CN116595945B - High-precision simulation scattering parameter extraction method, electronic equipment and storage medium - Google Patents
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Abstract
The application discloses a high-precision simulation scattering parameter extraction method, electronic equipment and a storage medium, wherein the method comprises the following steps: acquiring various parameter information of a circuit board, constructing a plurality of samples corresponding to different transmission line lengths under each type of parameter information, and obtaining a sample set corresponding to each type of parameter information; acquiring original scattering parameters and reference scattering parameters corresponding to samples in each sample set; the original scattering parameters are obtained based on an equivalent circuit method, and the reference scattering parameters are obtained based on a finite element analysis method or through testing; obtaining an error set based on the relative errors of the original scattering parameters and the reference scattering parameters corresponding to at least part of the samples in each sample set on a plurality of frequency points; and classifying the error set to obtain a classification result of the relative error in each sample set, and carrying out regression on the classification result corresponding to each sample set to obtain regression parameters matched with each parameter information. According to the scheme, the accuracy of the scattering parameters obtained through simulation can be improved, and the generation efficiency is considered.
Description
Technical Field
The present application relates to the field of data processing technologies, and in particular, to a high-precision simulated scattering parameter extraction method, an electronic device, and a storage medium.
Background
The scattering parameter can describe the frequency domain characteristics of the transmission channel, so the scattering parameter is an important item of data corresponding to the transmission line in the circuit board, and the simulation is an important way to acquire the scattering parameter. In the prior art, an equivalent circuit method is generally utilized for simulation to improve the simulation efficiency, but the equivalent circuit method has the defect of insufficient precision. In view of this, how to improve the accuracy of the scattering parameters obtained by simulation and to achieve the generation efficiency at the same time has become a problem to be solved.
Disclosure of Invention
The application mainly solves the technical problem of providing a high-precision simulation scattering parameter extraction method, electronic equipment and a storage medium, which can improve the precision of scattering parameters obtained by simulation and give consideration to the generation efficiency.
In order to solve the above technical problems, a first aspect of the present application provides a high-precision simulated scattering parameter extraction method, including: acquiring various parameter information of a circuit board, and constructing a plurality of samples corresponding to different transmission line lengths under each parameter information to obtain a sample set corresponding to each parameter information; acquiring original scattering parameters and reference scattering parameters corresponding to the samples in each sample set; the original scattering parameters are obtained based on an equivalent circuit method, and the reference scattering parameters are obtained based on a finite element analysis method or through testing; obtaining an error set based on the relative errors of the original scattering parameters and the reference scattering parameters corresponding to at least part of the samples in each sample set on a plurality of frequency points; classifying the error set to obtain a classification result of the relative error in each sample set, and carrying out regression on the classification result corresponding to each sample set to obtain regression parameters matched with each parameter information; the regression parameters are used for adjusting the original scattering parameters corresponding to the circuit board with the matched parameter information.
To solve the above technical problem, a second aspect of the present application provides an electronic device, including: a memory and a processor coupled to each other, wherein the memory stores program data, and the processor invokes the program data to perform the method of the first aspect.
To solve the above technical problem, a third aspect of the present application provides a computer-readable storage medium having stored thereon program data which, when executed by a processor, implements the method described in the first aspect.
According to the scheme, multiple parameter information corresponding to the circuit board is obtained, multiple samples corresponding to different transmission line lengths are constructed under each parameter information, a sample set corresponding to each parameter information is obtained, original scattering parameters of the samples in each sample set are obtained based on an equivalent circuit method, and reference scattering parameters are obtained based on a finite element analysis method or through testing, wherein the reference scattering parameters are taken as true values, at least part of the samples are extracted from each sample set, error sets are obtained based on the original scattering parameters corresponding to the extracted samples and relative errors of the reference scattering parameters on multiple frequency points, the error sets are classified, so that classification results of the relative errors of the samples in each sample set on the multiple frequency points are obtained, the classification results corresponding to the sample sets with different transmission line lengths correspond to distribution conditions of the relative errors of the samples in the multiple frequency points, each sample set corresponds to the parameter information, regression is carried out on the classification results corresponding to each sample set, regression parameters matched with each parameter information are obtained, regression parameters matched with the parameter information are utilized, the regression parameters matched with the parameter information can be adjusted, and the accuracy of the regression parameters can be improved, namely, the simulation parameters can be obtained, and the accuracy of the simulation parameters can be better, and the accuracy of the simulation parameters can be obtained, and the simulation parameters are carried out, and the accuracy of the original parameters are obtained.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for the description of the embodiments will be briefly described below, and it is apparent that the drawings in the following description are only some embodiments of the present application, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art. Wherein:
FIG. 1 is a flow chart of an embodiment of a method for extracting high-precision simulated scattering parameters according to the present application;
FIG. 2 is a schematic flow chart of another embodiment of the high-precision simulated scattering parameter extraction method of the present application;
FIG. 3 is a schematic diagram of an embodiment of an electronic device according to the present application;
fig. 4 is a schematic diagram of a computer-readable storage medium according to an embodiment of the present application.
Detailed Description
The following description of the embodiments of the present application will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present application, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
The terms "system" and "network" are often used interchangeably herein. The term "and/or" is herein merely an association relationship describing an associated object, meaning that there may be three relationships, e.g., a and/or B, may represent: a exists alone, A and B exist together, and B exists alone. In addition, the character "/" herein generally indicates that the front and rear associated objects are an "or" relationship. Further, "a plurality" herein means two or more than two.
The high-precision simulation scattering parameter extraction method provided by the application is used for generating regression parameters matched with each parameter information, so that the regression parameters are used for adjusting the original scattering parameters obtained based on an equivalent circuit method, and the corresponding execution main body is a processor capable of calling data.
Referring to fig. 1, fig. 1 is a flow chart of an embodiment of a high-precision simulation scattering parameter extraction method according to the present application, the method includes:
s101: acquiring various parameter information of a circuit board, constructing a plurality of samples corresponding to different transmission line lengths under each parameter information, and obtaining a sample set corresponding to each parameter information.
Specifically, multiple parameter information corresponding to the circuit board is obtained, multiple samples corresponding to different transmission line lengths are constructed under each parameter information, and a sample set corresponding to each parameter information is obtained.
It will be appreciated that each sample set corresponds to one type of parameter information, and that each sample set includes a plurality of samples corresponding to different transmission line lengths.
In an application mode, multiple parameter information of a circuit board is obtained, initial length of a transmission line is obtained, multiple transmission line lengths with increasing lengths are generated from the initial length under each parameter information, multiple samples corresponding to each parameter information are obtained, and a sample set corresponding to each parameter information is formed.
In another application mode, multiple parameter information of the circuit board is obtained, multiple transmission line lengths with different lengths are randomly generated under each parameter information, multiple samples corresponding to each parameter information are obtained, and a sample set corresponding to each parameter information is formed.
Optionally, the parameter information includes multiple types of sub-parameters, where the multiple types of sub-parameters include at least impedance, delay, topology, electrical components, lamination number and board material of the circuit board, and at least one type of sub-parameters between every two types of parameter information are different from each other, so that multiple types of parameter information are traversed, so as to obtain regression parameters corresponding to each type of parameter information, and the regression parameters are used for adjusting original scattering parameters obtained based on an equivalent circuit method.
S102: and acquiring original scattering parameters and reference scattering parameters corresponding to the samples in each sample set, wherein the original scattering parameters are obtained based on an equivalent circuit method, and the reference scattering parameters are obtained based on a finite element analysis method or through testing.
Specifically, the original scattering parameters obtained by the equivalent circuit method based on the samples in each sample set and the reference scattering parameters obtained by the finite element analysis method or through testing are obtained, wherein the reference scattering parameters are taken as the true values.
In one application, an equivalent circuit method is used for determining original scattering parameters corresponding to samples in each sample set, and a finite element analysis method is used for determining reference scattering parameters corresponding to samples in each sample set.
In another application mode, an equivalent circuit method is utilized to determine original scattering parameters corresponding to the samples in each sample set, and the samples in each sample set are tested to obtain reference scattering parameters corresponding to each sample.
In another application mode, an equivalent circuit method is utilized to determine original scattering parameters corresponding to samples in each sample set, reference scattering parameters obtained by testing at least part of the samples are obtained, and a finite element analysis method is utilized to determine the reference scattering parameters corresponding to the remaining samples in each sample set.
In an application scenario, determining the original scattering parameters corresponding to the samples in each sample set by using the equivalent circuit method includes: and acquiring a frequency range corresponding to the circuit board, determining an equivalent circuit corresponding to the sample based on the length and the frequency range of the transmission line corresponding to the sample, wherein the equivalent circuit comprises a plurality of cascaded RLCG lumped models, determining transmission parameters (ABCD parameters) corresponding to each lumped model, generating a transmission parameter matrix based on the transmission parameters corresponding to all the lumped models, and converting the transmission parameter matrix into original scattering parameters.
Specifically, when the transmission line becomes a non-uniform transmission line due to crossing and bending, the transmission line is regarded as a distributed element, the distributed model can be approximately described by the lumped model, in order to make the bandwidth of the distributed model meet specific frequency, a frequency range corresponding to the circuit board is obtained, and the number of RLCG lumped models is determined based on the frequency range and the transmission line length corresponding to the sample, so as to obtain an equivalent circuit of cascade connection of a plurality of lumped models.
Further, the transmission parameters corresponding to each lumped model are obtained, the wave equation of the voltage and the current on the transmission line is deduced through the transmission line equation, and the relation between the voltage and the current and the RLCG is established, wherein the process is expressed as follows by using the formula:
（1）
（2）
and obtaining a second derivative, and deriving z to obtain the following relation:
（3）
（4）
substituting formula 2 into formula 3, substituting formula 1 into formula 4, and converting by Euler formula:
（5）
（6）
wherein,,，/>，/>representing the transmission coefficient of the transmission line>Representing the characteristic impedance of the transmission line, and further calculating the transmission parameters corresponding to each lumped model:
（7）
wherein,,representing the transmission line length.
It can be understood that, assuming that there are n lumped models, and any lumped model is defined as i, a transmission parameter matrix obtained after the transmission parameters of the lumped models are cascaded satisfies the following formula:
（8）
further, the transmission parameter matrix is converted into an original scattering parameter, taking 2 ports as an example, and the following relation is satisfied:
（9）
where S11 is the input port voltage reflection coefficient, S12 is the reverse voltage gain coefficient, S21 is the forward voltage gain coefficient, and S22 is the output port voltage reflection coefficient.
S103: and obtaining an error set based on the relative errors of the original scattering parameters and the reference scattering parameters corresponding to at least part of the samples in each sample set on a plurality of frequency points.
Specifically, at least part of samples are extracted from each sample set, and an error set is obtained based on the relative errors of the original scattering parameters and the reference scattering parameters corresponding to the extracted samples on a plurality of frequency points.
It should be noted that, the plurality of frequency points are a plurality of points distributed on a preset frequency range, and the original scattering parameter and the reference scattering parameter can describe frequency domain characteristics, so that a relative error between the original scattering parameter and the reference scattering parameter can be obtained on each frequency point, and the frequency points can be uniformly distributed or randomly distributed in the preset frequency range, for example, when the preset frequency range is 40GHz, 200 frequency points are defined every 10GHz, and a total of 800 frequency points are defined. Of course, in other scenarios, the preset frequency range and the distribution of the frequency points can be set in a self-defined manner according to the requirements, which is not particularly limited by the present application.
In one application mode, an error set is obtained based on the relative errors of the original scattering parameters and the reference scattering parameters corresponding to all samples in each sample set on a plurality of frequency points.
In another application mode, a part of samples are extracted from each sample set, and an error set is obtained based on the relative errors of the original scattering parameters and the reference scattering parameters corresponding to the extracted samples on a plurality of frequency points.
S104: and classifying the error set to obtain a classification result of the relative error in each sample set, and regressing the classification result corresponding to each sample set to obtain regression parameters matched with each type of parameter information, wherein the regression parameters are used for adjusting original scattering parameters corresponding to the circuit board with the matched parameter information.
Specifically, the error set is classified, so that a classification result of the relative error of each sample in each sample set corresponding to a plurality of frequency points is obtained.
It can be understood that the classification results corresponding to the sample sets correspond to the distribution conditions of the relative errors of the samples with different transmission line lengths on a plurality of frequency points, each sample set corresponds to parameter information, regression is performed on the classification results corresponding to each sample set, and thus regression parameters matched with each parameter information are obtained.
In an application mode, classifying the relative errors in each sample set in the error set by using a support vector machine to obtain a classification result of the relative errors in each sample set, and regressing the classification result corresponding to each sample set by using a support vector machine regression to obtain regression parameters matched with the parameter information corresponding to each sample set.
Specifically, the support vector machine is trained in advance, the trained support vector machine can generate a target hyperplane corresponding to each frequency point, the target hyperplane is used for carrying out two-classification on the relative error of the sample in each sample set at each frequency point, so that the classification result corresponding to each sample set comprises the distribution condition of the relative error of the sample in the sample set at a plurality of frequency points, regression is carried out on the classification result corresponding to the sample set by using the support vector machine regression to obtain regression parameters corresponding to each sample set, each sample set corresponds to one parameter information, and each parameter information is respectively matched with the regression parameters and is used for adjusting the original scattering parameters obtained based on the equivalent circuit method.
In another application mode, classifying the relative errors in each sample set in the error set by using a classification model to obtain a classification result of the relative errors in each sample set, and regressing the classification result corresponding to each sample set by using a regression model to obtain regression parameters matched with the parameter information corresponding to each sample set.
Specifically, the classification model comprises a neural network, the classification model is trained in advance, the trained classification model is used for classifying the relative errors of the samples in each sample set at each frequency point into an error interval, so that the classification result corresponding to each sample set comprises an error interval in which the relative errors of the samples in the sample set at a plurality of frequency points are located, the regression model is utilized to carry out regression on the classification result corresponding to the sample set, regression parameters corresponding to each sample set are obtained, each sample set corresponds to one parameter information, and each parameter information is respectively matched with the regression parameters and is used for adjusting the original scattering parameters obtained based on the equivalent circuit method.
It will be appreciated that the regression parameter is used to adjust the original scattering parameter to obtain an adjusted scattering parameter such that the relative error of the adjusted scattering parameter to the reference scattering parameter is less than the relative error of the original scattering parameter to the reference scattering parameter.
Furthermore, the regression parameters matched with the parameter information can be used for adjusting the original scattering parameters of the circuit board with the corresponding parameter information, so that after the original scattering parameters are obtained by simulation based on an equivalent circuit method, the regression parameters are used for adjusting, more accurate scattering parameters can be obtained, the accuracy of the scattering parameters obtained by simulation is improved, and the generation efficiency of the scattering parameters is considered.
According to the scheme, multiple parameter information corresponding to the circuit board is obtained, multiple samples corresponding to different transmission line lengths are constructed under each parameter information, a sample set corresponding to each parameter information is obtained, original scattering parameters of the samples in each sample set are obtained based on an equivalent circuit method, and reference scattering parameters are obtained based on a finite element analysis method or through testing, wherein the reference scattering parameters are taken as true values, at least part of the samples are extracted from each sample set, error sets are obtained based on the original scattering parameters corresponding to the extracted samples and relative errors of the reference scattering parameters on multiple frequency points, the error sets are classified, so that classification results of the relative errors of the samples in each sample set on the multiple frequency points are obtained, the classification results corresponding to the sample sets with different transmission line lengths correspond to distribution conditions of the relative errors of the samples in the multiple frequency points, each sample set corresponds to the parameter information, regression is carried out on the classification results corresponding to each sample set, regression parameters matched with each parameter information are obtained, regression parameters matched with the parameter information are utilized, the regression parameters matched with the parameter information can be adjusted, and the accuracy of the regression parameters can be improved, namely, the simulation parameters can be obtained, and the accuracy of the simulation parameters can be better, and the accuracy of the simulation parameters can be obtained, and the simulation parameters are carried out, and the accuracy of the original parameters are obtained.
Referring to fig. 2, fig. 2 is a flow chart of another embodiment of the high-precision simulation scattering parameter extraction method according to the present application, the method includes:
s201: obtaining multiple types of sub-parameters corresponding to the circuit board, and obtaining multiple parameter information of the circuit board based on random combination among the sub-parameters of each type, wherein the number of the sub-parameters of each type is at least one, and the multiple types of sub-parameters at least comprise impedance, time delay, topological structure, electric components, lamination number and board materials.
Specifically, multiple types of sub-parameters corresponding to the circuit board are obtained, one type of sub-parameters is selected from the multiple types of sub-parameters respectively to be combined, and multiple types of parameter information are obtained, wherein the multiple types of sub-parameters at least comprise impedance, delay, topological structure, electrical components, lamination quantity and board materials corresponding to the circuit board, so that multiple types of parameter information are traversed, and the quantity of the parameter information is the product of the quantities of all types of sub-parameters.
It will be appreciated that when enough parameter information is constructed, the corresponding parameter information of a conventional circuit board can be contained.
S202: and constructing a plurality of samples corresponding to the lengths of a plurality of incremental transmission lines under each type of parameter information to obtain a sample set corresponding to each type of parameter information.
Specifically, a plurality of incremental transmission line lengths are combined with each type of parameter information, a plurality of samples corresponding to each type of parameter information are determined, and a sample set corresponding to each type of parameter information is obtained.
Further, when the transmission line lengths have an increasing relation, the distribution rule of the relative errors of samples with different transmission line lengths corresponding to the same parameter information at different frequency points can be conveniently discovered later.
In an application scenario, constructing a plurality of samples corresponding to a plurality of incremental transmission line lengths under each kind of parameter information to obtain a sample set corresponding to each kind of parameter information, including: generating a plurality of incremental transmission line lengths according to a preset step length and combining the incremental transmission line lengths with each type of parameter information to obtain a plurality of samples corresponding to each type of parameter information; generating sample identifiers for a plurality of samples corresponding to each type of parameter information to obtain a sample set corresponding to each type of parameter information; wherein the sample identity is related to the parameter information and the transmission line length.
Specifically, a plurality of incremental transmission line lengths are generated according to a preset step length, so that the plurality of transmission line lengths have the characteristic of linear growth, the transmission line lengths are more regular, a plurality of transmission line length distributions are combined with each parameter information to obtain a plurality of samples corresponding to each parameter information, and the number of all the samples corresponds to the product between the number of all the parameter information and the number of the transmission line lengths.
Further, sample identifiers are generated for a plurality of samples corresponding to each type of parameter information, a plurality of samples corresponding to each type of parameter information and comprising the sample identifiers are obtained, wherein the sample identifiers corresponding to any two samples are mutually distinguished, so that each sample has unique identification information, the sample identifiers are related to the parameter information and the length of a transmission line, a sample set to which the sample belongs can be distinguished by using the sample identifiers, and the length of the transmission line corresponding to the sample can be marked by using the sample identifiers when the relative error corresponding to the sample is obtained later, and the classification accuracy is convenient to judge.
S203: and acquiring original scattering parameters and reference scattering parameters corresponding to the samples in each sample set, wherein the original scattering parameters are obtained based on an equivalent circuit method, and the reference scattering parameters are obtained based on a finite element analysis method or through testing.
Specifically, the original scattering parameters and the reference scattering parameters corresponding to the samples in each sample set are obtained, the original scattering parameters are obtained based on an equivalent circuit method, the original scattering parameters are used as the predicted values, the reference scattering parameters are obtained based on a finite element analysis method or through testing, and the reference scattering parameters are used as the true values.
S204: and obtaining an error set based on the relative errors of the original scattering parameters and the reference scattering parameters corresponding to at least part of the samples in each sample set on a plurality of frequency points.
Specifically, the original scattering parameter and the reference scattering parameter correspond to a plurality of parameter items, wherein the parameter items are positively correlated with the number of the port numbers, and for each parameter item, an error set is obtained based on the relative errors of the original scattering parameter and the reference scattering parameter corresponding to at least part of the samples in each sample set on a plurality of frequency points.
In an application mode, obtaining an error set based on the relative errors of the original scattering parameters and the reference scattering parameters corresponding to at least part of the samples in each sample set at a plurality of frequency points includes: taking samples with preset proportions in each sample set as a training set, and taking the remaining samples in each sample set as a test set; aiming at each parameter item in the training set, obtaining an error matrix corresponding to each parameter item on each frequency point based on the relative error of the original scattering parameter corresponding to the sample in each sample set and the reference scattering parameter on each frequency point; and obtaining an error set based on an error matrix corresponding to each parameter item on each frequency point.
Specifically, samples with preset proportions are extracted from each sample set to form a training set, the remaining samples in each sample set are taken as test sets, the samples in each sample set in the training set are taken as training samples, for each parameter item, based on the relative errors of the original scattering parameters and the reference scattering parameters corresponding to each training sample on each frequency point, an error matrix corresponding to each parameter item on each frequency point is obtained, all the error matrices are ordered according to the parameter items and the frequency points, and an error set is obtained, wherein each error matrix comprises all the samples in the training set, so that the relative errors of different parameter items on each frequency point can be classified and regressed conveniently.
It should be noted that, the error set is expressed as follows:
（10）
wherein,,represents a parameter item, j represents a port number, f represents the number of frequency points, in order +.>For example, a->The set of relative errors at the first frequency point of the S11 parameter term for all samples in the training set is represented as an error matrix.
Further, taking 2 ports as an example, the total of 800 frequency points, the error set is expressed as follows by using a formula:
（11）
optionally, k-fold cross validation is used to select samples with a preset proportion from each sample set to form a training set, and the remaining samples form a test set, where the preset proportion may be 80%, and the proportion of the test set is 20%, and of course, the preset proportion may be other customized values in other application scenarios, which is not particularly limited in the present application.
Further, before classifying the error set to obtain a classification result of the relative error in each sample set, the method further includes: and training and testing the support vector machine by using the training set and the testing set to obtain the trained support vector machine.
Specifically, the training set is used for training the support vector machine to classify the error set, the test set is used for testing the support vector machine, the trained support vector machine capable of accurately classifying the error set is obtained, and the accuracy of classifying the error set is improved.
In an application scenario, training and testing the support vector machine by using the training set and the testing set to obtain a trained support vector machine, including: each error matrix in the error set corresponding to the training set is respectively constructed as an input error item, the input error item is respectively input into a support vector machine, and an estimated hyperplane corresponding to each error matrix is generated; determining output parameters corresponding to all relative errors in each error matrix based on the relative errors in the error matrix and the error interval of the estimated hyperplane segmentation corresponding to the error matrix; the output parameter comprises a first numerical value and a second numerical value, wherein the first numerical value represents that an error interval where a relative error is located is correct, and the second numerical value represents that the error interval where the relative error is located is incorrect; adjusting parameters of the support vector machine based on the output parameters until convergence conditions are met; wherein the convergence condition is related to a ratio of the first value in the output parameter; and testing the support vector machine by using the test set to obtain the support vector machine with the training completed.
Specifically, each error matrix in the error set corresponding to the training set is respectively constructed as an input error item, each input error item is input to the support vector machine, so that the support vector machine generates a pre-estimated hyperplane corresponding to each error matrix, the input error item corresponding to each error matrix is classified, whether the error interval in which the relative error corresponding to each sample is located is correct or not is determined based on the relative error in the error matrix and the pre-estimated hyperplane segmented error interval corresponding to the error matrix, and output parameters corresponding to all the relative errors in each error matrix are obtained. The above procedure is formulated as follows:
（12）
wherein,,for inputting error items +.>Representing each error matrix in the error set E, respectively>For the output parameter, +1 corresponds to a first value, -1 corresponds to a second value.
Further, parameters of the support vector machine are adjusted based on the output parameters, so that the step of inputting the input error items to the support vector machine respectively and generating the estimated hyperplane corresponding to each error matrix is returned until the proportion of the first numerical value in the output parameters exceeds the proportion threshold value, the trained support vector machine is obtained, and the trained support vector machine is tested by using the test set, so that the support vector machine is more accurate.
It should be noted that, the error set is classified into a nonlinear problem, the support vector machine is a nonlinear support vector machine model, the parameters of the support vector machine are adjusted by using a particle swarm optimization algorithm, and the final classification decision function is determined:
（13）
wherein,,as a kernel function->For outputting parameters +.>Is Lagrangian multiplier +.>For displacement, the distance between the hyperplane and the origin is determined, and each error matrix corresponds to a respective classification decision function, so that each error matrix is accurately classified.
In an implementation scenario, each error matrix in the error set corresponding to the training set is respectively configured as an input error term, including: converting each relative error in each error matrix into two-dimensional data points; and taking all two-dimensional data points corresponding to the error matrix as input error items corresponding to the error matrix.
Specifically, two basic formats are corresponding to the frequency points, including real part and imaginary part formats, or amplitude and phase formats, each error matrix corresponds to one frequency point, each relative error in each error matrix is converted into two-dimensional data points according to the format, and then all the two-dimensional data points corresponding to the error matrix are used as input error items corresponding to the error matrix. The above procedure takes amplitude and phase as examples, and is expressed as follows:
（14）
where M represents the amplitude, P represents the phase, and the number of samples is n. Taking 2-port as an example, the input error term can be expressed as:
（15）
the relative error is converted into two-dimensional data points expressed by amplitude and phase, and the input error item is expressed by coordinates, so that the support vector machine can conveniently classify the input error item, and a more accurate hyperplane is generated.
S205: and classifying the error set to obtain a classification result of the relative error in each sample set, and regressing the classification result corresponding to each sample set to obtain regression parameters matched with each type of parameter information, wherein the regression parameters are used for adjusting original scattering parameters corresponding to the circuit board with the matched parameter information.
Specifically, after the trained support vector machine is obtained, each error matrix in the error set can be classified by using the trained support vector machine.
In an application scene, classifying each error matrix in the error set by using a trained support vector machine, generating a target hyperplane corresponding to each error matrix, and obtaining an error interval corresponding to each error matrix and segmented by the target hyperplane; and obtaining a classification result of the relative error in each sample set based on the error interval of the relative error in each sample set in all error matrixes.
Specifically, each error matrix in the error set is converted into two-dimensional data points, the two-dimensional data points corresponding to each error matrix are classified by using a trained support vector machine, a target hyperplane corresponding to each error matrix is generated, an error interval corresponding to each error matrix and divided by the target hyperplane is obtained, the error intervals where the relative errors in each sample set are located in all the error matrices are combined, and a classification result of the relative errors in each sample set is obtained, so that the classification result can represent the distribution condition of the relative errors corresponding to each parameter item of different samples corresponding to the same parameter information on a plurality of frequency points.
Further, regression is performed on the classification result corresponding to each sample set to obtain regression parameters matched with each parameter information, including: regression is carried out on the classification result corresponding to the sample set by utilizing the regression model according to the target error threshold value, and regression parameters matched with the parameter information are obtained; the classification result corresponding to each sample set corresponds to the regression model, the regression parameters are utilized to adjust the original scattering parameters to obtain adjusted scattering parameters, and the relative error between the adjusted scattering parameters and the corresponding reference scattering parameters is smaller than the target error threshold.
Specifically, regression is performed on a classification result corresponding to a sample set by using a regression model according to a target error threshold value to obtain regression parameters corresponding to the sample set, wherein the sample set corresponds to the parameter information, and a binding relationship between the regression parameters corresponding to the sample set and the parameter information is generated, so that regression parameters matched with the parameter information are obtained.
Further, the classification result corresponding to each sample set corresponds to a respective regression model, and is used for carrying out regression on the classification result corresponding to the sample set to obtain regression parameters, and the regression parameters are utilized to adjust the original scattering parameters to obtain adjusted scattering parameters, so that the relative error between the adjusted scattering parameters and the reference scattering parameters is smaller than the target error threshold, and the accuracy of the scattering parameters is improved.
In this embodiment, enough parameter information is constructed, multiple incremental transmission line lengths are combined with each parameter information, multiple samples corresponding to each parameter information are determined, a distribution rule of relative errors of samples with different transmission line lengths corresponding to the same parameter information at different frequency points is conveniently explored later, samples in a sample set are divided into a training set and a test set, an error matrix corresponding to each parameter item on each frequency point is constructed based on the training set, an error set composed of the error matrices is obtained, a support vector machine is trained and tested by using the training set and the test set, a support vector machine after training is obtained, each error matrix in the error set is classified by using the support vector machine, the method comprises the steps of obtaining an error interval which corresponds to each error matrix and is divided by a target hyperplane, combining error intervals in which relative errors in each sample set are located in all error matrices, and obtaining a classification result of the relative errors in each sample set, so that the classification result can represent distribution conditions of the relative errors corresponding to each parameter item of different samples corresponding to the same parameter information on a plurality of frequency points, regression is carried out on the classification result by using a regression model, regression parameters are obtained, the relative errors of the adjusted scattering parameters and the reference scattering parameters obtained after the original scattering parameters are adjusted by the regression parameters are smaller than a target error threshold, accuracy of the scattering parameters obtained through simulation is improved, and the advantage of high simulation efficiency of an equivalent circuit method is reserved.
Referring to fig. 3, fig. 3 is a schematic structural diagram of an electronic device according to an embodiment of the present application, the electronic device 30 includes a memory 301 and a processor 302 coupled to each other, wherein the memory 301 stores program data (not shown), and the processor 302 invokes the program data to implement the method in any of the above embodiments, and the description of the related content is referred to the detailed description of the above method embodiments and is not repeated herein.
Referring to fig. 4, fig. 4 is a schematic structural diagram of an embodiment of a computer readable storage medium 40 according to the present application, where the computer readable storage medium 40 stores program data 400, and the program data 400 when executed by a processor implements the method in any of the above embodiments, and details of the related content are described in the above embodiments, which are not repeated herein.
The units described as separate units may or may not be physically separate, and units displayed as units may or may not be physical units, may be located in one place, or may be distributed over a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the embodiment.
In addition, each functional unit in the embodiments of the present application may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units may be implemented in hardware or in software functional units.
The integrated units, if implemented in the form of software functional units and sold or used as stand-alone products, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present application may be embodied in essence or a part contributing to the prior art or all or part of the technical solution in the form of a software product stored in a storage medium, including several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) or a processor (processor) to execute all or part of the steps of the methods of the embodiments of the present application. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), a magnetic disk, or an optical disk, or other various media capable of storing program codes.
The foregoing description is only of embodiments of the present application, and is not intended to limit the scope of the application, and all equivalent structures or equivalent processes using the descriptions and the drawings of the present application or directly or indirectly applied to other related technical fields are included in the scope of the present application.
Claims (10)
1. The high-precision simulation scattering parameter extraction method is characterized by comprising the following steps of:
acquiring various parameter information of a circuit board, and constructing a plurality of samples corresponding to different transmission line lengths under each parameter information to obtain a sample set corresponding to each parameter information; the parameter information comprises a plurality of types of sub-parameters, wherein the plurality of types of sub-parameters at least comprise impedance, delay, topological structure, electric components, lamination quantity and plate materials of the circuit board, and at least one type of sub-parameters are distinguished between every two types of parameter information;
acquiring original scattering parameters and reference scattering parameters corresponding to the samples in each sample set; the original scattering parameters are obtained based on an equivalent circuit method, and the reference scattering parameters are obtained based on a finite element analysis method or through testing;
obtaining an error set based on the relative errors of the original scattering parameters and the reference scattering parameters corresponding to at least part of the samples in each sample set on a plurality of frequency points;
classifying the error set to obtain a classification result of the relative error in each sample set, and carrying out regression on the classification result corresponding to each sample set to obtain regression parameters matched with each parameter information; the classification result comprises distribution conditions of relative errors of samples with different transmission line lengths in the sample set on a plurality of frequency points, and the regression parameters are used for adjusting the original scattering parameters corresponding to the circuit board with the matched parameter information.
2. The method according to claim 1, wherein the original scattering parameters and the reference scattering parameters correspond to a plurality of parameter items, the obtaining the error set based on relative errors of the original scattering parameters and the reference scattering parameters corresponding to at least a part of the samples in each of the sample sets at a plurality of frequency points includes:
taking the samples with preset proportions in each sample set as a training set, and taking the remaining samples in each sample set as a test set;
aiming at each parameter item in the training set, obtaining an error matrix corresponding to each parameter item on each frequency point based on the relative error of the original scattering parameter corresponding to the sample and the reference scattering parameter on each frequency point in each sample set;
and obtaining the error set based on an error matrix corresponding to each parameter item on each frequency point.
3. The method for extracting high-precision simulation scattering parameters according to claim 2, wherein before classifying the error set to obtain the classification result of the relative error in each sample set, the method further comprises:
training and testing the support vector machine by using the training set and the testing set to obtain the trained support vector machine;
the classifying the error set to obtain a classification result of the relative error in each sample set includes:
classifying each error matrix in the error set by using the trained support vector machine, and generating a target hyperplane corresponding to each error matrix to obtain an error interval corresponding to each error matrix and segmented by the target hyperplane;
and obtaining a classification result of the relative error in each sample set based on the error interval of the relative error in all the error matrixes in each sample set.
4. The method for extracting high-precision simulation scattering parameters according to claim 3, wherein training and testing a support vector machine by using the training set and the testing set to obtain the trained support vector machine comprises:
each error matrix in the error set corresponding to the training set is respectively constructed as an input error item, the input error item is respectively input to the support vector machine, and an estimated hyperplane corresponding to each error matrix is generated;
determining output parameters corresponding to all the relative errors in each error matrix based on the relative errors in the error matrix and the segmented error intervals of the estimated hyperplane corresponding to the error matrix; the output parameter comprises a first numerical value and a second numerical value, wherein the first numerical value represents that the error interval where the relative error is located is correct, and the second numerical value represents that the error interval where the relative error is located is wrong;
adjusting the parameters of the support vector machine based on the output parameters until convergence conditions are met; wherein the convergence condition is related to a ratio of the first values in the output parameter;
and testing the support vector machine by using the test set to obtain the support vector machine with the training completed.
5. The method of claim 4, wherein said constructing each of the error matrices in the error set corresponding to the training set as an input error term comprises:
converting each of said relative errors in each of said error matrices into two-dimensional data points;
and taking all the two-dimensional data points corresponding to the error matrix as the input error items corresponding to the error matrix.
6. The method for extracting high-precision simulation scattering parameters according to claim 3, wherein the regression is performed on the classification result corresponding to each sample set to obtain regression parameters matched with each parameter information, comprising:
regression is carried out on the classification result corresponding to the sample set according to a target error threshold by using a regression model, so that regression parameters matched with the parameter information are obtained;
the classification result corresponding to each sample set corresponds to each regression model, the original scattering parameter is adjusted by using the regression parameters to obtain adjusted scattering parameters, and the relative error between the adjusted scattering parameters and the corresponding reference scattering parameters is smaller than the target error threshold.
7. The method for extracting high-precision simulation scattering parameters according to any one of claims 1 to 6, wherein the steps of obtaining multiple parameter information of a circuit board, constructing multiple samples corresponding to different transmission line lengths under each of the parameter information, and obtaining a sample set corresponding to each of the parameter information, include:
acquiring multiple types of sub-parameters corresponding to the circuit board, and acquiring multiple parameter information of the circuit board based on random combination among the sub-parameters of each type; wherein the number of each type of the sub-parameters is at least one, and the plurality of types of the sub-parameters at least comprise impedance, delay, topological structure, electrical components, lamination number and plate materials;
and constructing a plurality of samples corresponding to a plurality of incremental transmission line lengths under each type of parameter information, and obtaining a sample set corresponding to each type of parameter information.
8. The method for extracting high-precision simulation scattering parameters according to claim 7, wherein said constructing a plurality of samples corresponding to a plurality of incremental transmission line lengths for each of the parameter information, to obtain a sample set corresponding to each of the parameter information, comprises:
generating a plurality of incremental transmission line lengths according to a preset step length and combining the incremental transmission line lengths with each piece of parameter information to obtain a plurality of samples corresponding to each piece of parameter information;
generating sample identifiers for a plurality of samples corresponding to each piece of parameter information to obtain a sample set corresponding to each piece of parameter information; wherein the sample identity is related to the parameter information and the transmission line length.
9. An electronic device, comprising: a memory and a processor coupled to each other, wherein the memory stores program data that the processor invokes to perform the method of any of claims 1-8.
10. A computer readable storage medium having stored thereon program data, which when executed by a processor, implements the method of any of claims 1-8.
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Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US6295635B1 (en) * | 1998-11-17 | 2001-09-25 | Agilent Technologies, Inc. | Adaptive Multidimensional model for general electrical interconnection structures by optimizing orthogonal expansion parameters |
| US8386216B1 (en) * | 2009-12-17 | 2013-02-26 | Cadence Design Systems, Inc. | Method and system for adaptive modeling and simulation of lossy transmission lines |
| CN109596543A (en) * | 2018-11-25 | 2019-04-09 | 西安建筑科技大学 | The spectral reflectance recovery method of particle group optimizing multicore support vector regression |
| CN110110339A (en) * | 2018-01-29 | 2019-08-09 | 中国电力科学研究院有限公司 | A kind of hydrologic forecast error calibration method and system a few days ago |
| CN111898320A (en) * | 2020-08-06 | 2020-11-06 | 广州大学 | Radio frequency power amplifier scattering parameter extraction method and device based on neural network |
| CN115560759A (en) * | 2022-09-06 | 2023-01-03 | 哈尔滨工程大学 | Underwater multi-source navigation positioning method based on seabed oil and gas pipeline detection |
| CN115906741A (en) * | 2022-12-23 | 2023-04-04 | 杭州电子科技大学 | Radio frequency circuit optimization design method based on high-performance calculation |
Family Cites Families (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| ATE321273T1 (en) * | 2002-10-18 | 2006-04-15 | Cit Alcatel | SIGNAL PREPROCESSING TO DETERMINE THE PROPERTIES OF A TRANSMISSION LINE |
| US10459018B2 (en) * | 2016-05-06 | 2019-10-29 | Eight Ten Labs LLC | Method to remove the effects of LO drift from vector network analyzer measurements |
-
2023
- 2023-07-14 CN CN202310867318.9A patent/CN116595945B/en active Active
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US6295635B1 (en) * | 1998-11-17 | 2001-09-25 | Agilent Technologies, Inc. | Adaptive Multidimensional model for general electrical interconnection structures by optimizing orthogonal expansion parameters |
| US8386216B1 (en) * | 2009-12-17 | 2013-02-26 | Cadence Design Systems, Inc. | Method and system for adaptive modeling and simulation of lossy transmission lines |
| CN110110339A (en) * | 2018-01-29 | 2019-08-09 | 中国电力科学研究院有限公司 | A kind of hydrologic forecast error calibration method and system a few days ago |
| CN109596543A (en) * | 2018-11-25 | 2019-04-09 | 西安建筑科技大学 | The spectral reflectance recovery method of particle group optimizing multicore support vector regression |
| CN111898320A (en) * | 2020-08-06 | 2020-11-06 | 广州大学 | Radio frequency power amplifier scattering parameter extraction method and device based on neural network |
| CN115560759A (en) * | 2022-09-06 | 2023-01-03 | 哈尔滨工程大学 | Underwater multi-source navigation positioning method based on seabed oil and gas pipeline detection |
| CN115906741A (en) * | 2022-12-23 | 2023-04-04 | 杭州电子科技大学 | Radio frequency circuit optimization design method based on high-performance calculation |
Non-Patent Citations (4)
| Title |
|---|
| Y. Xu,et al..AN SUPPORT VECTOR REGRESSION BASED NONLINEAR MODELING METHOD FOR SIC MESFET.《Progress In Electromagnetics Research Letters》.2008,全文. * |
| 周期电磁结构的空间散射参数测量方法研究;宋志强;《中国优秀硕士学位论文全文数据库 基础科学辑》;全文 * |
| 基于SVR的高频场效应管等效电路参数提取;王旭;《中国优秀硕士学位论文全文数据库 信息科技辑》(第8期);第2-3节 * |
| 矢量网络分析仪MTRL校准技术研究;郭钊;《万方》;第7、10-11、20、37页 * |
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