CN112560375A - Method and device for extracting model parameters, server and storage medium - Google Patents
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Abstract
The present disclosure provides a method and an apparatus for extracting model parameters, a server and a storage medium, which are used for extracting model parameters of a simulation model of a gallium nitride high electron mobility transistor, and the method includes: obtaining test data, wherein the test data is drain current obtained by testing the gallium nitride high electron mobility transistor under a working voltage; extracting candidate values which enable corresponding simulation data to be consistent with test data from a plurality of candidate values of the model parameters through an fmincon function, and determining the extracted candidate values as target values of the model parameters; the simulation data is drain current obtained by simulating the gallium nitride high electron mobility transistor model under the working voltage based on the corresponding candidate values. The method can be used for automatically and rapidly extracting the model parameters of the simulation model of the gallium nitride high electron mobility transistor.
Description
Technical Field
The disclosure relates to the field of microelectronic device modeling, in particular to a method and a device for extracting model parameters, a server and a storage medium.
Background
Gallium nitride (GaN) has High bandwidth, breakdown voltage and thermal conductivity, and these excellent characteristics make gallium nitride applied to transistors to produce transistors with High Electron Mobility, i.e. GaN High Electron Mobility transistors (GaN-HEMTs).
In order to evaluate and analyze the current and voltage of the GaN-HEMT, there are many models for simulating the GaN-HEMT, including curice, EEHEMT, Angelov-GaN, ASM-HEMT and MVSG-HV, wherein ASM-HEMT and MVSG-HV are also recognized as new standard models for simulating GaN devices by the compact model union. The simulation models are established based on current-voltage characteristic equations, the characteristic equations establish a functional relation between the drain current and the working voltage (including the drain voltage and the grid voltage) of the GaN-HEMT, the equations relate to model parameters to be fitted, and accurate extraction of the model parameters is necessary for accurate simulation of the GaN-HEMT by the simulation models.
However, all simulation models of GaN-HEMTs on the market do not have a complete flow definition in terms of model parameter extraction, and the current model parameter extraction is realized mainly by manual adjustment according to experience of engineers, so that the efficiency is low.
Disclosure of Invention
In order to solve the technical problem, the present disclosure provides a method and an apparatus for extracting model parameters, a server, and a storage medium, which are used for extracting model parameters of a simulation model of a gallium nitride high electron mobility transistor, and can effectively improve the efficiency of extracting the model parameters.
In one aspect, the present disclosure provides a method for extracting model parameters, which is used to extract model parameters of a simulation model of a gallium nitride high electron mobility transistor, and the method includes:
obtaining test data, wherein the test data is drain current obtained by testing the gallium nitride high electron mobility transistor under a working voltage;
extracting candidate values which enable corresponding simulation data to be consistent with the test data from the plurality of candidate values of the model parameters through an fmincon function, and determining the extracted candidate values as target values of the model parameters;
the simulation data correspond to the candidate values one by one, and the simulation data are drain currents obtained by simulation of the simulation model under the working voltage based on the corresponding candidate values.
Optionally, the operating voltage is multiple;
the test data are multiple, and the multiple test data and the multiple working voltages are in one-to-one correspondence relationship;
a plurality of simulation data corresponding to each candidate value are provided, and the plurality of simulation data corresponding to each candidate value and the plurality of working voltages are in one-to-one correspondence relationship;
and the root mean square of the plurality of difference values is used as an error function value to be solved by the fmincon function, wherein each difference value is a numerical value obtained by subtracting the simulation data and the test data under the working voltage.
Optionally, the extraction method further includes:
acquiring a value lower limit and a value upper limit of the model parameter;
obtaining the step length of the model parameter;
and determining a plurality of candidate values according to the value lower limit, the value upper limit and the step length.
Optionally, the number of the model parameters is multiple, the step lengths of the multiple model parameters are the same, and determining the multiple candidate values according to the value lower limit, the value upper limit and the step length includes:
judging whether the magnitude difference between the upper value limit and the lower value limit is not less than a preset magnitude;
under the condition that the difference of the magnitude of the upper value limit and the magnitude of the lower value limit is not smaller than the preset magnitude, carrying out the same operation processing on the upper value limit and the lower value limit, wherein the operation processing comprises carrying out common logarithm processing and then carrying out normalization processing;
determining a plurality of candidate initial values according to the lower limit updated value and the upper limit updated value obtained after the operation processing and the step length;
and carrying out inverse operation processing of the operation processing on each candidate initial value to obtain a plurality of candidate values.
Optionally, obtaining the step size of the model parameter includes:
determining whether the lower limit update value and the upper limit update value are equal to zero;
determining the step size of the model parameter to be 1e-6 in case the lower limit update value or the upper limit update value is equal to zero;
determining the step size of the model parameter to be one tenth of the lower limit update value if both the lower limit update value and the upper limit update value are not equal to zero.
Optionally, the operating voltage is a gate voltage at a gate of the gallium nitride high electron mobility transistor, and the model parameter is a first parameter;
the working voltage is the drain voltage at the drain of the gallium nitride high electron mobility transistor, and the model parameter is a second parameter;
and under the condition that the working voltage is the drain voltage, the simulation data is drain current simulated by the simulation model under the working voltage based on the first parameter and the corresponding candidate value.
Optionally, the first parameter includes: the slope influence factors of the turn-off voltage and the sub-turn-off voltage, the effect parameter of potential barrier reduction introduced at the drain end, the slope change value of the sub-turn-off voltage caused by drain-source voltage and drain voltage, the minimum leakage current, the low-field mobility, the first-order degradation coefficient of the mobility and the second-order degradation coefficient of the mobility;
the second parameter includes: drain terminal contact resistance, gate terminal contact resistance, temperature dependent parameters of mobility, temperature dependent parameters of two-dimensional electron gas density of an access region, thermal resistance, saturation velocity parameters, and saturation velocity of a source access region.
Optionally, extracting, by an fmincon function, a candidate value that makes the corresponding simulation data consistent with the test data from the plurality of candidate values of the model parameter includes:
acquiring a convergence precision lower limit value of the fmincon function limited by the running equipment;
extracting candidate values which make the corresponding simulation data consistent with the test data from a plurality of candidate values of the target parameter through an fmincon function based on the convergence precision lower limit value;
wherein the target parameters include at least one of: the device comprises a slope influence factor of turn-off voltage and sub-turn-off voltage, low field mobility, a mobility first-order degradation coefficient, a mobility second-order degradation coefficient, a mobility temperature dependent parameter, an access area two-dimensional electron gas density temperature dependent parameter, a thermal resistance, a saturation speed parameter and a source access area saturation speed.
In another aspect, the present disclosure provides a model parameter extraction apparatus for extracting model parameters of a simulation model of a gallium nitride high electron mobility transistor, the apparatus including:
the acquisition module is used for acquiring test data, wherein the test data is drain current obtained by testing the gallium nitride high electron mobility transistor under a working voltage;
an extraction module, configured to extract, from the plurality of candidate values of the model parameter, a candidate value that makes corresponding simulation data consistent with the test data through an fmincon function, and determine the extracted candidate value as a target value of the model parameter;
the simulation data correspond to the candidate values one by one, and the simulation data are drain currents obtained by simulation of the simulation model under the working voltage based on the corresponding candidate values.
Optionally, the extraction device further comprises:
and the determining module is used for acquiring the value lower limit, the value upper limit and the step length of the model parameter and determining a plurality of candidate values according to the value lower limit, the value upper limit and the step length.
In another aspect, the present disclosure also provides a server, including:
a processor;
a memory for storing one or more programs;
wherein the one or more programs are executed by the processor such that the processor implements any of the model parameter extraction methods described above.
In another aspect, the present disclosure provides a computer-readable storage medium, on which a computer program is stored, wherein the program, when executed by a processor, implements any one of the above-mentioned model parameter extraction methods.
The beneficial effects of this disclosure are:
extracting candidate values which enable corresponding simulation data to be consistent with test data from a plurality of candidate values of model parameters through an fmincon function, and determining the extracted candidate values as target values of the model parameters; the test data is drain current obtained by testing the gallium nitride high electron mobility transistor under a working voltage, the simulation data corresponds to the candidate values one to one, and the simulation data is the drain current obtained by simulating the gallium nitride high electron mobility transistor model under the working voltage based on the corresponding candidate values. The fmincon function can automatically extract candidate values which enable corresponding simulation data to be consistent with test data from a plurality of candidate values of the model parameters through operation, so that workload and time of manual parameter adjustment are reduced, and extraction efficiency of the model parameters is improved.
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The above and other objects, features and advantages of the present disclosure will become more apparent from the following description of the embodiments of the present disclosure with reference to the accompanying drawings.
Fig. 1 is a schematic flow chart of a method for extracting model parameters according to a first embodiment of the present disclosure;
fig. 2 is a schematic flow chart of another method for extracting model parameters according to a first embodiment of the present disclosure;
FIG. 3 is a schematic diagram illustrating the execution flow of step S130 in the extraction method shown in FIG. 2;
fig. 4 is a schematic structural diagram illustrating an apparatus for extracting model parameters according to a second embodiment of the present disclosure;
fig. 5 is a schematic structural diagram of another device for extracting model parameters according to a second embodiment of the present disclosure;
fig. 6 shows a schematic structural diagram of a server provided in the third embodiment of the present disclosure.
Detailed Description
To facilitate an understanding of the present disclosure, the present disclosure will now be described more fully with reference to the accompanying drawings. Preferred embodiments of the present disclosure are set forth in the accompanying drawings. However, the present disclosure may be embodied in different forms and is not limited to the embodiments described herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this disclosure belongs. The terminology used in the description of the disclosure herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the disclosure.
According to the related technology, the drain current of the GaN-HEMT is evaluated and analyzed through a simulation model, wherein the simulation model is established based on a current-voltage characteristic equation, the current-voltage characteristic equation establishes a functional relation between the drain current of the GaN-HEMT and working voltage (including drain voltage and grid voltage), and the current-voltage characteristic equation relates to some model parameters to be fitted, so that accurate extraction of each model parameter is necessary for accurate simulation of the GaN-HEMT through the simulation model. However, all simulation models of GaN-HEMTs on the market do not have a complete flow definition in terms of model parameter extraction, and the current model parameter extraction is realized mainly by manual adjustment according to experience of engineers, so that the efficiency is low.
Based on this, the present disclosure provides a method and an apparatus for extracting model parameters, a server, and a storage medium, which can automatically extract candidate values that make corresponding simulation data consistent with test data from a plurality of candidate values of the model parameters by using an fmincon function, thereby improving the efficiency of extracting the model parameters.
The present disclosure is described in detail below with reference to the accompanying drawings.
The first embodiment is as follows:
fig. 1 shows a schematic flow chart of a method for extracting model parameters according to a first embodiment of the present disclosure. The method for extracting the model parameters is used for extracting the model parameters of the simulation model of the gallium nitride high electron mobility transistor, and comprises the following steps:
step S120, obtaining test data, wherein the test data is drain current obtained by testing the gallium nitride high electron mobility transistor under a working voltage;
step S140, extracting candidate values which enable corresponding simulation data to be consistent with test data from a plurality of candidate values of the model parameters through an fmincon function, and determining the extracted candidate values as target values of the model parameters (the target values are values set for the model parameters when the simulation model simulates the GaN-HEMT); the simulation data correspond to the candidate values one by one, and the simulation data are drain currents obtained by simulation of the simulation model under the working voltage based on the corresponding candidate values.
Specifically, the fmincon function is a matlab function used to solve the nonlinear multivariate function minimum, and the optimization toolkit of matlab provides the fmincon function for solving constrained optimization problems. The syntax format of fmincon function is shown in equation (1):
x＝ fmincon(fun，x0，A，b，Aeq，beq，lb，ub，nonlcon) (1)
wherein, fun is a function value to be solved, which is also called an objective function; linear inequality constraint of A and b parameter values x; aeq and beq are equality linear constraints on parameter values; lb and ub are respectively a value lower limit and a value upper limit of the parameter, and x0 is the initialization of the parameter value of the function fun and is positioned in the range limited by the value lower limit and the value upper limit of the parameter; nolcon is a non-linear constraint on the parameter values.
The fmincon function applied to the disclosed embodiments includes: the target function fun is a function for representing the consistency of the simulation data and the test data, such as the absolute difference between the simulation data and the test data; linear inequality constraints characterized by a and b, equality linear constraints characterized by Aeq and beq, and nonlinear constraints characterized by nonlcon are all provided by a simulation model of the GaN-HEMT, and specific linear constraint conditions and nonlinear constraint conditions are determined by model parameters to be extracted and the simulation model, which belong to the prior art and are not described herein. In the embodiment of the disclosure, a candidate value which enables corresponding simulation data to be consistent with test data is extracted from a plurality of candidate values of the model parameter through the fmincon function, that is, a candidate parameter which enables the simulation data to be as close as possible to the test data under the condition that the model parameter is constrained by the simulation model is solved. In the embodiment of the disclosure, the fmincon function is embodied as an optimizer for parameter adjustment, which is pre-constructed by the fmincon function of matlab, and when the optimizer runs on a device as a computer program, the optimizer can automatically perform parameter adjustment on the model parameters, and the parameter adjustment range is defined by a lower limit lb and an upper limit ub of the model parameters.
In the embodiment of the disclosure, the fmincon function is used for extracting the candidate value which enables the corresponding simulation data to be consistent with the test data from the plurality of candidate values of the model parameter, and the candidate value which enables the corresponding simulation data to be consistent with the test data can be automatically extracted from the plurality of candidate values of the model parameter by running the fmincon function, so that the workload and time for manually adjusting the parameters are reduced, the extraction efficiency of the model parameter is improved, and the model parameter file can be quickly exported.
Further, the number of the working voltages may be multiple, correspondingly, the test data is multiple and the multiple test data and the multiple working voltages are in one-to-one correspondence, the corresponding simulation data of each candidate value is multiple and the multiple simulation data of each candidate value and the multiple working voltages are in one-to-one correspondence, and the root mean square of multiple difference values is used as an error function value to be solved by the fmincon function, wherein each difference value is a numerical value obtained by subtracting the simulation data and the test data under one working voltage.
It should be understood that the operating voltage is plural, each operating voltage ViCorresponds to oneA test data Idi' (i equals 1, 2, …, n, n is the number of operating voltages), each operating voltage ViCorresponding to a simulation data IdiSuch that each operating voltage ViCorresponding to a difference (Id) accordinglyi-Idi') to a host; the root mean square of the plurality of difference values is used as the error function value to be solved by the fmincon function, that is, the root mean square of the plurality of difference values is used as the objective function fun of the fmincon function, so that the objective function fun of the fmincon function adopts the expression shown in the formula (2):
wherein x isj(j ═ 1, 2, …, m, m is the number of candidate values) is one candidate value of a plurality of candidate values of the model parameter to be extracted, and finally, the candidate value x' which makes fun (x) the minimum in the plurality of candidate values is the target value of the extracted model parameter.
In this embodiment, the number of the operating voltages is multiple, and the root mean square of the multiple difference values is used as an error function value to be solved by the fmincon function, so that the target value extracted from the model parameters is more universal for various operating scenarios of the GaN-HEMT, and accurate simulation of current-voltage characteristics of the simulation model on the GaN-HEMT under various operating voltages can be ensured.
FIG. 2 is a flow chart of another method for extracting model parameters. Referring to fig. 2, the extraction method further includes: step S130, obtaining a value lower limit and a value upper limit of the model parameter and obtaining a step length of the model parameter, and determining a plurality of candidate values according to the value lower limit, the value upper limit and the step length, so that a large number of candidate values set for accurate extraction of the model parameter can be located in a range limited by the value lower limit and the value upper limit, and the interval between two adjacent candidate values in a sequence of the plurality of candidate values sorted according to the numerical value is the step length, namely, the values of the plurality of candidate values are ensured to be reasonably and uniformly distributed; and a plurality of candidate values are automatically generated, namely, the plurality of candidate values are provided to the running equipment of the fmincon function one by one without manual operation. Therefore, on the premise of ensuring that the model parameters obtain more accurate target values, the manual workload consumed by parameter extraction is reduced, and the efficiency of extracting the model parameters is improved.
The value lower limit and the value upper limit are determined by model parameters, and different model parameters correspond to different value lower limits and different value upper limits, so that the numerical difference between the value lower limit and the value upper limit is different due to different model parameters. For the step length of the model parameter, because a simulation model of the GaN-HEMT often has a plurality of model parameters to be fitted, in order to simplify the step length setting of the model parameter, a default step length can be used for all the model parameters, that is, the step lengths of a plurality of the model parameters in the simulation model of the GaN-HEMT are the same, however, when the step lengths of all the model parameters are the same, a large number of candidate values of the model parameters exist in a large number of model parameters with large numerical differences between the lower value limit and the upper value limit. In order to ensure that the candidate values of all the model parameters are more reasonable in number on the premise of simplifying the setting of the step length of the model parameters, referring to fig. 3, in step S130, a plurality of candidate values are determined according to the value lower limit, the value upper limit and the step length, including:
step S131, judging whether the difference of the magnitude of the upper value limit and the lower value limit is not less than a preset magnitude;
step S132, under the condition that the difference of the order of the upper limit and the lower limit is not less than the preset order, carrying out the same operation processing on the upper limit and the lower limit, wherein the operation processing comprises the steps of carrying out common logarithm processing and then carrying out normalization processing;
step S133, determining a plurality of candidate initial values according to the lower limit updated value and the upper limit updated value obtained after the operation processing and the step length;
in step S134, inverse operation processing of the operation processing is performed on each candidate initial value to obtain a plurality of candidate values.
It should be noted that, common logarithm processing is performed on the upper value limit and the lower value limit, that is, the logarithm of the upper value limit is obtained with 10 as a base number, and the logarithm of the lower value limit is obtained with 10 as a base number; and then normalization processing is carried out, namely, two values obtained after logarithm processing is respectively carried out on the lower limit of the value and the upper limit of the value are normalized to be in a range of 0-1, wherein the value obtained after the logarithm processing is carried out on the lower limit of the value is normalized to be 0, and the value obtained after the logarithm processing is carried out on the upper limit of the value is normalized to be 1.
Specifically, the value lower limit and the value upper limit are pre-configured by the user according to the model parameters. The following exemplary description is given with a lower value limit lb of 1 and an upper value ub of 10000 (the units of the lower value limit and the upper value limit are the same and are set according to the model parameters, and the international unit of the model parameters may be selected), and with a predetermined number of orders of magnitude of 2 and a step size of 0.2:
step S131 determines that the difference between the upper value limit ub and the lower value limit lb is greater than a predetermined number 2, and step S132 is executed in this case;
in step S132, a common logarithm processing lg (lb) is performed first on the value of the lower limit lb to obtain 0, and a common logarithm processing lg (ub) is performed first on the value of the upper limit ub to obtain 4; then, normalization processing is carried out, namely 4 obtained by solving a common logarithm from a value upper limit ub is normalized to be 1, and a lower limit update value lb 'is 0 and an upper limit update value ub' is 1 after operation processing;
next, in step S133, a plurality of candidate initial values are determined according to the lower limit updated value, the upper limit updated value, and the step size obtained after the arithmetic processing, and the determined plurality of candidate initial values sequentially include: 0. 0.2, 0.4, 0.6, 0.8, 1;
finally, in step S134, the inverse operation processing of the operation processing is performed on each candidate initial value, wherein 10 is the inverse operation processing performed on the candidate initial value 00×4(i.e. the lower limit lb) of the value), the candidate initial value 0.2 is processed by inverse operation, i.e. 100.2×4The candidate initial value 0.4 is processed by inverse operation to obtain 100.4×4The candidate initial value 0.6 is subjected to inverse operation processing, that is, 100.6×4The candidate initial value 0.8 is subjected to inverse operation processing, that is, 100.8×4The candidate initial value 1 is subjected to inverse operation processing, that is, 101×4(i.e., the value upper limit ub).
It should be understood that if it is determined in step S131 that the difference between the upper value limit and the lower value limit is smaller than 2, the above pre-processing is not performed on the upper value limit and the lower value limit, that is: and directly determining a plurality of candidate values by the sum of the value lower limit and a plurality of step lengths, wherein the maximum value in the plurality of candidate values is not more than the value upper limit.
Further, the step length of the model parameter obtained in step S130 includes: judging whether the lower limit update value lb 'and the upper limit update value ub' are equal to zero; determining the step size of the model parameter to be 1e-6 (i.e., 10 to the power of-6) in the case that the lower limit update value lb 'or the upper limit update value ub' is equal to zero; in the case where both the lower limit update value lb ' and the upper limit update value ub ' are not equal to zero, the step size of the model parameter is determined to be one tenth of the lower limit update value lb '. The unit of the lower limit update value lb 'is the same as the unit of the upper limit update value ub', and the unit of the step size is the same as the unit of the lower limit update value lb 'and the unit of the upper limit update value ub'. For the GaN-HEMT model with the simulation current-voltage characteristics, the step length enables each model parameter to extract a target value meeting the simulation precision requirement.
In a simulation model of a GaN-HEMT that simulates current-voltage characteristics, the operating voltage includes a gate voltage at a gate of a gallium nitride high electron mobility transistor and a drain voltage at a drain of the gallium nitride high electron mobility transistor. Wherein, under the condition that the working voltage comprises the grid voltage at the grid of the gallium nitride high electron mobility transistor, the drain voltage and the source voltage of the gallium nitride high electron mobility transistor are constant, the grid voltage Vg is multiple, and each grid voltage VgiCorresponding to a test data Idi' and one simulation data Idi(ii) a Under the condition that the working voltage comprises a drain voltage at the drain of the gallium nitride high electron mobility transistor, the grid voltage and the source voltage of the gallium nitride high electron mobility transistor are constant, the drain voltage Vd is multiple, and each grid voltage VdiCorresponding to a test data Idi' and one simulation data Idi. If the extracted model parameter is recorded as a first parameter under the condition that the working voltage is the grid voltage; the model parameter extracted under the condition that the working voltage is the drain voltage is recorded as a second parameter, and the simulation data is the simulation model based on the first parameter under the condition that the working voltage is the drain voltageAnd drain current obtained by simulating the corresponding candidate value under the working voltage. That is, if the model parameters of the simulation model are divided into the first parameter and the second parameter, the first parameter is extracted by setting the operating voltage as the gate voltage, and then the second parameter is extracted by setting the operating voltage as the drain voltage, wherein the simulation model uses the extracted first parameter in the second parameter extraction process, and since the characteristic equation for calculating the second parameter in the simulation model of the GaN-HEMT uses the first parameter, the simulation model extracts the second parameter based on the accurate first parameter, which is beneficial to the accurate extraction of the second parameter.
Specifically, the first parameter includes: (1) an effect parameter eta0 of Drain Induced Barrier Lowering (DIBL) introduced by a Drain end, a slope change value cdscd of a Drain source voltage vdscale and a Drain voltage caused by Drain voltage, (3) a minimum leakage current imin, and (4) a low-field mobility u0, a mobility first-order degradation coefficient ua and a mobility second-order degradation coefficient ub. The second parameter includes: (1) drain contact resistance rdc and gate contact resistance rsc, (2) temperature dependent parameter of mobility ute, temperature dependent parameter of access area two-dimensional electron density kns0 and thermal resistance rth0, (3) saturation velocity vsat, saturation velocity parameter thesat and saturation velocity vsataccs of source access area.
For each of the above-mentioned sets of first parameters, in the case where the operating voltage is set as the gate voltage, step S140, extracting a candidate value that makes the corresponding simulation data consistent with the test data from a plurality of candidate values of the model parameter by the fmincon function includes:
(1) extracting model parameters of a linear region of the transistor under low bias voltage through an fmincon function, wherein the extracted model parameters comprise: the slope influencing factor nfactor of the turn-off voltage voff and the sub-turn-off voltage;
(2) extracting relevant model parameters of a linear region of the transistor under a plurality of bias voltages through an fmincon function, wherein the extracted model parameters comprise: a DIBL effect parameter eta0, a drain-source voltage vdscale under DIBL and a sub-turn-off voltage slope change value cdscdd caused by drain voltage;
(3) extracting model parameters of a transistor cut-off region under a plurality of bias voltages through an fmincon function, wherein the extracted model parameters comprise: extracting the minimum leakage current imin;
(4) extracting model parameters for characterizing the change of drain current along with the grid voltage through an fmincon function, wherein the extracted model parameters comprise: low field mobility u0, and a mobility first order degradation coefficient ua and a mobility second order degradation coefficient ub.
For each set of the second parameters, under the condition that the working voltage is set as the drain voltage, step S140, extracting candidate values that make the corresponding simulation data consistent with the test data from the plurality of candidate values of the model parameters by the fmincon function, includes:
(1) extracting model parameters from a linear region of the transistor through an fmincon function, wherein the extracted model parameters comprise: drain contact resistance rdc and gate contact resistance rsc;
(2) extracting model parameters of a transistor saturation region through an fmincon function, wherein the extracted model parameters comprise: a temperature dependent parameter ute of mobility, a temperature dependent parameter kns0 of access area two-dimensional electron gas density and a thermal resistor rth 0;
(3) extracting model parameters describing the whole area of id-Vd by an fmincon function, wherein the extracted model parameters comprise: saturation velocity vsat, saturation velocity parameter theasat, and saturation velocity vsataccs of the source access region.
Further, for the target parameter, in step S140, extracting a candidate value that makes the corresponding simulation data consistent with the test data from the plurality of candidate values of the model parameter through the fmincon function, including: acquiring a convergence precision lower limit value of the fmincon function limited by the running equipment; and extracting candidate values which enable corresponding simulation data to be consistent with the test data from a plurality of candidate values of the target parameters through an fmincon function based on a convergence precision lower limit value, wherein the target parameters at least comprise the following group: the slope influence factors nfactor of the turn-off voltage voff and the sub-turn-off voltage, the low-field mobility u0, the mobility first-order degradation coefficient ua and the mobility second-order degradation coefficient ub, the temperature dependence parameter ute of the mobility, the temperature dependence parameter kns0 of the two-dimensional electron density of the access region, the thermal resistance rth0, the saturation velocity vsat, the saturation velocity parameter theasat, and the saturation velocity vsataccs of the source access region.
Specifically, the above-mentioned lower limit value of convergence accuracy of obtaining the fmincon function limited by the running device may be: and calculating the target function fun by continuously reducing the convergence precision of the fmincon function until the current convergence precision enables the target function fun not to be converged all the time, wherein the previous convergence precision of the current convergence precision is the convergence precision lower limit value limited by the operating equipment.
For example, the initial convergence accuracy is set to 1e-6, and the primary objective function fun is calculated based on the initial convergence accuracy 1 e-6; then, the convergence accuracy is successively decreased, and the convergence accuracy at the k (k ═ 1, 2, …, 10) th time is (1e-6) - (k-1) × (1e-7), and if the target function fun calculated at the 6 th time cannot be converged at all times due to the accuracy limitation of the fmincon function operating device, the convergence accuracy lower limit value of the fmincon function by the operating device is determined as (1e-6) - (5-1) × (1e-7), that is, as 6 × (1 e-6).
Because the target parameters have large influence on the simulation model, namely the target parameters have slight deviation, the simulation precision of the simulation model is reduced to a great extent, and therefore, the target values of the target parameters in the first parameters and the second parameters are extracted by the lower limit value of the convergence precision, which is beneficial to improving the simulation precision of the simulation model; and the model parameter which does not belong to the target parameter in the first parameter and the second parameter can adopt the initial convergence precision to extract the target value, so that the extraction of the model parameter can be completed quickly.
Example two:
fig. 4 is a schematic structural diagram of a model parameter extraction apparatus according to an embodiment of the present invention. The model parameter extraction device is used for extracting model parameters of a simulation model of a gallium nitride high electron mobility transistor, and referring to fig. 4, the model parameter extraction device 100 includes:
an obtaining module 120, configured to obtain test data, where the test data is a drain current obtained by testing the gallium nitride high electron mobility transistor at a working voltage;
an extracting module 140, configured to extract, from the multiple candidate values of the model parameter, a candidate value that makes the corresponding simulation data consistent with the test data through an fmincon function, and determine the extracted candidate value as a target value of the model parameter;
the simulation data correspond to the candidate values one by one, and the simulation data are drain currents obtained by simulation of the simulation model under the working voltage based on the corresponding candidate values.
In the embodiment of the present disclosure, the extracting apparatus of the model parameter includes an obtaining module 120 and an extracting module 140, where the extracting module 140 extracts a candidate value that makes the corresponding simulation data consistent with the test data from a plurality of candidate values of the model parameter through an fmincon function, and determines the extracted candidate value as a target value of the model parameter; the test data is drain current obtained by testing the gallium nitride high electron mobility transistor under a working voltage, the simulation data corresponds to the candidate values one to one, and the simulation data is the drain current obtained by simulating the gallium nitride high electron mobility transistor model under the working voltage based on the corresponding candidate values. The fmincon function can automatically extract candidate values which enable corresponding simulation data to be consistent with test data from a plurality of candidate values of the model parameters through operation, so that workload and time of manual parameter adjustment are reduced, and extraction efficiency of the model parameters is improved.
Fig. 5 is a schematic structural diagram of another model parameter extraction apparatus according to an embodiment of the present invention. Referring to fig. 5, the apparatus for extracting model parameters further includes: the determining module 130 is configured to obtain a value lower limit, a value upper limit, and a step size of the model parameter, and determine a plurality of candidate values according to the value lower limit, the value upper limit, and the step size. The setting of the determining module 130 makes it unnecessary to manually provide a plurality of candidate values one by one to the extracting device of the model parameters, thereby reducing the manual workload consumed by parameter extraction and being beneficial to improving the extracting efficiency of the model parameters.
EXAMPLE III
Fig. 6 shows a schematic structural diagram of a server provided in the third embodiment of the present disclosure.
Referring to fig. 6, the present disclosure also presents a block diagram of an exemplary server suitable for use in implementing embodiments of the present disclosure. It should be understood that the server shown in fig. 6 is only an example, and should not bring any limitation to the function and the scope of the application of the embodiments of the present disclosure.
As shown in FIG. 6, server 200 is in the form of a general purpose computing device. The components of server 200 may include, but are not limited to: one or more processors or processing units 210, a memory 220, and a bus 201 that couples the various system components (including the memory 220 and the processing unit 210).
The system memory 220 may include computer system readable media in the form of volatile memory, such as Random Access Memory (RAM)221 and/or cache memory 222. The server 200 may further include other removable/non-removable, volatile/nonvolatile computer system storage media. By way of example only, storage system 223 may be used to read from and write to non-removable, nonvolatile magnetic media (not shown in FIG. 6, often referred to as a "hard drive"). Although not shown in FIG. 6, a magnetic disk drive for reading from and writing to a removable, nonvolatile magnetic disk (e.g., a "floppy disk") and an optical disk drive for reading from or writing to a removable, nonvolatile optical disk (e.g., a CD-ROM, DVD-ROM, or other optical media) may be provided. In these cases, each drive may be connected to bus 201 by one or more data media interfaces. Memory 220 may include at least one program product having a set (e.g., at least one) of program modules that are configured to carry out the functions of embodiments of the disclosure.
Program/utility 224 having a set (at least one) of program modules 2241 may be stored, for example, in memory 220, such program modules 2241 including, but not limited to, an operating system, one or more application programs, other program modules, and program data, each of which or some combination of which may comprise an implementation of a network environment. Program modules 2241 generally perform the functions and/or methods of the embodiments described in the embodiments of the present disclosure.
Further, the server 200 may also be communicatively connected to a display 300 for displaying the results of the model parameter extraction, the display 300 may include, but is not limited to, a Liquid Crystal Display (LCD), a Light Emitting Diode (LED) display, and a plasma display. In some embodiments, the display 300 may also be a touch screen.
Further, the server 200 may also communicate with one or more devices that enable a user to interact with the server 200, and/or with any devices (e.g., network cards, modems, etc.) that enable the server 200 to communicate with one or more other computing devices. Such communication may be through input/output (I/O) interfaces 230. Also, server 200 may communicate with one or more networks (e.g., a Local Area Network (LAN), a Wide Area Network (WAN) and/or a public network, such as the Internet) via network adapter 240. As shown, network adapter 240 communicates with the other modules of server 200 via bus 201. It should be appreciated that although not shown in the figures, other hardware and/or software modules may be used in conjunction with the server 200, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems, among others.
The processing unit 210 executes various functional applications and data processing by executing programs stored in the system memory 220, for example, implementing the extraction method of the model parameters provided in the first embodiment of the present disclosure.
Example four:
the fourth embodiment of the present disclosure further provides a computer-readable storage medium, on which a computer program (or referred to as computer-executable instructions) is stored, where the computer program is used for executing the method for extracting model parameters provided in the first embodiment of the present disclosure when executed by a processor, and the method includes:
obtaining test data, wherein the test data is drain current obtained by testing the gallium nitride high electron mobility transistor under a working voltage;
extracting candidate values which enable corresponding simulation data to be consistent with test data from a plurality of candidate values of the model parameters through an fmincon function, and determining the extracted candidate values as target values of the model parameters;
the simulation data correspond to the candidate values one by one, and the simulation data are drain currents obtained by simulation of the simulation model under the working voltage based on the corresponding candidate values.
The computer storage media of the disclosed embodiments may take any combination of one or more computer-readable media. The computer readable medium may be a computer readable signal medium or a computer readable storage medium. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples (a non-exhaustive list) of the computer readable storage medium would include the following: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the present disclosure, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.
A computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device.
Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.
Computer program code for carrying out operations for embodiments of the present disclosure may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, Smalltalk, C + +, and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider).
Further, in this document, the contained terms "include", "contain" or any other variation thereof are intended to cover a non-exclusive inclusion, so that a process, a method, an article or an apparatus including a series of elements includes not only those elements but also other elements not explicitly listed or inherent to such process, method, article or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in a process, method, article, or apparatus that comprises the element.
Finally, it should be noted that: it should be understood that the above examples are only for clearly illustrating the present disclosure, and are not intended to limit the embodiments. Other variations and modifications will be apparent to persons skilled in the art in light of the above description. And are neither required nor exhaustive of all embodiments. And obvious variations or modifications of the invention as herein taught are within the scope of the present disclosure.
Claims (12)
1. A method for extracting model parameters for a simulation model of a gallium nitride high electron mobility transistor (GaN HEMT), the method comprising:
obtaining test data, wherein the test data is drain current obtained by testing the gallium nitride high electron mobility transistor under a working voltage;
extracting candidate values which enable corresponding simulation data to be consistent with the test data from the plurality of candidate values of the model parameters through an fmincon function, and determining the extracted candidate values as target values of the model parameters;
the simulation data correspond to the candidate values one by one, and the simulation data are drain currents obtained by simulation of the simulation model under the working voltage based on the corresponding candidate values.
2. The extraction method according to claim 1,
the number of the working voltages is multiple;
the test data are multiple, and the multiple test data and the multiple working voltages are in one-to-one correspondence relationship;
a plurality of simulation data corresponding to each candidate value are provided, and the plurality of simulation data corresponding to each candidate value and the plurality of working voltages are in one-to-one correspondence relationship;
and the root mean square of the plurality of difference values is used as an error function value to be solved by the fmincon function, wherein each difference value is a numerical value obtained by subtracting the simulation data and the test data under the working voltage.
3. The extraction method according to claim 2, further comprising:
acquiring a value lower limit and a value upper limit of the model parameter;
obtaining the step length of the model parameter;
and determining a plurality of candidate values according to the value lower limit, the value upper limit and the step length.
4. The extraction method according to claim 3, wherein the number of the model parameters is multiple, the step sizes of the plurality of model parameters are the same, and determining the plurality of candidate values according to the lower value limit, the upper value limit and the step size includes:
judging whether the magnitude difference between the upper value limit and the lower value limit is not less than a preset magnitude;
under the condition that the difference of the magnitude of the upper value limit and the magnitude of the lower value limit is not smaller than the preset magnitude, carrying out the same operation processing on the upper value limit and the lower value limit, wherein the operation processing comprises carrying out common logarithm processing and then carrying out normalization processing;
determining a plurality of candidate initial values according to the lower limit updated value and the upper limit updated value obtained after the operation processing and the step length;
and carrying out inverse operation processing of the operation processing on each candidate initial value to obtain a plurality of candidate values.
5. The extraction method of claim 4, wherein obtaining the step size of the model parameter comprises:
determining whether the lower limit update value and the upper limit update value are equal to zero;
determining the step size of the model parameter to be 1e-6 in case the lower limit update value or the upper limit update value is equal to zero;
determining the step size of the model parameter to be one tenth of the lower limit update value if both the lower limit update value and the upper limit update value are not equal to zero.
6. The extraction method according to claim 2,
the working voltage is a gate voltage at the gate of the gallium nitride high electron mobility transistor, and the model parameter is a first parameter;
the working voltage is the drain voltage at the drain of the gallium nitride high electron mobility transistor, and the model parameter is a second parameter;
and under the condition that the working voltage is the drain voltage, the simulation data is drain current simulated by the simulation model under the working voltage based on the first parameter and the corresponding candidate value.
7. The extraction method according to claim 6,
the first parameter includes: the slope influence factors of the turn-off voltage and the sub-turn-off voltage, the effect parameter of potential barrier reduction introduced at the drain end, the slope change value of the sub-turn-off voltage caused by drain-source voltage and drain voltage, the minimum leakage current, the low-field mobility, the first-order degradation coefficient of the mobility and the second-order degradation coefficient of the mobility;
the second parameter includes: drain terminal contact resistance, gate terminal contact resistance, temperature dependent parameters of mobility, temperature dependent parameters of two-dimensional electron gas density of an access region, thermal resistance, saturation velocity parameters, and saturation velocity of a source access region.
8. The extraction method of claim 7, wherein extracting, from the plurality of candidate values of the model parameter, a candidate value that makes corresponding simulation data consistent with the test data by an fmincon function comprises:
acquiring a convergence precision lower limit value of the fmincon function limited by the running equipment;
extracting candidate values which make the corresponding simulation data consistent with the test data from a plurality of candidate values of the target parameter through an fmincon function based on the convergence precision lower limit value;
wherein the target parameters include at least one of: the device comprises a slope influence factor of turn-off voltage and sub-turn-off voltage, low field mobility, a mobility first-order degradation coefficient, a mobility second-order degradation coefficient, a mobility temperature dependent parameter, an access area two-dimensional electron gas density temperature dependent parameter, a thermal resistance, a saturation speed parameter and a source access area saturation speed.
9. An apparatus for extracting model parameters from a simulation model of a gan hemt, the apparatus comprising:
the acquisition module is used for acquiring test data, wherein the test data is drain current obtained by testing the gallium nitride high electron mobility transistor under a working voltage;
an extraction module, configured to extract, from the plurality of candidate values of the model parameter, a candidate value that makes corresponding simulation data consistent with the test data through an fmincon function, and determine the extracted candidate value as a target value of the model parameter;
the simulation data correspond to the candidate values one by one, and the simulation data are drain currents obtained by simulation of the simulation model under the working voltage based on the corresponding candidate values.
10. The extraction device of claim 9, further comprising:
and the determining module is used for acquiring the value lower limit, the value upper limit and the step length of the model parameter and determining a plurality of candidate values according to the value lower limit, the value upper limit and the step length.
11. A server, comprising:
a processor;
a memory for storing one or more programs;
wherein the one or more programs are executed by the processor such that the processor implements the method of extracting model parameters of any one of claims 1-8.
12. A computer-readable storage medium, on which a computer program is stored, wherein the program, when being executed by a processor, implements the method of extracting model parameters according to any one of claims 1 to 8.
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