CN112149372B - Device simulation model construction method, device, equipment and storage medium - Google Patents
Device simulation model construction method, device, equipment and storage medium Download PDFInfo
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Abstract
The application relates to a method, a device, equipment and a storage medium for constructing a device simulation model, and belongs to the technical field of device simulation. According to the method, through obtaining measured electrical characteristic change data of a device to be simulated, an initial simulation model of the device to be simulated is obtained, the initial simulation model comprises c key parameters for reflecting the electrical characteristics of the device to be simulated, an initial value of the c key parameters is updated and iterated by adopting a simulated annealing algorithm, in each updating and iterating process, simulation electrical characteristic change data of a current simulation model of the device to be simulated is obtained, and whether the value of the key parameters is updated is judged according to the similarity of the measured electrical characteristic change data and the simulation electrical characteristic change data; and obtaining a final simulation model of the simulation device according to the values of the c key parameters at the end of the simulated annealing algorithm. The method is used for solving the problems of long time, low efficiency and low accuracy of model parameters in the construction of the model in the existing device model simulation process.
Description
Technical Field
The present application relates to the field of device simulation technologies, and in particular, to a method, an apparatus, a device, and a storage medium for constructing a device simulation model.
Background
In the electromagnetic compatibility simulation project, the interference source device model is used as a main interference source of the whole system, has important significance for expressing the EMI characteristics of the system, the current device modeling mode mainly adopts a mathematical formula, and is exemplified by a simulation circuit simulator (Simulation program with integrated circuit emphasis, SPICE for short) modeling mode, the mathematical model of the device to be tested which needs to be modeled in the simulation circuit simulator is the existing model, and a designer can successfully model only by adding numerical values to key parameters of the device manually. However, these key parameter values often depend on a data manual of a device manufacturer, and the data of the data manual is incomplete due to technical confidentiality, so that the device is modeled by using the values in the data manual, and the obtained device model is inaccurate in terms of electrical characteristics and the like.
In the prior art, a circuit related to a device to be tested is firstly built, the characteristic waveform of the device is actually measured according to the circuit, and modeling parameters of the device are reversely pushed by the actually measured characteristic waveform, so that the accuracy of data sources of a device model can be ensured. However, the key parameters of the device model are closely related to each other, the mathematical formulas are complex and changeable, the reverse pushing process is very difficult, parameter values are set manually according to experience, parameter adjustment is repeated, the time is long, the efficiency is low, and the accuracy of the constructed model parameters is low.
Disclosure of Invention
The application provides a method, a device, equipment and a storage medium for constructing a device simulation model, which are used for solving the problems of long time, low efficiency and low accuracy of model parameters in the traditional device model simulation process.
In a first aspect, an embodiment of the present application provides a method for constructing a model for device simulation construction, where the method includes:
obtaining measured electrical characteristic change data of a device to be simulated, wherein the measured electrical characteristic change data is obtained by detecting the electrical characteristic change of the device to be simulated contained in a measured circuit;
acquiring an initial simulation model of the device to be simulated, wherein the initial simulation model comprises c key parameters for reflecting the electrical characteristics of the device to be simulated, and c is a positive integer;
updating and iterating initial values of the c key parameters by adopting a simulated annealing algorithm, acquiring simulation electrical characteristic change data of a current simulation model of the device to be simulated in each updating and iterating process, and judging whether the values of the key parameters are updated or not according to the similarity of the actually measured electrical characteristic change data and the simulation electrical characteristic change data;
And obtaining a final simulation model of the simulation device according to the values of the c key parameters at the end of the simulated annealing algorithm.
Optionally, obtaining measured electrical characteristic change data of the device to be simulated includes:
acquiring an actual measurement electrical characteristic change curve of the device to be simulated in the actual measurement circuit;
sampling the measured electrical characteristic change curve at equal intervals to obtain a measured discretization data sequence of the measured electrical characteristic change curve;
calculating the slope of an actual measurement curve of the position of each actual measurement discretization data according to the actual measurement discretization data sequence and the sampling interval;
and taking the actually measured discretization data sequence and the actually measured curve slope as the actually measured electrical characteristic change data.
Optionally, updating and iterating initial values of the c key parameters by using a simulated annealing algorithm, acquiring simulation electrical characteristic change data of a current simulation model of the device to be simulated in each updating and iterating process, and judging whether the values of the key parameters are updated according to the similarity of the actually measured electrical characteristic change data and the simulation electrical characteristic change data, including:
Randomly generating initial values of the c key parameters to obtain a current simulation model of the device to be simulated;
setting the current temperature value of the simulated annealing algorithm as an initial value, and setting a lowest temperature value, wherein the initial value of the current temperature value is larger than the lowest temperature value;
the one-time updating iterative process comprises the following steps:
acquiring first simulation electrical characteristic change data of the current simulation model, and calculating the total similarity of the first simulation electrical characteristic change data and the actually measured electrical characteristic change data to be used as a first similarity;
randomly changing the values of part of the c key parameters to obtain an alternative simulation model of the device to be simulated;
acquiring second simulation electrical characteristic change data of the alternative simulation model;
calculating the total similarity of the second simulation electrical characteristic change data and the actually measured electrical characteristic change data as a second similarity;
if the second similarity is greater than the first similarity, replacing the values of the key parameters in the current simulation model with the values of the key parameters in the alternative simulation model;
if the second similarity is smaller than the first similarity, calculating the probability of replacing the value of the key parameter in the current simulation model with the value of the key parameter in the alternative simulation model according to the first similarity, the second similarity and the current temperature;
If the probability is within a preset range, replacing the value of the key parameter in the current simulation model with the value of the key parameter in the alternative simulation model, and if the probability is not within the preset range, reserving the value of the key parameter in the current simulation model;
and reducing the current temperature value, judging whether the reduced current temperature value is larger than the minimum temperature value, if so, executing the next updating iteration process, otherwise, ending the updating iteration process.
Optionally, the updating iteration is performed on the initial values of the c key parameters by using a simulated annealing algorithm, in each updating iteration process, simulation electrical characteristic change data of a current simulation model of the device to be simulated is obtained, and according to the similarity between the actually measured electrical characteristic change data and the simulation electrical characteristic change data, whether the values of the key parameters are updated or not is judged, including:
randomly generating initial values of the c key parameters to obtain a current simulation model of the device to be simulated;
setting the current temperature value of the simulated annealing algorithm as an initial value, and setting a lowest temperature value, wherein the initial value of the current temperature value is larger than the lowest temperature value;
The one-time updating iterative process comprises the following steps:
acquiring first simulation electrical characteristic change data of the current simulation model, and calculating the total similarity of the first simulation electrical characteristic change data and the actually measured electrical characteristic change data to be used as a first similarity;
continuously randomly changing the values of part of the key parameters for S times to obtain S alternative simulation models;
setting the initial value of j to be 1;
performing the following parameter substitution procedure on the j-th alternative simulation model:
acquiring a j-th alternative simulation model, and acquiring second simulation electrical characteristic change data of the j-th alternative simulation model;
calculating the total similarity of the second simulation electrical characteristic change data and the actually measured electrical characteristic change data as a second similarity;
if the second similarity is greater than the first similarity, replacing the value of the key parameter in the current simulation model with the value of the key parameter in the j-th alternative simulation model;
if the second similarity is smaller than the first similarity, calculating the probability of replacing the value of the key parameter in the current simulation model with the value of the key parameter in the j-th alternative simulation model according to the first similarity, the second similarity and the current temperature;
If the probability is within a preset range, replacing the value of the key parameter in the current simulation model with the value of the key parameter in the j-th alternative simulation model, and if the probability is not within the preset range, reserving the value of the key parameter in the current simulation model;
judging whether j is smaller than S, if yes, after j=j+1 is updated, returning to execute the next updating and replacing process, otherwise, ending the updating and replacing process;
and reducing the current temperature value, judging whether the reduced current temperature value is larger than the minimum temperature value, if so, executing the next updating iteration process, and otherwise, ending the updating iteration process.
Optionally, the obtaining the first simulated electrical characteristic change data of the current simulation model includes:
building a simulation test circuit corresponding to the actual measurement circuit through a simulation circuit simulator;
acquiring a first simulation electrical characteristic change curve of the current simulation model in the simulation test circuit;
sampling the first simulation electrical characteristic change curve at equal intervals to obtain a first simulation discretization data sequence of the first simulation electrical characteristic change curve;
Calculating a first simulation curve slope of the position of each first simulation discretization data according to the first simulation discretization data sequence and the sampling interval;
taking the first simulation discretization data sequence and the first simulation curve slope as the first simulation electrical characteristic change data;
the obtaining second simulation electrical characteristic change data of the alternative simulation model includes:
acquiring a second simulation electrical characteristic change curve of the alternative simulation model in the simulation test circuit;
sampling the second simulation electrical characteristic change curve at equal intervals to obtain a second simulation discretization data sequence of the second simulation electrical characteristic change curve;
calculating a second simulation curve slope of the position of each second simulation discretization data according to the second simulation discretization data sequence and the sampling interval;
and taking the second simulation discretization data sequence and the second simulation curve slope as the second simulation electrical characteristic change data.
Optionally, the calculating the total similarity between the first simulated electrical characteristic change data and the measured electrical characteristic change data includes:
Obtaining the similarity of each first simulation discretization data and each actual measurement discretization data according to each first simulation discretization data, a first simulation curve slope of a position where each first simulation discretization data is located, each actual measurement discretization data and an actual measurement curve slope of a position where each actual measurement discretization data is located;
and calculating the sum of the similarity of each first simulation discretization data and the measured discretization data to obtain the total similarity of the first simulation electrical characteristic change data and the measured electrical characteristic change data.
Optionally, the calculating the total similarity of the second simulated electrical characteristic change data and the measured electrical characteristic change data includes, as a second similarity:
obtaining the similarity of each second simulation discretization data and each actual measurement discretization data according to each second simulation discretization data, the second simulation curve slope of the position of each second simulation discretization data, each actual measurement discretization data and the actual measurement curve slope of the position of each actual measurement discretization data;
and calculating the sum of the similarity of each second simulation discretization data and the actually measured discretization data to obtain the total similarity of the second simulation electrical characteristic change data and the actually measured electrical characteristic change data.
In a second aspect, an embodiment of the present application provides a device simulation model building apparatus, including:
the device comprises a measured data acquisition module, a measurement circuit and a measurement circuit, wherein the measured data acquisition module is used for acquiring measured electrical characteristic change data of a device to be simulated, and the measured electrical characteristic change data are obtained by detecting the electrical characteristic change of the device to be simulated contained in a measured circuit;
the initial model acquisition module is used for acquiring an initial simulation model of the device to be simulated, wherein the initial simulation model comprises c key parameters for reflecting the electrical characteristics of the device to be simulated, and c is a positive integer;
the updating module is used for updating and iterating initial values of the c key parameters by adopting a simulated annealing algorithm, acquiring simulation electrical characteristic change data of a current simulation model of the device to be simulated in each updating and iterating process, and judging whether the values of the key parameters are updated or not according to the similarity of the actually measured electrical characteristic change data and the simulation electrical characteristic change data;
and the final model acquisition module is used for acquiring a final simulation model of the simulation device according to the values of the c key parameters when the simulated annealing algorithm is finished.
In a third aspect, an embodiment of the present application provides an electronic device, including: the device comprises a processor, a communication interface, a memory and a communication bus, wherein the processor, the communication interface and the memory are communicated with each other through the communication bus;
the memory is used for storing a computer program;
the processor is configured to execute the program stored in the memory, and implement the method for constructing a device simulation model according to the first aspect.
In a fourth aspect, an embodiment of the present application provides a computer readable storage medium storing a computer program, where the computer program when executed by a processor implements the method for constructing a device simulation model according to the first aspect.
Compared with the prior art, the technical scheme provided by the embodiment of the application has the following advantages: according to the method provided by the embodiment of the application, the measured electrical characteristic change data of the device to be simulated and the initial simulation model of the device to be simulated are obtained, the initial values of the c key parameters in the initial simulation model are updated and iterated by adopting a simulated annealing algorithm, compared with the characteristic parameter values of the artificial repeated parameter adjustment determination model, the parameter adjustment time is reduced, the efficiency of device model construction is improved, and the value of the key parameter with the highest similarity with the measured electrical characteristic change data is determined according to the similarity of the measured electrical characteristic change data and the simulated electrical characteristic change data, so that the accuracy of the device simulation model is ensured.
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The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the application and together with the description, serve to explain the principles of the application.
In order to more clearly illustrate the embodiments of the application or the technical solutions of the prior art, the drawings which are used in the description of the embodiments or the prior art will be briefly described, and it will be obvious to a person skilled in the art that other drawings can be obtained from these drawings without inventive effort.
FIG. 1 is a schematic flow chart of a method for constructing a device simulation model according to an embodiment of the present application;
FIG. 2 is a flow chart of a specific implementation process for determining whether values of key parameters are updated according to the second embodiment of the present application;
FIG. 3 is a flowchart of a third embodiment of the present application for determining whether a value of a key parameter is updated in a specific implementation process;
FIG. 4 is a flowchart illustrating a specific implementation process for obtaining first simulation electrical characteristic variation data of a current simulation model according to a fourth embodiment of the present application;
FIG. 5 is a flowchart illustrating a process for obtaining second simulation electrical characteristic variation data of an alternative simulation model according to a fourth embodiment of the present application;
FIG. 6 is a schematic structural diagram of a device simulation model building apparatus according to a fifth embodiment of the present application;
fig. 7 is a schematic structural diagram of an electronic device according to a sixth embodiment of the present application.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present application more apparent, the technical solutions of the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present application, and it is apparent that the described embodiments are some embodiments of the present application, but not all embodiments of the present application. 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 simulated annealing algorithm is derived from a solid annealing principle, and is a random optimization algorithm based on a Monte Carlo simulation (Monte Carlo simulation, also called random sampling) iterative solution strategy. The simulated annealing algorithm starts from a certain higher initial temperature, and randomly searches for a global optimal solution of an objective function in a solution space along with continuous decline of temperature parameters and combines the probability kick characteristic, namely after obtaining a local optimal solution, continuously iterating and solving with a certain probability to accept a solution worse than the current local optimal solution and search for the global optimal solution, thereby jumping out of the local optimal solution and finally tending to global optimal.
In the embodiment of the application, the simulated annealing algorithm is used in the construction process of the device simulation model so as to find the global optimal solution of the device simulation model and solve the problems of long time, low efficiency and low accuracy of model parameters in the construction process of the existing device model.
Example 1
An embodiment of the present application provides a method for constructing a device simulation model, as shown in fig. 1, where the method includes:
s101, obtaining actually-measured electrical characteristic change data of a device to be simulated, wherein the actually-measured electrical characteristic change data are obtained by detecting the electrical characteristic change of the device to be simulated, which is contained in an actually-measured circuit.
An actual measurement circuit (actually measured circuit for short) of the device to be simulated is built, actually measured data in the actually measured circuit is obtained, the actually measured data are data representing the change of the electrical characteristics of the device to be simulated, and the change data of the electrical characteristics of the device to be simulated are obtained according to the actually measured data.
The device to be simulated may be a common interference source device in electromagnetic compatibility simulation, such as a Metal-Oxide-Semiconductor Field-Effect Transistor (MOSFET for short). The electrical characteristic change data may include voltage magnitudes, current magnitudes, etc. corresponding to each point in time of the aggressor device.
In some embodiments, obtaining measured electrical characteristic change data of a device to be simulated includes:
obtaining an actual measurement electrical characteristic change curve of a device to be simulated in an actual measurement circuit; sampling the measured electrical characteristic change curve at equal intervals to obtain a measured discretization data sequence of the measured electrical characteristic change curve; calculating the slope of an actual measurement curve of the position of each actual measurement discretization data according to the actual measurement discretization data sequence and the sampling interval; and taking the actually measured discretization data sequence and the actually measured curve slope as actually measured electrical characteristic change data.
The equidistant sampling is to divide and set interval acquisition data for the abscissa axis of the actually measured electrical characteristic change curve.
For example, the electrical characteristic change curve of the disturbance source device may be a voltage amplitude change curve with time as the axis of abscissa, or may be a current amplitude change curve with time as the axis of abscissa, and the type of curve is not limited in this embodiment, and may be set according to the electrical characteristic of the device to be simulated.
The process of obtaining measured electrical characteristic change data is described below by taking a device to be simulated as an interference source device as an example:
and acquiring actual measurement voltage waveform data of the interference source device in the actual measurement circuit, and drawing a change curve of the actual measurement voltage amplitude according to the actual measurement voltage waveform data.
In a preset time, sampling the time of a change curve of the actually measured voltage amplitude at equal intervals to obtain the actually measured voltage amplitude corresponding to each sampling point, obtaining each actually measured discretization data, calculating the actually measured voltage amplitude of each sampling point and the actually measured voltage amplitude increment corresponding to the interval time of the sampling point, and obtaining the actually measured curve slope of each sampling point, wherein the calculation formula is as follows:
where d represents time, represents Δd interval time, i.e., sampling interval, v (d) represents voltage amplitude corresponding to time point d, and v (d+Δd) represents voltage amplitude corresponding to time point d+Δd.
And taking the measured voltage amplitude and the measured curve slope of each sampling point as measured electrical characteristic change data of the interference source device within a preset time.
S102, acquiring an initial simulation model of the device to be simulated, wherein the initial simulation model comprises c key parameters for reflecting the electrical characteristics of the device to be simulated, and c is a positive integer.
Constructing a simulation test circuit consistent with the actual measurement circuit through a simulation circuit simulator, and constructing an initial simulation model of a device to be simulated in the simulation test circuit by adopting a circuit-level simulation program (Simulation program with integrated circuit emphasis, SPICE for short) language, wherein the initial simulation model comprises c key parameters reflecting the characteristics of the device to be simulated.
Taking a MOSFET as an example, the key parameters in the initial simulation model of the MOSFET constructed by SPICE language include: channel length L, channel width W, transconductance KP, etc. And c, marking the number of all the key parameters as c, and sequentially setting an initial value for each key parameter, wherein the initial value is a value in a value range set by people according to the device characteristics of the device to be simulated.
And S103, updating and iterating initial values of the c key parameters by adopting a simulated annealing algorithm, acquiring simulated electrical characteristic change data of a current simulation model of the device to be simulated in each updating and iterating process, and judging whether the values of the key parameters are updated or not according to the similarity of the actually measured electrical characteristic change data and the simulated electrical characteristic change data.
S104, obtaining a final simulation model of the simulation device according to the values of the c key parameters when the simulated annealing algorithm is finished.
According to the embodiment, the actually measured electrical characteristic change data of the device to be simulated and the initial simulation model of the device to be simulated are obtained by establishing an actually measured circuit of the device to be simulated, the initial values of c key parameters in the initial simulation model are updated and iterated by adopting a simulated annealing algorithm, compared with the characteristic parameter values of a manually repeated parameter adjustment determination model, the parameter adjustment time is shortened, the efficiency of device model construction is improved, and the value of the key parameter with the highest similarity with the actually measured electrical characteristic change data is determined according to the similarity of the actually measured electrical characteristic change data and the simulated electrical characteristic change data of the actually measured circuit by establishing the actually measured circuit of the device to be simulated, so that the accuracy of the device simulation model is ensured.
Example two
In this embodiment, as shown in fig. 2, the specific implementation process of S103 is described, and the updating iteration is performed on the initial values of c key parameters by using a simulated annealing algorithm, and in each updating iteration process, simulated electrical characteristic change data of a current simulation model of a device to be simulated is obtained, and according to similarity between actually measured electrical characteristic change data and simulated electrical characteristic change data, whether the values of the key parameters are updated is determined, which specifically includes:
s201, randomly generating initial values of c key parameters to obtain a current simulation model of the device to be simulated.
S202, setting the current temperature value of the simulated annealing algorithm as an initial value, and setting the lowest temperature value, wherein the initial value of the current temperature value is larger than the lowest temperature value.
The one-time updating iterative process comprises the following steps:
s203, obtaining first simulation electrical characteristic change data of the current simulation model, and calculating the total similarity of the first simulation electrical characteristic change data and the actually measured electrical characteristic change data to serve as the first similarity.
S204, randomly changing the values of part of the key parameters in the c key parameters to obtain an alternative simulation model of the device to be simulated.
S205, second simulation electrical characteristic change data of the alternative simulation model are obtained, and the total similarity of the second simulation electrical characteristic change data and the actually measured electrical characteristic change data is calculated and used as the second similarity.
S206, judging whether the second similarity is larger than the first similarity, if so, executing S207, otherwise, executing S208.
S207, replacing the values of the key parameters in the current simulation model with the values of the key parameters in the alternative simulation model.
S208, calculating the probability of replacing the value of the key parameter in the current simulation model with the value of the key parameter in the alternative simulation model according to the first similarity, the second similarity and the current temperature.
S209, judging whether the probability is within a preset range, if so, executing S210, otherwise, executing S211.
S210, replacing the values of the key parameters in the current simulation model with the values of the key parameters in the alternative simulation model.
S211, reserving values of key parameters in the current simulation model.
S212, reducing the current temperature value, judging whether the reduced current temperature value is larger than the minimum temperature value, if so, returning to S204, executing the next updating iteration process, otherwise, executing S213.
S213, updating the iteration process to be finished, and outputting the values of the key parameters in the current simulation model.
Specifically, the current temperature value is reduced at a rate, expressed as:
t=rt, where T represents a current temperature value, R represents a rate of decrease of the current temperature, and the value of R is a real number greater than 0 and less than 1.
In some embodiments, calculating the probability of replacing the value of the key parameter in the current simulation model with the value of the key parameter in the alternative simulation model in S207 according to the first similarity, the second similarity and the current temperature specifically includes:
calculating a difference between the second similarity and the first similarity, the difference being formulated as:
dE＝Y(i+1)-Y(i)
wherein dE represents a difference between the second similarity and the first similarity, Y (i+1) represents the second similarity, and Y (i) represents the first similarity.
According to the difference value and the current temperature value, calculating to obtain the probability of replacing the value of the key parameter in the current simulation model with the value of the key parameter in the alternative simulation model, wherein the calculation of the probability can be expressed as follows:
where dE represents the difference between the second similarity and the first similarity, m represents a constant, and T represents the current temperature value.
Specifically, whether the probability is within a preset range of more than 0 and less than 1 is judged, and if yes, the value of the key parameter in the current simulation model is replaced by the value of the key parameter in the alternative simulation model. Otherwise, the values of the key parameters in the current simulation model are preserved.
Example III
In this embodiment, as shown in fig. 3, a simulation annealing algorithm is used to update and iterate initial values of c key parameters, in each update and iterate process, simulation electrical characteristic change data of a current simulation model of a device to be simulated is obtained, and according to similarity between actually measured electrical characteristic change data and simulation electrical characteristic change data, whether the value of the key parameter is updated is determined, which specifically includes:
S301, randomly generating initial values of c key parameters to obtain a current simulation model of the device to be simulated.
S302, setting the current temperature value of the simulated annealing algorithm as an initial value, and setting the lowest temperature value, wherein the initial value of the current temperature value is larger than the lowest temperature value.
The one-time updating iterative process comprises the following steps:
s303, acquiring first simulation electrical characteristic change data of the current simulation model, and calculating the total similarity of the first simulation electrical characteristic change data and the actually measured electrical characteristic change data to serve as the first similarity.
S304, randomly changing the values of part of the key parameters in the c key parameters for S times continuously to obtain S alternative simulation models.
Setting the initial value of j to be 1;
the following parameter substitution procedure is performed on the j-th alternative simulation model:
s305, acquiring a j-th alternative simulation model, acquiring second simulation electrical characteristic change data of the j-th alternative simulation model, and calculating the total similarity of the second simulation electrical characteristic change data and the actually measured electrical characteristic change data to serve as a second similarity.
S306, judging whether the second similarity is larger than the first similarity. If yes, execution proceeds to S307, otherwise execution proceeds to S308.
S307, replacing the value of the key parameter in the current simulation model with the value of the key parameter in the j-th alternative simulation model.
S308, calculating the probability of replacing the value of the key parameter in the current simulation model with the value of the key parameter in the j-th alternative simulation model according to the first similarity, the second similarity and the current temperature value.
S309, judging whether the probability is within a preset range, if so, executing S310, otherwise, executing S311.
S310, replacing the value of the key parameter in the current simulation model with the value of the key parameter in the j-th alternative simulation model.
S311, reserving the values of key parameters in the current simulation model.
S312, judging whether j is smaller than S, if yes, executing S313, otherwise executing S314.
S313, after updating j=j+1, the process returns to S305 to execute the next update replacement process.
And S314, ending the updating and replacing process, and determining the values of the key parameters in the current simulation model.
S315, the current temperature value is reduced, whether the reduced current temperature value is larger than the minimum temperature value is judged, if yes, the next updating iteration process is executed, and otherwise, S316 is executed.
S316, updating the iteration process to be finished, and outputting the values of the key parameters in the current simulation model.
According to the method, the values of part of the key parameters in the c key parameters are changed randomly for many times, a plurality of alternative simulation models are obtained in a one-time updating iterative process, the similarity between each alternative simulation model and the actually measured simulation model is obtained in advance, the similarity between the current simulation model and the alternative simulation model is compared in a linear mode, whether the values of the key parameters of the alternative simulation model are used for replacing the values of the key parameters of the current simulation model is determined, the time for establishing the alternative simulation model can be greatly reduced, the time for determining the values of the key parameters in the simulation model is reduced, and the construction effect of the simulation device model is further improved.
Example IV
The present embodiment is a specific description of the process of acquiring the first simulation electrical characteristic change data of the current simulation model in S203 and the process of acquiring the second simulation electrical characteristic change data of the alternative simulation model in S205 in the second embodiment.
As shown in fig. 4, in this embodiment, a specific process for obtaining first simulation electrical characteristic change data of a current simulation model includes:
s401, constructing a simulation test circuit corresponding to the actual measurement circuit through a simulation circuit simulator.
S402, acquiring a first simulation electrical characteristic change curve of a current simulation model in a simulation test circuit.
S403, sampling the first simulation electrical characteristic change curve at equal intervals to obtain a first simulation discretization data sequence of the first simulation electrical characteristic change curve.
S404, calculating a first simulation curve slope of the position of each first simulation discretization data according to the first simulation discretization data sequence and the sampling interval.
S405, the first simulation discretization data sequence and the slope of the first simulation curve are used as first simulation electrical characteristic change data.
Fig. 5 is a flowchart of a specific implementation process of obtaining second simulation electrical characteristic change data of the alternative simulation model, as shown in fig. 5, in this embodiment, the specific process of obtaining second simulation electrical characteristic change data of the alternative simulation model includes:
S501, obtaining a second simulation electrical characteristic change curve of an alternative simulation model in the simulation test circuit.
S502, sampling the second simulation electrical characteristic change curve at equal intervals to obtain a second simulation discretization data sequence of the second simulation electrical characteristic change curve.
S503, calculating a second simulation curve slope of the position of each second simulation discretization data according to the second simulation discretization data sequence and the sampling interval.
S504, using the second simulation discretization data sequence and the second simulation curve slope as second simulation electrical characteristic change data.
In some embodiments, after obtaining the first simulation electrical characteristic change data of the current simulation model, calculating the total similarity between the first simulation electrical characteristic change data and the actually measured electrical characteristic change data, where the specific process for using the total similarity as the first similarity includes:
according to each first simulation discretization data, a first simulation curve slope of a position where each first simulation discretization data is located, each actually measured discretization data and an actually measured curve slope of a position where each actually measured discretization data is located, obtaining the similarity between each first simulation discretization data and each actually measured discretization data, wherein the calculation formula is as follows:
Wherein, the initial value of i is 1, Y (i, d) represents the similarity between the first simulation discretization data of the sampling point d and the actually measured discretization data in the ith updating iterative process; k (K) 1 ，K 2 Represents the weight coefficient, and generally K1 is more than or equal to 10K 2 ，k 1 (dN) represents the slope of the first simulation curve of the position of the first simulation discretization data of the sampling point d in the ith updating iteration process, and k (d) represents the slope v of the actual measurement curve of the position of the actual measurement discretization data of the sampling point d in the ith updating iteration process 1 (dN) represents the first simulated discretized data of the sampling point d in the ith updating iteration process, and v (d) represents the actually measured discretized data of the sampling point d in the ith updating iteration process.
Calculating the sum of the similarity of each first simulation discretization data and the actually measured discretization data to obtain the total similarity of the first simulation electrical characteristic change data and the actually measured electrical characteristic change data, wherein the calculation of the total similarity can be expressed as follows by a formula:
Y(i)＝Y(i，0)+Y(i，1)+Y(i，2)+......+Y(i，d)
wherein Y (i) represents the total similarity between the first simulated electrical characteristic change data and the actually measured electrical characteristic change data of the sampling point 0 to the sampling point d in the ith updating iteration process, Y (i, 0) represents the similarity between the first simulated discretization data and each actually measured discretization data of the coordinate origin of the first simulated electrical characteristic change curve in the ith updating iteration process, Y (i, 1) represents the similarity between the first simulated discretization data and the actually measured discretization data of the sampling point 1 in the ith updating iteration process, Y (i, 2) represents the similarity between the first simulated discretization data and the actually measured discretization data of the sampling point 2 in the ith updating iteration process, and Y (i, d) represents the similarity between the first simulated discretization data and the actually measured discretization data of the sampling point d in the ith updating iteration process.
In some embodiments, after obtaining the second simulation electrical characteristic change data of the alternative simulation model, calculating the total similarity of the second simulation electrical characteristic change data and the measured electrical characteristic change data, where the specific process as the second similarity includes:
and obtaining the similarity of each second simulation discretization data and each actual measurement discretization data according to each second simulation discretization data, the second simulation curve slope of the position of each second simulation discretization data, each actual measurement discretization data and the actual measurement curve slope of the position of each actual measurement discretization data.
Wherein Y (i, d) represents the similarity between the second simulation discretization data of the sampling point d and the actually measured discretization data in the ith updating iteration process; k (K) 1 ，K 2 Represents the weight coefficient, generally K1 is more than or equal to 10K2, K 2 (dN) represents the second simulation curve slope of the position of the second simulation discretization data of the sampling point d in the ith updating iteration process, k (d) represents the actual measurement curve slope of the position of the actual measurement discretization data of the sampling point d in the ith updating iteration process, v 2 (dN) represents the second simulated discrete of the sampling point dThe converted data, v (d), represents the measured discretized data of the sampling point d.
Calculating the sum of the similarity of each second simulation discretization data and the actually measured discretization data to obtain the total similarity of the second simulation electrical characteristic change data and the actually measured electrical characteristic change data, wherein the calculation formula is as follows:
Y(i+1)＝Y(i+1，0)+Y(i+1，1)+Y(i+1，2)+......+Y(i+1，d)
wherein Y (i+1) represents the total similarity between the second simulated electrical characteristic change data and the actually measured electrical characteristic change data from the sampling point 0 to the sampling point d in the ith updating iteration process, Y (i+1, 0) represents the similarity between the second simulated discretization data and the actually measured discretization data of the coordinate origin of the second simulated electrical characteristic change curve of the sampling point 0 in the ith updating iteration process, Y (i+1, 1) represents the similarity between the second simulated discretization data and the actually measured discretization data of the sampling point 1 in the ith updating iteration process, Y (i+1, 2) represents the similarity between the second simulated discretization data and the actually measured discretization data of the sampling point 2 in the ith updating iteration process, and Y (i+1, d) represents the similarity between the second simulated discretization data and each actually measured discretization data of the sampling point d in the ith updating iteration process.
According to the embodiment, the similarity of the actually measured electrical characteristic change data and the simulated electrical characteristic change data is obtained, the value of the key parameter with the highest similarity with the actually measured electrical characteristic change data is determined, and the accuracy of the source of the simulated data is ensured, so that the accuracy of a simulation model of the device is ensured.
Example five
Based on the same concept, in the fifth embodiment of the present application, a device simulation model building apparatus is provided, and the specific implementation of the apparatus may be referred to the description of the embodiment of the method, and the repetition is omitted, as shown in fig. 6, where the apparatus mainly includes:
the measured data obtaining module 601 is configured to obtain measured electrical characteristic change data of the device to be simulated, where the measured electrical characteristic change data is obtained by detecting an electrical characteristic change of the device to be simulated included in the measured circuit.
The initial model obtaining module 602 is configured to obtain an initial simulation model of the device to be simulated, where the initial simulation model includes c key parameters for reflecting electrical characteristics of the device to be simulated, and c is a positive integer.
And the updating module 603 is configured to update and iterate initial values of the c key parameters by using a simulated annealing algorithm, obtain simulated electrical characteristic change data of a current simulation model of the device to be simulated in each updating and iterating process, and determine whether the values of the key parameters are updated according to the similarity between the actually measured electrical characteristic change data and the simulated electrical characteristic change data.
The final model obtaining module 604 obtains a final simulation model of the simulation device according to the values of the c key parameters at the end of the simulated annealing algorithm.
Example six
Based on the same concept, the embodiment of the application also provides an electronic device, as shown in fig. 7, where the electronic device mainly includes: the processor 701, the communication interface 702, the memory 703 and the communication bus 704, wherein the processor 701, the communication interface 702 and the memory 703 complete communication with each other through the communication bus 704. The memory 703 stores a program executable by the processor 701, and the processor 701 executes the program stored in the memory 703 to implement the steps of the method for constructing a device simulation model described in the above embodiment.
The communication bus 704 mentioned in the above electronic device may be a peripheral component interconnect standard (Peripheral Component Interconnect, abbreviated to PCI) bus or an extended industry standard architecture (Extended Industry Standard Architecture, abbreviated to EISA) bus, or the like. The communication bus 704 may be divided into an address bus, a data bus, a control bus, and the like. For ease of illustration, only one thick line is shown in fig. 7, but not only one bus or one type of bus.
The communication interface 702 is used for communication between the electronic device and other devices described above.
The memory 703 may include random access memory (Ranndom Access Memory, RAM) or may include non-volatile memory (non-volatile memory), such as at least one disk memory. Optionally, the memory may also be at least one memory device located remotely from the aforementioned processor 701.
The processor 701 may be a general-purpose processor including a central processing unit (Central Processing Unit, CPU), a network processor (network Processor, nP), a digital signal processor (Digital Signal Processing, DSP), an application specific integrated circuit (Application Specific Integrated Circuit, ASIC), a Field programmable gate array (Field-Programmable Gate Array, FPGA), or other programmable logic device, discrete gate or transistor logic device, or discrete hardware components.
In a further embodiment of the present application, there is also provided a computer-readable storage medium having stored therein a computer program which, when run on a computer, causes the computer to perform the steps of the method for constructing a device simulation model described in the above embodiments.
In the above embodiments, it may be implemented in whole or in part by software, hardware, firmware, or any combination thereof. When implemented in software, may be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer instructions are loaded and executed on a computer, the processes or functions described in accordance with embodiments of the present application are produced in whole or in part. The computer may be a general purpose computer, a special purpose computer, a computer network, or other programmable apparatus. The computer instructions may be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another computer-readable storage medium, for example, by a wired (e.g., coaxial cable, optical fiber, digital Subscriber Line (DSL)), or wireless (e.g., infrared, microwave, etc.) means from one website, computer, server, or data center to another. The computer readable storage medium may be any available medium that can be accessed by a computer or a data storage device such as a server, data center, etc. that contains an integration of one or more available media. The usable medium may be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape, etc.), an optical medium (e.g., DVD), or a semiconductor medium (e.g., solid state disk), etc.
It should be noted that in this document, relational terms such as "first" and "second" and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
The foregoing is only a specific embodiment of the invention to enable those skilled in the art to understand or practice the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the invention. Thus, the present invention is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims (9)
1. The method for constructing the device simulation model is characterized by comprising the following steps of:
obtaining measured electrical characteristic change data of a device to be simulated, wherein the measured electrical characteristic change data is obtained by detecting the electrical characteristic change of the device to be simulated contained in a measured circuit;
acquiring an initial simulation model of the device to be simulated, wherein the initial simulation model comprises c key parameters for reflecting the electrical characteristics of the device to be simulated, and c is a positive integer;
and updating and iterating initial values of the c key parameters by adopting a simulated annealing algorithm, acquiring simulation electrical characteristic change data of a current simulation model of the device to be simulated in each updating and iterating process, and judging whether the values of the key parameters are updated according to the similarity of the actually measured electrical characteristic change data and the simulation electrical characteristic change data, wherein the method comprises the following steps: randomly generating initial values of the c key parameters to obtain a current simulation model of the device to be simulated; setting the current temperature value of the simulated annealing algorithm as an initial value, and setting a lowest temperature value, wherein the initial value of the current temperature value is larger than the lowest temperature value; the one-time updating iterative process comprises the following steps: acquiring first simulation electrical characteristic change data of the current simulation model, and calculating the total similarity of the first simulation electrical characteristic change data and the actually measured electrical characteristic change data to be used as a first similarity; randomly changing the values of part of the key parameters of the c key parameters to obtain an alternative simulation model of the device to be simulated; acquiring second simulation electrical characteristic change data of the alternative simulation model; calculating the total similarity of the second simulation electrical characteristic change data and the actually measured electrical characteristic change data as a second similarity; if the second similarity is greater than the first similarity, replacing the values of the key parameters in the current simulation model with the values of the key parameters in the alternative simulation model; if the second similarity is smaller than the first similarity, calculating the probability of replacing the value of the key parameter in the current simulation model with the value of the key parameter in the alternative simulation model according to the first similarity, the second similarity and the current temperature; if the probability is within a preset range, replacing the value of the key parameter in the current simulation model with the value of the key parameter in the alternative simulation model, and if the probability is not within the preset range, reserving the value of the key parameter in the current simulation model; reducing the current temperature value, judging whether the reduced current temperature value is larger than a minimum temperature value, if so, executing the next updating iteration process, otherwise, ending the updating iteration process;
And obtaining a final simulation model of the simulation device according to the values of the c key parameters at the end of the simulated annealing algorithm.
2. The method for constructing a device simulation model according to claim 1, wherein the obtaining measured electrical characteristic change data of the device to be simulated includes:
acquiring an actual measurement electrical characteristic change curve of the device to be simulated in the actual measurement circuit;
sampling the measured electrical characteristic change curve at equal intervals to obtain a measured discretization data sequence of the measured electrical characteristic change curve;
calculating the slope of an actual measurement curve of the position of each actual measurement discretization data according to the actual measurement discretization data sequence and the sampling interval;
and taking the actually measured discretization data sequence and the actually measured curve slope as the actually measured electrical characteristic change data.
3. The method for constructing a device simulation model according to claim 2, wherein updating and iterating initial values of the c key parameters by using a simulated annealing algorithm, obtaining simulated electrical characteristic change data of a current simulation model of the device to be simulated in each updating and iterating process, and judging whether the values of the key parameters are updated according to the similarity of the measured electrical characteristic change data and the simulated electrical characteristic change data, wherein the method comprises the steps of:
Randomly generating initial values of the c key parameters to obtain a current simulation model of the device to be simulated;
setting the current temperature value of the simulated annealing algorithm as an initial value, and setting a lowest temperature value, wherein the initial value of the current temperature value is larger than the lowest temperature value;
the one-time updating iterative process comprises the following steps:
acquiring first simulation electrical characteristic change data of the current simulation model, and calculating the total similarity of the first simulation electrical characteristic change data and the actually measured electrical characteristic change data to be used as a first similarity;
continuously randomly changing the values of part of the key parameters for S times to obtain S alternative simulation models;
setting the initial value of j to be 1;
performing the following parameter substitution procedure on the j-th alternative simulation model:
acquiring a j-th alternative simulation model, and acquiring second simulation electrical characteristic change data of the j-th alternative simulation model;
calculating the total similarity of the second simulation electrical characteristic change data and the actually measured electrical characteristic change data as a second similarity;
if the second similarity is greater than the first similarity, replacing the value of the key parameter in the current simulation model with the value of the key parameter in the j-th alternative simulation model;
If the second similarity is smaller than the first similarity, calculating the probability of replacing the value of the key parameter in the current simulation model with the value of the key parameter in the j-th alternative simulation model according to the first similarity, the second similarity and the current temperature value;
if the probability is within a preset range, replacing the value of the key parameter in the current simulation model with the value of the key parameter in the j-th alternative simulation model, and if the probability is not within the preset range, reserving the value of the key parameter in the current simulation model;
judging whether j is smaller than S, if yes, after j=j+1 is updated, returning to execute the next updating and replacing process, otherwise, ending the updating and replacing process;
and reducing the current temperature value, judging whether the reduced current temperature value is larger than a minimum temperature value, if so, executing the next updating iteration process, and otherwise, ending the updating iteration process.
4. The method for constructing a device simulation model according to claim 3, wherein the obtaining the first simulated electrical characteristic change data of the current simulation model comprises:
building a simulation test circuit corresponding to the actual measurement circuit through a simulation circuit simulator;
Acquiring a first simulation electrical characteristic change curve of the current simulation model in the simulation test circuit;
sampling the first simulation electrical characteristic change curve at equal intervals to obtain a first simulation discretization data sequence of the first simulation electrical characteristic change curve;
calculating a first simulation curve slope of the position of each first simulation discretization data according to the first simulation discretization data sequence and the sampling interval;
taking the first simulation discretization data sequence and the first simulation curve slope as the first simulation electrical characteristic change data;
the obtaining second simulation electrical characteristic change data of the alternative simulation model includes:
acquiring a second simulation electrical characteristic change curve of the alternative simulation model in the simulation test circuit;
sampling the second simulation electrical characteristic change curve at equal intervals to obtain a second simulation discretization data sequence of the second simulation electrical characteristic change curve;
calculating a second simulation curve slope of the position of each second simulation discretization data according to the second simulation discretization data sequence and the sampling interval;
And taking the second simulation discretization data sequence and the second simulation curve slope as the second simulation electrical characteristic change data.
5. The method of constructing a device simulation model according to claim 4, wherein the calculating of the total similarity of the first simulated electrical characteristic change data and the measured electrical characteristic change data as the first similarity includes:
obtaining the similarity of each first simulation discretization data and each actual measurement discretization data according to each first simulation discretization data, a first simulation curve slope of a position where each first simulation discretization data is located, each actual measurement discretization data and an actual measurement curve slope of a position where each actual measurement discretization data is located;
and calculating the sum of the similarity of each first simulation discretization data and the measured discretization data to obtain the total similarity of the first simulation electrical characteristic change data and the measured electrical characteristic change data.
6. The method of constructing a device simulation model according to claim 4, wherein the calculating the total similarity of the second simulated electrical characteristic change data and the measured electrical characteristic change data as the second similarity includes:
Obtaining the similarity of each second simulation discretization data and each actual measurement discretization data according to each second simulation discretization data, the second simulation curve slope of the position of each second simulation discretization data, each actual measurement discretization data and the actual measurement curve slope of the position of each actual measurement discretization data;
and calculating the sum of the similarity of each second simulation discretization data and the actually measured discretization data to obtain the total similarity of the second simulation electrical characteristic change data and the actually measured electrical characteristic change data.
7. A device simulation model building apparatus, comprising:
the device comprises a measured data acquisition module, a measurement circuit and a measurement circuit, wherein the measured data acquisition module is used for acquiring measured electrical characteristic change data of a device to be simulated, and the measured electrical characteristic change data are obtained by detecting the electrical characteristic change of the device to be simulated contained in a measured circuit;
the initial model acquisition module is used for acquiring an initial simulation model of the device to be simulated, wherein the initial simulation model comprises c key parameters for reflecting the electrical characteristics of the device to be simulated, and c is a positive integer;
The updating module is configured to update and iterate initial values of the c key parameters by using a simulated annealing algorithm, obtain simulated electrical characteristic change data of a current simulation model of the device to be simulated in each updating and iterating process, and determine whether the values of the key parameters are updated according to similarity between the actually measured electrical characteristic change data and the simulated electrical characteristic change data, where the updating module includes: randomly generating initial values of the c key parameters to obtain a current simulation model of the device to be simulated; setting the current temperature value of the simulated annealing algorithm as an initial value, and setting a lowest temperature value, wherein the initial value of the current temperature value is larger than the lowest temperature value; the one-time updating iterative process comprises the following steps: acquiring first simulation electrical characteristic change data of the current simulation model, and calculating the total similarity of the first simulation electrical characteristic change data and the actually measured electrical characteristic change data to be used as a first similarity; randomly changing the values of part of the key parameters of the c key parameters to obtain an alternative simulation model of the device to be simulated; acquiring second simulation electrical characteristic change data of the alternative simulation model; calculating the total similarity of the second simulation electrical characteristic change data and the actually measured electrical characteristic change data as a second similarity; if the second similarity is greater than the first similarity, replacing the values of the key parameters in the current simulation model with the values of the key parameters in the alternative simulation model; if the second similarity is smaller than the first similarity, calculating the probability of replacing the value of the key parameter in the current simulation model with the value of the key parameter in the alternative simulation model according to the first similarity, the second similarity and the current temperature; if the probability is within a preset range, replacing the value of the key parameter in the current simulation model with the value of the key parameter in the alternative simulation model, and if the probability is not within the preset range, reserving the value of the key parameter in the current simulation model; reducing the current temperature value, judging whether the reduced current temperature value is larger than a minimum temperature value, if so, executing the next updating iteration process, otherwise, ending the updating iteration process;
And the final model acquisition module is used for acquiring a final simulation model of the simulation device according to the values of the c key parameters when the simulated annealing algorithm is finished.
8. An electronic device, comprising: the device comprises a processor, a communication interface, a memory and a communication bus, wherein the processor, the communication interface and the memory are communicated with each other through the communication bus;
the memory is used for storing a computer program;
the processor is configured to execute a program stored in the memory, and implement the method for constructing a device simulation model according to any one of claims 1 to 6.
9. A computer-readable storage medium storing a computer program, wherein the computer program, when executed by a processor, implements a method of constructing a device simulation model according to any one of claims 1-6.
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