

[0123] In this embodiment, in order to evaluate the performance of the traditional method and the dynamic adaptive multi-parameter tuning optimization method provided by the present application in terms of efficiency, the average number of iterations for finding codes of 30 groups of samples is analyzed. The traditional method directly uses Nelder-Mead (simplex search method) for searching. The essence of this traditional method is to find the optimal solution through a geometric transformation, such as reflection, expansion, and contraction. Although this method is simple and easy to implement, it has obvious limitations: the centroid calculation only uses simple arithmetic mean and fails to utilize the trend information of the objective function, which is prone to fall into local optimum in high-dimensional complex space, resulting in low search efficiency. For example, Figure 3 ​​As shown, the iteration number of the conventional method is an orange column, and the iteration number of the method of the present application is a green column. The present application exhibits a lower iteration number on most samples. The average iteration number of the conventional method is 12.70, and the average iteration number of the method of the present application is 6.37. The average iteration number of the present application is reduced by 50% compared with the conventional method. The overall performance of the present application is significantly better than that of the conventional method.

[0124] Compared with the conventional method, the improvement of the present application mainly embodies in two aspects. First, the parameter dependence relationship identification is introduced before the search starts. For example, the strong correlation between chip trimming parameters can be quantified and identified by Spearman correlation analysis, so that the chip trimming parameters with strong correlation are divided into a group for joint and collaborative optimization, thereby improving the pertinence of the search from the root cause. The present application adopts a dynamic fitting centroid strategy in the search process, instead of the traditional static centroid calculation. The present application fits a deviation trend function through historical sample data, and then predicts and locates the centroid point closer to the global optimal region. This makes the subsequent reflection, expansion and other search operation directions more accurate, not only accelerating the convergence, but also having the strong ability to jump out of the local optimum, effectively overcoming the inherent defects of the conventional method.

[0125] In summary, the present application performs dynamic adaptive multi-parameter trimming optimization, aiming to solve the problems of insufficient dependence processing, low trimming efficiency and slow convergence speed in the existing trimming process. By introducing parameter dependence relationship identification and joint trimming mechanism, and using an adaptive search algorithm, the search starting point is dynamically adjusted, the search direction is optimized, the convergence of the trimming process is accelerated, and it is ensured that the trimming process can flexibly cope with the dynamic changes of process deviation, thereby improving the efficiency and accuracy of trimming.

[0126] The Spearman correlation adopted by the present application mainly investigates the strength of the monotonic relationship between variables, and does not depend on the specific distribution form of the data, and is suitable for processing various numerical data. The Spearman correlation coefficient is used to calculate the correlation coefficient between chip trimming parameters, which can effectively reveal the dependence relationship between chip trimming parameters.

[0127] The present application fits a quadratic deviation function through data driving, captures the change trend of the vertex process deviation value, and speculates the best position of the centroid, so as to effectively guide the search to focus on the potential optimal solution region, thereby optimizing the reflection, expansion and contraction operations.

[0128] The fitting process of the quadratic deviation function of the present application is simple, and can accurately capture the trend of the vertex process deviation value with the change of the parameter, which helps to reduce the iteration number and quickly guide the search to move towards the optimal value.

[0129] The position of the centroid is obtained by solving the minimum point of the fitted quadratic deviation function, which considers the influence weight of the current point and the distribution trend of the whole parameter space, and is helpful to jump out of the local optimum and approach the global optimum.

[0130] The application can search the multi-parameter space more efficiently and find the optimal tuning parameter combination.

[0131] The application solves the technical problems of mutual interference of parameters in the tuning process, complicated tuning process and large time consumption, difficulty in quickly finding the global optimal solution and lack of dynamic adaptability in the prior art.

[0132] The above examples are only used to illustrate the technical solutions of the application, but not to limit it; although the application has been described in detail with reference to the foregoing examples, those skilled in the art should understand that the technical solutions recorded in the foregoing examples can be modified or some technical features can be replaced by equivalents; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the application.

Claims

1. A dynamic adaptive multi-parameter trim optimization method, characterized in that, The method comprises: S1, correlation modeling of chip trimming parameters is performed through correlation analysis to obtain a high correlation parameter combination; S2, joint trimming is performed according to the high correlation parameter combination to obtain a joint trimming parameter group; S3, an initial vertex of Simplex search is set according to the joint trimming parameter group, and an initial trimming code combination is performed; wherein, the Simplex search is dynamically optimized; a quadratic deviation function is fitted through data driving to capture the change trend of process deviation values, to speculate the best position of the centroid, to guide the Simplex search, so that the Simplex search is focused on the potential optimal solution area; S4, process deviation function calculation is performed on the initial trimming code combination, the vertex process deviation values are sorted to obtain a sorting result; S5, an adaptive search algorithm is designed, a quadratic deviation function is fitted based on data driving, the search starting point is dynamically adjusted, the optimal trimming parameter set is searched, and a Simplex dynamic centroid is obtained; S6, according to the sorting result and the Simplex dynamic centroid, reflection, expansion and contraction operations are performed, and an applicable trimming parameter combination is iteratively searched.

2. The dynamic adaptive multi-parameter retuning optimization method of claim 1, wherein, In S1, Spearman correlation coefficient is used to calculate the correlation coefficient between the chip trimming parameters, a parameter correlation matrix is generated, the correlation analysis is performed to obtain the dependency relationship between the chip trimming parameters, and the high correlation parameter combination is obtained according to the dependency relationship.

3. The dynamic adaptive multi-parameter retuning optimization method of claim 2, wherein, A preset correlation threshold is used to determine the correlation degree between parameters according to the correlation coefficient between the chip trimming parameters, high correlation, indirect dependency parameters and low correlation parameters are obtained, parameter grouping is performed based on the parameter correlation matrix, the high correlation and the indirect dependency parameters are combined to obtain the high correlation parameter combination, and the low correlation parameters are independently trimmed and tested.

4. The dynamic adaptive multi-parameter retuning optimization method of claim 1, wherein, In S3, a problem modeling operation is performed; for the identified joint trimming parameter group, a trimming code corresponding to each chip trimming parameter is set; constructing the Simplex search of vertices, wherein each of the vertices represents a combination of different trim codes of different chip trim parameters in the joint trim parameter set; Based on the current set of joint trim parameters, the actual output of the circuit is calculated, and based on the actual output and the desired output, a process bias function is set: by adjusting the trim code of the chip trim parameters such that the actual output approaches the desired output, the process bias function is minimized .

5. The dynamic adaptive multi-parameter retuning optimization method of claim 1, wherein, In S4, according to the output corresponding to the first vertex and the expected output, the process deviation function calculation is performed on the initial trimming code combination to obtain the vertex process deviation value; the vertex process deviation values of the initial trimming code combination are combined to obtain a process deviation value combination, and the sorting operation is performed according to the process deviation value combination.

6. The dynamic adaptive multi-parameter retuning optimization method of claim 1, wherein, In S5, the quadratic deviation function is fitted, the mode of change of the vertex process deviation value with the parameter is predicted according to the current data point; the centroid calculation is performed, wherein, the position of the Simplex dynamic centroid in the Simplex search is obtained by solving the minimum point of the fitted quadratic deviation function.

7. The dynamic adaptive multi-parameter retuning optimization method of claim 1, wherein, In the contraction operation of S6, if the reflection or the expansion does not realize the improved solution operation, the contraction operation is performed, and a contraction point is calculated according to the Simplex dynamic centroid, the worst point in the sorting result and a contraction coefficient; If the objective function value of the contraction point is better than that of the worst point, the worst point is updated to the contraction point.

8. The dynamic adaptive multi-parameter retuning optimization method of claim 1, wherein, In the S6, when the reflection, the expansion, and the contraction operation do not achieve the process bias value improvement operation, then a reduction operation is performed to reduce the search range, and all vertices in the sorting result except the optimal point are reduced in distance to the optimal point.

9. The dynamic adaptive multi-parameter retuning optimization method of claim 1, wherein, In the S6, a reflection point is obtained, and if a target function value of the reflection point is better than an optimal point in the sorting result, an expansion point is calculated according to the Simplex dynamic centroid, the worst point in the sorting result, and an expansion coefficient , the search region is expanded, and if a target function value of the expansion point satisfies a preset applicable condition, the worst point is updated as the expansion point.

10. A dynamically adaptive multi-parameter trim optimization system, characterized by, The system comprises: a correlation analysis module configured to perform correlation modeling on chip trimming parameters through correlation analysis to obtain a high-correlation parameter combination; a joint trimming module configured to perform joint trimming according to the high-correlation parameter combination to obtain a joint trimming parameter group, wherein the joint trimming module is connected to the correlation analysis module; a dynamic optimization Simplex search module configured to set initial vertices of Simplex search according to the joint trimming parameter group to perform initial trimming code combination, wherein the Simplex search is dynamically optimized, a quadratic bias function is fitted through data-driven fitting to capture a change trend of a process bias value, an optimal position of a centroid is speculated to guide the Simplex search, and the Simplex search is concentrated in a potential optimal solution region, wherein the dynamic optimization Simplex search module is connected to the joint trimming module; a bias computer sorting module configured to perform process bias function calculation on the initial trimming code combination to calculate vertex process bias values and sort them to obtain a sorting result, wherein the bias computer sorting module is connected to the dynamic optimization Simplex search module; a quadratic bias function fitting module configured to design an adaptive search algorithm, fit a quadratic bias function based on data-driven fitting, dynamically adjust a search starting point, find an optimal trimming parameter set, and obtain a Simplex dynamic centroid; an iterative search module configured to perform reflection, expansion, and contraction operations according to the sorting result and the Simplex dynamic centroid to iteratively search for an applicable trimming parameter combination, wherein the iterative search module is connected to the quadratic bias function fitting module and the bias computer sorting module.

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