CN104679960A - Statistical modeling method for radiofrequency variable capacitor - Google Patents
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Abstract
The invention discloses a statistical modeling method for a radiofrequency variable capacitor. The statistical modeling method comprises the following steps: determining all model parameters needing to be extracted for device modeling; classifying the parameters; performing a scattering parameter test on a device, and establishing a reference model not including process fluctuation of the device; acquiring the fluctuation characteristic of the scattering parameter of the device on a whole wafer, and determining a fluctuation data part; constructing an index for performing statistical modeling on the device based on the scattering parameter of the fluctuation data part and a network parameter converted from the scattering parameter; calculating each element value in a sub-circuit model based on the index, performing sensitivity analysis and sequencing of the element values relative to an index value, and selecting an element with highest sensitivity; performing sensitivity analysis and sequencing of parameter values relative to the element values respectively specific to each type of parameter groups in an element extension formula; selecting a parameter with highest sensitivity, and fitting the statistical characteristic distribution of the device to obtain a statistical model of overall fluctuation distribution of the device. The modeling process is rapid and clear.
Description
Technical field
The present invention relates to semiconductor devices modeling technique technical field, be specifically related to a kind of statistical modeling method of radio frequency variodenser.
Background technology
Variodenser (Varactor), as main capacitance adjustment device, plays a very important role, along with its performance inconsistency caused by technique on full wafer wafer of reduction of process node is also more and more remarkable in radio frequency integrated circuit design.And at present open report is had no for the modeling of the statistical property of variodenser radio-frequency performance.
The radio frequency modeling method of variodenser does not form unified standard, show as model and can take different electronic circuit structures, and the element in circuit also can adopt different scalability (Scalable) formula.And most formula does not have strict physical significance, but based on the matching of mathematics, cause model parameter can not accurate respective devices fabrication process parameters.This is that the statistical model parameter chosen for characterizing process fluctuation brings difficulty.
For the technique of different foundries (foundry), its technological fluctuation reason is different, namely in test data, shows as different statistical distribution characteristics, is difficult to choose the statistical modeling that unified fixing standard index carries out variodenser.
Therefore, a kind of statistical modeling method of radio-frequency capacitor of simple possible is provided to seem very important.
Summary of the invention
In order to overcome above problem, the present invention aims to provide a kind of statistical modeling method of radio frequency variodenser, to setting up the global statistics model of variodenser being used for radio frequency integrated circuit, characterize the impact of the device performance caused due to technological fluctuation, thus radio frequency integrated circuit (IC) design makes guidance.
To achieve these goals, the invention provides a kind of statistical modeling method of radio frequency variodenser, described radio frequency variodenser is distributed on the crystal grain of wafer, and it comprises the following steps:
Step 01: the electronic circuit topological structure determining organs weight, determines the scalability formula that in described electronic circuit, each circuit component uses, and for the modeling of different size device, thus determining device modeling needs all model parameters of extraction;
Step 02: classify to parameter, is divided into basic parameter group, the population of parameters relevant to applied bias, with device size correlation parameter group, and the population of parameters irrelevant with technological fluctuation;
Step 03: based on radio frequency modeling demand, scattering parameter test is carried out to device, first part critical size device is selected to carry out the mapping test of scattering parameter, namely all scattering parameter test is carried out to scale device selected by all crystal grains, analyze the mapping test data of scattering parameter, obtain the wave characteristic of device scattering parameter on whole wafer, by observing the scattering parameter distribution under different operating frequency, scattering parameter distribution under different applied bias, the distribution of the different component of scattering parameter, determine the characteristic part in described test data with undulatory property or discreteness,
Step 04: to the above-mentioned characteristic calculating mean value in all crystal grains, the characteristic selecting surveyed device is golden die near the crystal grain corresponding to this mean value, and test the scattering parameter of all device under tests in this golden die, with the device test data of this golden die for fit object, extract the described model parameter in described step 01, set up the benchmark model not comprising technological fluctuation of device;
Step 05: based on the scattering parameter of described characteristic part and the network parameter that converted by scattering parameter, be configured to one or one group of index that device carries out statistical modeling;
Step 06: based on the benchmark model in These parameters and step 04, calculates each component value in sub circuit model, units value of going forward side by side relative to desired value sensitivity analysis and sort, then choose the highest element of sensitivity;
Step 07: based on selected element, to all kinds of populations of parameters in the scalable formula of described element in described step 02, carries out parameter value respectively relative to the sensitivity analysis of component value and sorts, choose the parameter that sensitivity is the highest;
Step 08: based on selected parameter, the statistical property distribution of matching device, thus obtain the statistical model for characterizing device overall situation fluctuation distribution.
Preferably, in described step 03, the described characteristic part with undulatory property or discreteness for: the difference of the maxima and minima of described test data in different crystal grain is greater than the characteristic part of the fluctuation threshold value set by modeling requirement with the ratio of described minimum value.
Preferably, in described step 05, described index can reflect the characteristic of device under radio frequency operation condition; Described characteristic comprises effective capacitance value.
Preferably, in described step 06, carry out component value relative to the sensitivity analysis of desired value and sort, specifically comprise: near described component value, the scale of index change also sorts in the step 05 of more all elements caused by the identical change ratio of component value.
Preferably, in described step 06, when choosing the highest element of sensitivity, the highest number of elements of selected sensitivity is for reaching the minimum number of elements needed for fit object.
Preferably, described step 07 comprises: respectively basic parameter group, biased correlation parameter group, device size correlation parameter group are carried out to sensitivity analysis and sort.
Preferably, described step 07 comprises: when choosing the highest parameter of sensitivity, and the highest number of parameters of selected sensitivity is for reaching the minimum parameters quantity needed for fit object.
Preferably, in described step 08, the approximating method adopted is for adding statistical function to selected parameter and adjusting statistical function correlation parameter.
Preferably, fit procedure in described step 08 specifically comprises: first, for the minimum device of the non-ideal effects being reduced to bring by device size without index during applied bias, the highest parameter of the sensitivity in basic parameter group selected in described step 07 is used to carry out matching; Then, whether indicator-specific statistics characteristic when observing device applied bias meets matching requirement, as do not met, continues to add using the highest parameter of the sensitivity in biased correlation parameter group selected in described step 07 to carry out matching; Then, whether the indicator-specific statistics characteristic of observing different size device meets matching requirement, as do not met, continues to add using the highest parameter of the sensitivity in device size correlation parameter group selected in described step 07 to carry out matching; Complete above-mentioned fit procedure, namely obtain the statistical model for characterizing device overall situation fluctuation distribution.
Preferably, described radio frequency variodenser is MOS varactor.
The statistical modeling method of radio frequency variodenser of the present invention, for different electronic circuit structure, the model that different scalable formula is formed, the present invention is applicable; And, based on mapping test data analysis, choose the index that can reflect device critical nature, the principal statistical characteristic both can effectively held for this process characteristic distributes, and ensures that again institute's Modling model can react the statistical distribution of the Primary Component performance that circuit designers is concerned about; Moreover based on parameter value to component value, component value is to the sensitivity analysis of desired value, and line parameter of going forward side by side is classified, can fast selecting the most effective statistical model parameter on current base model basis; Meanwhile, for the independence of different parameters group, the performance index based on correspondence extract statistical parameter value respectively successively, make modeling process clear fast.
Accompanying drawing explanation
Fig. 1 is the cross-sectional view of N-type MOS varactor
Fig. 2 is the electronic circuit topological structure schematic diagram of a preferred embodiment of the present invention
Fig. 3 is the schematic flow sheet of the statistical modeling method of the radio frequency variodenser of a preferred embodiment of the present invention
Embodiment
For making content of the present invention clearly understandable, below in conjunction with Figure of description, content of the present invention is described further.Certain the present invention is not limited to this specific embodiment, and the general replacement known by those skilled in the art is also encompassed in protection scope of the present invention.
The statistical modeling method of radio frequency variodenser of the present invention, described radio frequency variodenser is distributed on the crystal grain of wafer, and it comprises the following steps:
Determine the electronic circuit topological structure of organs weight, determine the scalability formula that in described electronic circuit, each circuit component uses, for the modeling of different size device, thus determining device modeling needs all model parameters of extraction;
Parameter is classified, is divided into basic parameter group, the population of parameters relevant to applied bias, with device size correlation parameter group, and population of parameters uncorrelated with technological fluctuation;
Based on radio frequency modeling demand, scattering parameter test is carried out to device, first part critical size device is selected to carry out the mapping test of scattering parameter, namely all scattering parameter test is carried out to the selected scale device in all crystal grains (die), analyze the mapping test data of scattering parameter, obtain the wave characteristic of device scattering parameter on whole wafer, by observing the scattering parameter distribution under different operating frequency, scattering parameter distribution under different applied bias, the distribution of the different component of scattering parameter, determine the characteristic part fluctuated in described test data or dispersion degree is larger,
To the above-mentioned characteristic calculating mean value in all crystal grains, the characteristic selecting surveyed device is golden die near the crystal grain corresponding to this mean value, and test the scattering parameter of all device under tests in this golden die, with the device test data of this golden die for fit object, extract the described model parameter in described step 01, set up the benchmark model not comprising technological fluctuation of device;
Based on the scattering parameter of described characteristic part and the network parameter that converted by scattering parameter, be configured to one or one group of index that device carries out statistical modeling;
Based on These parameters and above-mentioned benchmark model, calculate each component value in sub circuit model, units value of going forward side by side relative to desired value sensitivity analysis and sort, then choose the highest element of sensitivity;
Based on selected element, to all kinds of populations of parameters in the above-mentioned scalable formula of described element, carry out parameter value respectively relative to the sensitivity analysis of component value and sort, choose the parameter that sensitivity is the highest;
Based on selected parameter, the statistical property distribution of matching device, thus obtain the statistical model for characterizing device overall situation fluctuation distribution.
Below in conjunction with accompanying drawing 1-3 and specific embodiment, the statistical modeling method to radio frequency variodenser of the present invention is described in further detail.It should be noted that, accompanying drawing all adopt simplify very much form, use non-ratio accurately, and only in order to object that is convenient, that clearly reach aid illustration the present embodiment.
Refer to Fig. 3, the statistical modeling method of the radio frequency variodenser of the present embodiment, comprises the following steps:
Step 01: the electronic circuit topological structure determining organs weight, determines the scalability formula that in electronic circuit, each circuit component uses, and for the modeling of different size device, thus determining device modeling needs all model parameters of extraction;
Concrete, referring to Fig. 1, is a kind of cross-sectional view of N-type MOS varactor, P type substrate 1 is formed N-type trap 2, be positioned at the grid oxide layer 4 in N-type trap 2, be covered in the grid 5 on grid oxide layer 4, and be arranged in the source/drain region 3 of N-type trap 2 of grid 5 both sides.Current industry adopts the form of electronic circuit mostly for the modeling of variodenser, and the electronic circuit topographical form for modeling is various.The electronic circuit topological structure that the present embodiment is taked as shown in Figure 2.This topological circuit structure comprises 10 circuit components altogether, is Lg, Rg, Cs, Cf, Rch, Rsd, Rwell, Csub1, Rsub and Csub2 respectively.
Concrete, in order to meet the needs emulated different size device, needing the scalable model setting up device, namely scalable formula being set up with the test data of matching different size device to each circuit component.Such as Lg=a1*W
a2* L
a3, a1, a2, a3 are the model parameter needing in this formula to determine.These formula can be determined according to device physics working mechanism and test data voluntarily by modeling personnel.The method of the invention not acceptor circuit structure restriction, does not also limit by the form of the fitting formula of circuit component each in electronic circuit.The model parameter extracted is all model parameters.
Step 02: classify to parameter, is divided into basic parameter group, the population of parameters relevant to applied bias, with device size correlation parameter group, and the population of parameters irrelevant with technological fluctuation; Here, irrelevant with technological fluctuation population of parameters can be temperature characterisitic correlation parameter group.
Step 03: based on radio frequency modeling demand, scattering parameter test is carried out to device, first part critical size device is selected to carry out the mapping test of scattering parameter, namely all scattering parameter test is carried out to scale device selected by all crystal grains, analyze the mapping test data of scattering parameter, obtain the wave characteristic of device scattering parameter on whole wafer, by observing the scattering parameter distribution under different operating frequency, scattering parameter distribution under different applied bias, the distribution of the different component of scattering parameter, determine the characteristic part in test data with undulatory property or discreteness, the characteristic part fluctuated or dispersion degree is larger is selected in this embodiment, concrete determined degree of fluctuation can be determined flexibly by the distribution situation of actual test data, and the ratio of difference and minimum value that the degree of fluctuation in the present embodiment is defined as the maxima and minima of described test data in different crystal grain is greater than the fluctuation threshold value preset.This threshold value can be determined flexibly by the actual distribution feature of modeling requirement and test data, and the present embodiment is taken as 5%.Here, crystal grain industry is commonly referred to die.
Step 04: to the above-mentioned characteristic calculating mean value in all crystal grains or median, the characteristic selecting surveyed device is golden die near the crystal grain corresponding to this mean value or median, and test the scattering parameter of all device under tests in this golden die, with the device test data of this golden die for fit object, extract the model parameter in described step 01, set up the benchmark model not comprising technological fluctuation of device; Here, golden die can be called best crystal grain.
Step 05: the S parameter of feature based data division and the network parameter converted by S parameter, be configured to one or one group of index that device carries out statistical modeling;
Concrete, determine the evaluation index of global statistics modeling here.This index should reflect the principal statistical distribution character of test data, and can reflect the key property of device within the scope of radio frequency operation such as effective capacitance value, can be one or one group of index.By observing the S parameter distribution under different operating frequency, the S parameter distribution under different applied bias, the distribution of the different component of S parameter, fluctuate in determining device test data larger data division.And based on S parameter and other network parameters of being converted by S parameter as impedance parameter (Z parameter), admittance parameter (Y parameter) etc., are configured to one or one group of index that device carries out statistical modeling.The present embodiment adopts
As the index judging variodenser radio frequency statistical property.Wherein, Z parameter is impedance parameter, and for two-port network, four components of Z parameter are plural number, are respectively Z11, Z12, Z21, Z22; Imag function representation gets imaginary part, namely gets the imaginary part of (Z11-Z12); ω is angular frequency, ω=2 π * f, and f is frequency; ω can the key job frequency of selector, also can select one group of ω, asks the weighted mean of F under different ω as index.Preferably, the present embodiment adopts ω=2 π * 5E9 to calculate this index.
Step 06: based on the benchmark model in These parameters and step 04, calculates each component value in sub circuit model, units value of going forward side by side relative to desired value sensitivity analysis and sort, then choose the highest element of sensitivity;
Concrete, carry out component value relative to the sensitivity analysis of desired value and sort, comprise: near component value, the scale of index change in the step 05 of more all elements caused by the identical change ratio of component value sequence calculates each component value sorts to the sensitivity of this index, chooses the highest element of sensitivity and carries out statistical modeling.When choosing the highest element of sensitivity, the highest number of elements of selected sensitivity, for reaching the minimum number of elements needed for fit object, that is to say that selected element is for can meet matching demand, the element of the minimum number chosen successively by sensitivity height.Such as, according to actual fit solution, if only need an element just can reach fit object, just only with one, add sensitivity second successively as do not met high, the elements such as third high, can not play a significant role until matching demand can be met or newly add element.
It should be noted that, the computing method for sensitivity have a variety of.The strict calculating of sensitivity should be taked value of statistical indicant to treat analysis element respectively to carry out differentiate, but immediate derivation is comparatively difficult in so more complicated electronic circuit structure.A kind of numerical method is taked in the present embodiment, namely based on benchmark model, calculate all component values, element to be analyzed changes certain proportion on this benchmark model value a0 basis, as i.e. component value on a0 basis by two values changed up and down to certainty ratio, can be expressed as a1=a0 (1-Δ), a2=a0 (1+ Δ), here Δ=5%, all the other component values are constant, the change ratio of counting statistics index F0 (component value on a0 basis by two values changed up and down to certainty ratio), namely F1 (statistical indicator calculated when component value is a1) is calculated, F2 (statistical indicator calculated when component value is a2).The calculating of sensitivity is exactly the change ratio of change ratio divided by component value to be analyzed of statistical indicator; Sensitivity α can calculate according to following formula:
α＝︱(F1-F2)/F0︱/︱(a 1-a2)/a0︱。
Through calculating, the present embodiment chooses Cs, carries out statistical model modeling with Rch two elements.
Step 07: based on selected element, to all kinds of populations of parameters in the scalable formula of the element in step 02, to carry out parameter value respectively relative to the sensitivity analysis of component value and sorts, choose the parameter that sensitivity is the highest;
Concrete, determine the parameter of statistical modeling, respectively basic parameter group, biased correlation parameter group and device size correlation parameter group analyzed and sorted, selecting the parameter that sensitivity is the highest; When choosing the highest parameter of sensitivity, the highest number of parameters of selected sensitivity, for reaching the minimum parameters quantity needed for fit object, that is to say that selected parameter is for can meet matching demand, the parameter of the minimum number chosen successively by sensitivity height.Such as, according to actual fit solution, if only need a parameter just can reach fit object, just only with one, add sensitivity second successively as do not met high, the elements such as third high, can not play a significant role until matching demand can be met or newly add parameter.
For the element chosen, analyze the impact that the parameter in the scalable formula of this element changes component value respectively, thus determine the parameter should doing statistical treatment.In the present embodiment, following formula is used for Cs and Rch:
In above-mentioned formula, w, l, area, pj are the physical dimension of device, and Vgs is the operating voltage of device, temperature when T is devices function, and remaining variables is model parameter, and model parameter can be divided into several groups, and the first kind is that basic parameter is as C
mpc
mlc
madC
pdC
ldC
a, Rch0 Rbeta etc., Equations of The Second Kind is as dvgs0 vgn0 dV with bias voltage correlation parameter
chk
accdeng, the 3rd class is with device size correlation parameter as dl, and the 4th class is the parameter irrelevant with technological fluctuation, and such as temperature characterisitic correlation parameter is as dvgs_temp, vgn_temp etc.
It should be noted that, Parameter Sensitivity Analysis can take the numerical analysis method in above-mentioned steps 06 equally.
Step 08: based on selected parameter, the statistical property distribution of matching device, thus obtain the statistical model for characterizing device overall situation fluctuation distribution.
Concrete, the approximating method adopted, for adding statistical function to selected parameter and adjusting statistical function correlation parameter, adjusts the 3 σ values that each parameter finds out them here, determines their fluctuation pattern.Use P=P0+P_3 σ * agauss (0,1,3) to replace raw parameter P0, wherein, P_3 σ represents the parameter needing to adjust matching, and agauss is normal distyribution function, is also gauss of distribution function.0 to represent average μ be that 0,1 and 3 to represent 3 times of standard deviation 3 σ be 1.The numerical value of adjustment P_3 σ, emulates model and makes the statistical distribution of model emulation data level off to the statistical distribution of the test data in institute's step 03;
Fit procedure specifically comprises:
First, the device minimum for the non-ideal effects being reduced to bring by size in device device under test (is generally size maximum device in designed test structure, hereinafter referred large-size device) without index during applied bias, use the highest parameter of the sensitivity in basic parameter group selected in step 07 to carry out matching; Then, whether indicator-specific statistics characteristic when observing device applied bias meets matching requirement, as do not met, continues to add using the highest parameter of the sensitivity in biased correlation parameter group selected in step 07 to carry out matching; Then, whether the indicator-specific statistics characteristic of observing different size device meets matching requirement, as do not met, continues to add using the highest parameter of the sensitivity in device size correlation parameter group selected in step 07 to carry out matching; Complete above-mentioned fit procedure, namely obtain the statistical model for characterizing device overall situation fluctuation distribution.
Such as, carry out statistical model matching, first determine that basic parameter group sorts to the sensitivity of component value, select the highest parameter of sensitivity to carry out models fitting.Here, dC is selected respectively
lwith Rbeta, statistical property matching is carried out to large-size device.Whether can matching this device data when applied bias, as can not be matching then continued to use bias voltage correlation parameter group to carry out models fitting, carry out sensitivity sequence equally, the present embodiment chooses vgn0 and K if then observing this model
acccarry out matching.Whether observe the statistical property of different size device afterwards by matching, should continue to use the matching of size correlation parameter as failed matching, the present embodiment uses dl to carry out matching.The present embodiment does not consider statistical property difference during different temperatures characteristic.Through said process, this statistical model meets the statistical property distribution of the biased lower device of different size difference.
In sum, the statistical modeling method of radio frequency variodenser of the present invention, for different electronic circuit structure, the model that different scalable formula is formed, the present invention is applicable; And, based on mapping test data analysis, choose the index that can reflect device critical nature, the principal statistical characteristic both can effectively held for this process characteristic distributes, and ensures that again institute's Modling model can react the statistical distribution of the Primary Component performance that circuit designers is concerned about; Moreover based on parameter value to component value, component value is to the sensitivity analysis of desired value, and line parameter of going forward side by side is classified, can fast selecting the most effective statistical model parameter on current base model basis; Meanwhile, for the independence of different parameters group, the performance index based on correspondence extract statistical parameter value respectively successively, make modeling process clear fast.
Although the present invention discloses as above with preferred embodiment; right described embodiment is citing for convenience of explanation only; and be not used to limit the present invention; those skilled in the art can do some changes and retouching without departing from the spirit and scope of the present invention, and the protection domain that the present invention advocates should be as the criterion with described in claims.
Claims (10)
1. a statistical modeling method for radio frequency variodenser, described radio frequency variodenser is distributed on the crystal grain of wafer, it is characterized in that, comprises the following steps:
Step 01: the electronic circuit topological structure determining organs weight, determines the scalability formula that in described electronic circuit, each circuit component uses, and for the modeling of different size device, thus determining device modeling needs all model parameters of extraction;
Step 02: classify to parameter, is divided into basic parameter group, the population of parameters relevant to applied bias, with device size correlation parameter group, and the population of parameters irrelevant with technological fluctuation;
Step 03: based on radio frequency modeling demand, scattering parameter test is carried out to device, first part critical size device is selected to carry out the mapping test of scattering parameter, namely all scattering parameter test is carried out to scale device selected by all crystal grains, analyze the mapping test data of scattering parameter, obtain the wave characteristic of device scattering parameter on whole wafer, by observing the scattering parameter distribution under different operating frequency, scattering parameter distribution under different applied bias, the distribution of the different component of scattering parameter, determine the characteristic part in described test data with undulatory property or discreteness,
Step 04: to the above-mentioned characteristic calculating mean value in all crystal grains, the characteristic selecting surveyed device is golden die near the crystal grain corresponding to this mean value, and test the scattering parameter of all device under tests in this golden die, with the device test data of this golden die for fit object, extract the described model parameter in described step 01, set up the benchmark model not comprising technological fluctuation of device;
Step 05: based on the scattering parameter of described characteristic part and the network parameter that converted by scattering parameter, be configured to one or one group of index that device carries out statistical modeling;
Step 06: based on the benchmark model in These parameters and step 04, calculates each component value in sub circuit model, units value of going forward side by side relative to desired value sensitivity analysis and sort, then choose the highest element of sensitivity;
Step 07: based on selected element, to all kinds of populations of parameters in the scalable formula of described element in described step 02, carries out parameter value respectively relative to the sensitivity analysis of component value and sorts, choose the parameter that sensitivity is the highest;
Step 08: based on selected parameter, the statistical property distribution of matching device, thus obtain the statistical model for characterizing device overall situation fluctuation distribution.
2. the statistical modeling method of radio frequency variodenser according to claim 1, it is characterized in that, in described step 03, the described characteristic part with undulatory property or discreteness for: the difference of the maxima and minima of described test data in different crystal grain is greater than the characteristic part of the fluctuation threshold value set by modeling requirement with the ratio of described minimum value.
3. the statistical modeling method of radio frequency variodenser according to claim 1, is characterized in that, in described step 05, described index can reflect the characteristic of device under radio frequency operation condition; Described characteristic comprises effective capacitance value.
4. the statistical modeling method of radio frequency variodenser according to claim 1, it is characterized in that, in described step 06, carry out component value relative to the sensitivity analysis of desired value and sort, specifically comprise: near described component value, the scale that in the step 05 of more all elements caused by the identical change ratio of component value, index changes also sorts.
5. the statistical modeling method of radio frequency variodenser according to claim 4, is characterized in that, in described step 06, when choosing the highest element of sensitivity, the highest number of elements of selected sensitivity is for reaching the minimum number of elements needed for fit object.
6. the statistical modeling method of radio frequency variodenser according to claim 1, is characterized in that, described step 07 comprises: respectively basic parameter group, biased correlation parameter group, device size correlation parameter group are carried out to sensitivity analysis and sort.
7. the statistical modeling method of radio frequency variodenser according to claim 6, it is characterized in that, described step 07 comprises: when choosing the highest parameter of sensitivity, and the highest number of parameters of selected sensitivity is for reaching the minimum parameters quantity needed for fit object.
8. the statistical modeling method of radio frequency variodenser according to claim 1, is characterized in that, in described step 08, the approximating method adopted is for adding statistical function to selected parameter and adjusting statistical function correlation parameter.
9. the statistical modeling method of radio frequency variodenser according to claim 8, it is characterized in that, fit procedure in described step 08 specifically comprises: first, for the minimum device of the non-ideal effects being reduced to bring by device size without index during applied bias, the highest parameter of the sensitivity in basic parameter group selected in described step 07 is used to carry out matching; Then, whether indicator-specific statistics characteristic when observing device applied bias meets matching requirement, as do not met, continues to add using the highest parameter of the sensitivity in biased correlation parameter group selected in described step 07 to carry out matching; Then, whether the indicator-specific statistics characteristic of observing different size device meets matching requirement, as do not met, continues to add using the highest parameter of the sensitivity in device size correlation parameter group selected in described step 07 to carry out matching; Complete above-mentioned fit procedure, namely obtain the statistical model for characterizing device overall situation fluctuation distribution.
10. the statistical modeling method of radio frequency variodenser according to claim 1, is characterized in that, described radio frequency variodenser is MOS varactor.
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