CN118013719A - Evaluation method for accelerated degradation test of space MOSFET - Google Patents
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Abstract
The invention discloses a method for evaluating a space MOSFET accelerated degradation test, which comprises the following steps: collecting degradation data of the power device at different times under different levels of external stress; assuming that the service life T of the power device is subjected to normal distribution, adopting a distribution parameter estimation method corresponding to the normal distribution to obtain estimated values of the power device at all times under different stresses, wherein the method comprises the following steps: mean and variance of normal distribution; selecting a fitting function according to the change trend of the estimated value along with time; according to the stress type of the accelerated degradation experiment, selecting an acceleration model, and according to the estimated value, obtaining parameters of a fitting function by using a least square method; setting a failure threshold value, and identifying data points by using a RANSAC algorithm: substituting the estimated value into a selected fitting function, and judging that the fitting function is suitable for the data point when the error of the estimated value is within a set failure threshold range; when the error is out of the range of the set failure threshold value, the data point is regarded as abnormal value elimination; and performing secondary data fitting on the estimated value with the abnormal value removed to obtain a correction parameter of a fitting function, and obtaining a final degradation model. The invention takes the mixed model as an acceleration model, improves the accuracy of fitting function parameters, eliminates abnormal values in estimated values by using a RANSAC algorithm, and ensures that the obtained degradation model is more accurate.
Description
Technical Field
The invention relates to the field of reliability of space power supplies, in particular to a space MOSFET accelerated degradation test evaluation method.
Background
With the continuous development of Chinese aerospace tasks, higher reliability requirements are put forward on space spacecraft technology. The power subsystem is used as an energy supply 'heart' of the satellite, and the power controller is used as a control core of the power subsystem, so that the reliability of the power subsystem is important.
The performance degradation of the power supply controller is caused by the degradation of components in the circuit, and the MOSFET of the power supply controller is a key heating device and a device with higher failure rate, wherein the sum of the failure rates is up to more than 90%. However, when the circuit is in a working state, the performance parameters of all components in the circuit are not easy to directly measure, and even cannot be measured; on the other hand, due to the correlation of faults of electronic equipment, the health condition of a single component in a circuit cannot accurately and comprehensively describe the overall health condition of the circuit. MOSFETs are key switching functions in power controllers, however MOSFETs are long life semiconductor devices that degrade slowly under normal operating conditions by derating over a period of years, and thus conventional degradation assessment methods are difficult to implement for power MOSFETs.
Disclosure of Invention
The invention aims to obtain degradation data of a power MOSFET required by reliability evaluation in a short period by adopting a method of accelerating degradation test.
In order to achieve the above purpose, the invention provides a method for evaluating a spatial MOSFET accelerated degradation test, which specifically comprises the following steps:
s1: collecting degradation data of the power device at different times under different levels of external stress;
S2: assuming that the service life T of the power device is subjected to normal distribution, as shown in a formula (1), adopting a distribution parameter estimation method corresponding to the normal distribution, as shown in a formula (2), obtaining estimated values of the power device at various moments under different stresses, wherein the estimated values comprise: the mean and variance of the normal distribution, as in formula (3) and formula (4);
Wherein: t represents time, μ represents the mean value of the standard normal distribution, σ represents the standard deviation of the standard normal distribution;
Wherein: f represents a normal distribution function of life, i represents ordinal number, H represents temperature, T 0 represents humidity, sigma T' represents a correlation coefficient of life variance, A represents a life correlation coefficient, b represents a temperature correlation coefficient, phi represents a humidity correlation coefficient;
Wherein: mu s represents the life mean and sigma s represents the life variance;
S3: selecting a fitting function according to the change trend of the estimated value obtained in the step S2 along with time;
s4: according to the stress type of the accelerated degradation experiment, selecting an acceleration model as shown in formula (5), and obtaining parameters of a fitting function by using a least square method according to the estimated value obtained in the step S2;
Wherein: l (H, T) represents accelerated thermal stress aging life, b, c are undetermined model parameters, A is a constant, H is relative pressure, and T is absolute temperature;
s5: setting a failure threshold value, and identifying data points by using a RANSAC algorithm:
Substituting the estimated value obtained in the step S2 into the fitting function selected in the step S3, and judging that the fitting function is suitable for the data point when the error is within the range of the set failure threshold value;
When the error is out of the range of the set failure threshold value, the data point is regarded as abnormal value elimination;
S6: and (4) performing secondary data fitting on the estimated value with the abnormal value removed through the step (S4) to obtain the correction parameters of the fitting function, and obtaining the final degradation model.
Optionally, the external stress includes: temperature, voltage, current.
Optionally, the degradation data is obtained based on the measured drain current and drain voltage.
Optionally, the acceleration model comprises a hybrid model of a temperature model and an electrical stress model or a dual stress aging life hybrid model.
Optionally, the distribution parameter estimation method corresponding to the normal distribution is a maximum likelihood estimation method.
Alternatively, the voltage force range is selected based on a predetermined range of gate-source voltage forces.
Optionally, the temperature range is selected based on the temperature at which the space battery system is located.
Compared with the prior art, the technical scheme of the invention has at least the following beneficial effects:
(1) Compared with a single model selected in the prior art, the obtained parameter result of the fitting function is more accurate;
(2) In the invention, the RANSAC algorithm is adopted to remove abnormal values in the model parameter identification to obtain the correction parameters of the fitting function, so that the finally obtained degradation model is more accurate;
(3) The invention provides a component-level accelerated degradation test design scheme based on a degradation mechanism, models and evaluates the service life of the obtained degradation data by adopting a random process method, finally obtains the service life evaluation value of the tested object, and solves the problems of long period and high cost of the traditional test verification method.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings that are needed for the description of the embodiments will be briefly described below, it being obvious that the drawings in the following description are only some embodiments of the present invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a flowchart of a degradation model establishment under a single failure mechanism in a spatial MOSFET accelerated degradation test evaluation method of the present invention.
Fig. 2 is a schematic diagram of eliminating abnormal values by RANSAC algorithm in the evaluation method of the spatial MOSFET accelerated degradation test according to the present invention.
Fig. 3 (a) shows the drain voltage of a method for evaluating the accelerated degradation test of a spatial MOSFET according to the present invention.
Fig. 3 (b) is a drain current diagram of a method for evaluating a spatial MOSFET accelerated degradation test according to the present invention.
Fig. 4 is a graph of a least squares fit result in an embodiment of a method for evaluating accelerated degradation of a spatial MOSFET according to the present invention.
Fig. 5 is a graph of a RANSAC algorithm fitting result in an embodiment of a spatial MOSFET accelerated degradation test evaluation method according to the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made apparent and fully in view of the accompanying drawings, in which some, but not all embodiments of the invention are shown. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
In the description of the present invention, it should be noted that the directions or positional relationships indicated by the terms "upper", "lower", "left", "right", "vertical", "horizontal", "inner", "outer", etc. are directions or positional relationships based on the drawings, are merely for convenience of describing the present invention and simplifying the description, and do not indicate or imply that the apparatus or elements referred to must have a specific orientation, be constructed and operated in a specific orientation, and thus should not be construed as limiting the present invention. Furthermore, the terms "first," "second," and "third" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance.
In the description of the present invention, it should be noted that, unless explicitly specified and limited otherwise, the terms "mounted," "connected," and "connected" are to be construed broadly, and may be either fixedly connected, detachably connected, or integrally connected, for example; may be a mechanical connection; can be directly connected or indirectly connected through an intermediate medium, and can be communication between two elements. The specific meaning of the above terms in the present invention will be understood in specific cases by those of ordinary skill in the art.
The invention provides a new idea for model parameter identification by utilizing an accelerated degradation experiment, which is an experimental method in reliability research, wherein a flow for establishing a degradation model under a single failure mechanism is shown in a figure 1, and the specific steps are as follows:
S1: accelerated degradation experiments were designed and performed to collect degradation data.
The design of accelerated degradation testing protocols generally requires consideration of stress loading patterns, accelerated stress types, accelerated stress levels, number of test samples, test tail-off time, monitoring parameter determinations, and the like.
Because the service life of the device is longer and the degradation amount is small under the conventional condition, the degradation characteristic parameters of the device need to be added with larger stress for testing. The invention selects external stress affecting the reliability of the power MOSFET, comprising: temperature, voltage and current, and three levels of each stress are selected, 27 groups of tests are theoretically required, the number of the test groups is reduced to 9 groups by adopting a uniform orthogonal test method, and degradation characteristic parameters of the power MOSFET are tested at fixed time. In the invention, degradation characteristic parameters obtained under certain test conditions and at moment are regarded as one data point.
For the selection of the stress range, the failure mechanism is changed by applying too high acceleration stress in the accelerated degradation test, and the effect of acceleration is not realized by too low acceleration stress. Meanwhile, for the universality of the device time stress parameter degradation model, the span of stress distribution should be made larger. The invention determines the range of the gate-source voltage stress through a pre-test. When the temperature is lower than the range, the time required for the assay withdrawal is longer, and the test time cost is higher; above this range, the device may suffer from direct breakdown of the gate under electrical stress. The temperature stress range of the invention is selected according to the environment of the space power system, so as to compare the high temperature with the normal temperature.
S2: and extracting degradation characteristic parameter items, and calculating estimated values at all moments under different stresses.
In the study of multi-stress accelerated life models, a lognormal distribution is often used to describe failure in a product due to fatigue fracture caused by mechanical stress. Assuming that the logarithm of the lifetime T of the power device obeys a normal distribution, the distribution function of T is:
Wherein, mu and sigma are the mean value and standard deviation of standard normal distribution respectively.
The failure probability density function f (t) is:
the reliability function R (t) is:
The failure rate function of the lognormal distribution is:
The final result is the life average The method comprises the following steps:
The standard deviation σ s is:
The life t R is:
Wherein K 1-r represents the upper quantile of the normal distribution function.
The average life to be obtainedSubstituting the model into the failure probability density function to obtain the time stress expression of the model as follows:
Selecting a distribution parameter estimation method corresponding to normal distribution, processing data by using a maximum likelihood estimation method to obtain related parameters in an accelerated life model, wherein the maximum likelihood equation is as follows:
Wherein H represents temperature, T 0 represents humidity, sigma T', A, b and phi are fixed values, and the fixed values are calculated through the combination of experimental data.
The calculation formula of the reliability function R (T, H, T 0) is:
the average life of the product is as follows:
The failure rate of the product under acceleration stress is as follows:
parameter estimation is performed by maximum likelihood estimation:
The average life and variance were calculated as:
s3: and selecting a fitting function according to the change trend of the estimated value with time.
And selecting a fitting function according to the change trend of the parameter estimation value along with time, and establishing a degradation model about time and stress level. Assuming that the degradation amount of the product at the time t is y (t), degradation failure can be defined as when the degradation amount changes with time to reach a predetermined failure standard D, the product fails, and the corresponding time is the service life (or failure time) of the product. The lifetime of the product can thus be defined as:
TD＝inf{t：y(t)＝Df；t≥0} (18)。
In some practical problems, there is often encountered a case where the reliability of a product is represented by the ratio of the degradation amount y (t) with respect to its initial value y (0) (or the initial value of the degradation amount of the product), and when y (t)/y (0) reaches a failure criterion, it is determined that the product fails, and the corresponding failure life of the product is:
S4: selecting an appropriate acceleration model, comprising: and obtaining fitting function parameters by using a least square method.
And selecting a parameter mixed model or a dual-stress aging life mixed model established by a single model such as a temperature model (such as an Arrhenius Wu Yasi model), an electric stress model (such as an tourmaline model) and the like as an acceleration model, and obtaining parameters of a fitting function by using a least square method.
The Arrhenius model is widely used for describing the influence of temperature on the chemical reaction rate, and then is gradually popularized to describe the influence of temperature on the performance degradation rate of various products, wherein the expression of the Arrhenius model is as follows:
wherein R is the reaction rate, A is a constant related to the reaction process, E a is the activation energy, K is the Boltzmann constant, and T is the reaction temperature.
The tourmaline model is a function acceleration model which is derived according to the quantum mechanics principle and indicates that the service life characteristic of a product is absolute temperature, and is expressed as follows:
where L is a life characteristic, A and B are coefficients to be determined, and V is a stress value in absolute units.
The mixing model is as follows:
wherein L (H, T) represents accelerated thermal stress aging life, b, c are undetermined model parameters, A is a constant, H is relative pressure, and T is absolute temperature.
The basic principle of the least squares method is to choose parameters in the degradation model to minimize the sum of squares of the deviations of the observed values from the corresponding function values. In general, the least square method is to apply the least square method principle to calculate the parameter estimation after the model is properly transformed.
Let the function y=f i(xi, w, λ) take n non-identical values x 1,x2,...,xn of x. And observing the corresponding dependent variable Y to obtain n pairs of observed values.
According to the principle of least square method, calculateOrder the
Such that:
Then The least square estimation of w, λ, and the method of obtaining the least square estimation is called the least square method.
S5: setting a failure threshold value, screening data points by using a RANSAC algorithm, and judging whether the error test is qualified or not.
Considering the error of the degradation data, a failure threshold value can be set according to the heating value of the MOSFET, and the RANSAC algorithm is utilized to remove abnormal values. The RANSAC algorithm, a random sample consensus algorithm (Random Sample Consensus), was originally developed in the machine vision literature and is a fast algorithm for eliminating outliers that estimate camera pose. This method is particularly effective when the data contains a large number of outliers; but to obtain good performance, normal values should be at least 50% of the dataset. RANSAC is an iterative method that uses the smallest subsamples of the observed data to estimate the model parameters. For example, to fit a line in a two-dimensional plane, there are two unknown parameters a and b, so a minimum subsamples requires two data points. The algorithm requires a minimum subsamples and uses voting to detect outliers. RANSAC can be summarized as the following steps:
1) Selecting a random minimum subsamples from the observed data;
2) Fitting the model using the smallest subsamples;
3) Test data are screened using a set threshold delta. If the error of the model is less than the defined threshold, it is considered an internal error and the fitted model will get a vote. Otherwise, it is regarded as outlier rejection. A subsampled of observation data for two points is included in fig. 2. The dashed line is drawn green. Two parallel lines show an acceptable threshold delta. Any point outside these lines is considered an outlier.
4) Repeating the steps 1 to 3 to form m sub-samples and selecting a model with the largest number of internal points;
5) Data fitting is again performed using, for example, the least squares method, this time by fitting a model to all selected normal values, which is the output of the algorithm.
S6: and performing secondary data fitting on the screened estimated values to obtain correction parameters of a fitting function, and obtaining a final degradation model.
Correcting parameters of the fitting function in the RANSAC algorithm by the estimated value after the abnormal value is removed to obtain corrected parameters of the fitting function, and obtaining a final degradation model. And extrapolating the degradation model of the fitting result to an invalidation threshold value to obtain the invalidation time of the power device, thereby obtaining the residual service life of the MOSFET.
Examples
S1: accelerated degradation experiments were designed and performed to collect degradation data.
The accelerated degradation test of the invention selects the model ceramic package MOSFET as a test object, the temperature stress range is 75-175 ℃, and the electric stress range is 45-52V. In order to save the time cost of the test, the test scheme is selected to be enough to perform time stress degradation modeling and reflect the stress combination of the power device under different electric and temperature degradation conditions, and the test of all the stress combinations is not performed. The stress combinations selected in the invention are shown in table 1, wherein "1" represents test stress, and "0" represents non-test stress, and specific stress loading is determined through pre-test.
Table 1: stress application scheme for accelerated degradation test
The on-resistance is used as an indication of MOSFET failure. Degradation data is obtained from the measured drain current and drain voltage during the accelerated aging test. The drain voltage and drain current data are shown in fig. 3 (a) and 3 (b), respectively.
S2: and extracting degradation characteristic parameter items, and calculating estimated values at all moments under different stresses.
In this embodiment, equations (1) to (17) are programmed and the degradation data is substituted to obtain the estimated values at each time under different stresses, where the estimated values are shown as black values in fig. 4 and 5.
S3: and selecting a fitting function according to the change trend of the estimated value with time.
According to the change trend of the estimated value with time, the degradation process of the power device can be seen to increase exponentially. Thus, the degradation process of a power MOSFET is described using an exponentially fitting function as the degradation model. The exponential function describing the degradation process is as follows:
ΔRon＝βeθx (24)。
the MOSFET degradation model is logarithmic as follows:
ln(ΔRon)＝lnβ+θ·x (25)。
The linear expression after taking the logarithm can be written as:
Y＝B+θ·x (26)。
S4: selecting an appropriate acceleration model, comprising: and obtaining fitting function parameters by using a least square method.
The mixed model (5) is selected as an acceleration model, fitting function parameters B and theta are obtained through least square fitting, the results are 0.620022 and 1.7469e -6 respectively, and the fitting result is shown in fig. 4.
The mixing model is as follows:
Wherein L (H, T) represents accelerated thermal stress aging life, b, c are undetermined model parameters, A is a constant, H is relative pressure, and T is absolute temperature.
S5: setting a failure threshold value, identifying data points by using a RANSAC algorithm, and judging whether the error test is qualified or not.
Considering that the data has obvious measurement errors, the failure threshold value is set to be 0.2, and the data is processed by using a RANSAC algorithm.
S6: and performing secondary data fitting on the screened estimated values to obtain correction parameters of a fitting function, and obtaining a final degradation model.
And removing data points with abnormal values according to the RANSAC algorithm, and obtaining a new fitting result after secondary fitting, as shown in figure 5, wherein a green line is the RANSAC algorithm fitting condition, and a red line is the original least square fitting condition.
The resulting fit was lnr= 1.7469e -6 -0.478 where β= 0.620022, θ= 1.7469e -6. A value of 18174.3 for x, i.e. failure after 18174 acceleration cycles, can be obtained.
In summary, the invention establishes a fitting function according to the degradation quantity obeying distribution parameter estimation values at each moment, obtains fitting function parameters through a least square method, eliminates abnormal values by using a RANSAC algorithm, obtains correction parameters of the fitting function again, and obtains a degradation model. The invention takes the mixed model as an acceleration model, improves the accuracy of fitting function parameters, eliminates abnormal values in estimated values by using a RANSAC algorithm, and ensures that the obtained degradation model is more accurate.
While the present invention has been described in detail through the foregoing description of the preferred embodiment, it should be understood that the foregoing description is not to be considered as limiting the invention. Many modifications and substitutions of the present invention will become apparent to those of ordinary skill in the art upon reading the foregoing. Accordingly, the scope of the invention should be limited only by the attached claims.
Claims (7)
1. The method for evaluating the accelerated degradation test of the space MOSFET is characterized by comprising the following steps of:
s1: collecting degradation data of the power device at different times under different levels of external stress;
S2: assuming that the service life T of the power device is subjected to normal distribution, as shown in a formula (1), adopting a distribution parameter estimation method corresponding to the normal distribution, as shown in a formula (2), obtaining estimated values of the power device at various moments under different stresses, wherein the estimated values comprise: the mean and variance of the normal distribution, as in formula (3) and formula (4);
Wherein: t represents time, μ represents the mean value of the standard normal distribution, σ represents the standard deviation of the standard normal distribution;
Wherein: f represents a normal distribution function of life, i represents ordinal number, H represents temperature, T 0 represents humidity, sigma T' represents a correlation coefficient of life variance, A represents a life correlation coefficient, b represents a temperature correlation coefficient, phi represents a humidity correlation coefficient;
Wherein: mu s represents the life mean and sigma s represents the life variance;
S3: selecting a fitting function according to the change trend of the estimated value obtained in the step S2 along with time;
s4: according to the stress type of the accelerated degradation experiment, selecting an acceleration model as shown in formula (5), and obtaining parameters of a fitting function by using a least square method according to the estimated value obtained in the step S2;
Wherein: l (H, T) represents accelerated thermal stress aging life, b, c are undetermined model parameters, A is a constant, H is relative pressure, and T is absolute temperature;
s5: setting a failure threshold value, and identifying data points by using a RANSAC algorithm:
Substituting the estimated value obtained in the step S2 into the fitting function selected in the step S3, and judging that the fitting function is suitable for the data point when the error is within the range of the set failure threshold value;
When the error is out of the range of the set failure threshold value, the data point is regarded as abnormal value elimination;
S6: and (4) performing secondary data fitting on the estimated value with the abnormal value removed through the step (S4) to obtain the correction parameters of the fitting function, and obtaining the final degradation model.
2. The method for evaluating the accelerated degradation test of a spatial MOSFET according to claim 1, wherein said external stress comprises: temperature, voltage, current.
3. The method for evaluating the accelerated degradation test of a spatial MOSFET according to claim 1, wherein the degradation data is obtained based on a measured drain current and drain voltage.
4. The method of claim 1, wherein the acceleration model comprises a hybrid model of a temperature model and an electrical stress model or a hybrid model of dual stress aging life.
5. The method for evaluating the accelerated degradation test of a spatial MOSFET according to claim 1, wherein the distribution parameter estimation method corresponding to the normal distribution is a maximum likelihood estimation method.
6. The method of claim 1, wherein the voltage force range is selected based on a predetermined range of gate-source voltage force determined by a pre-experiment.
7. The method for evaluating the accelerated degradation test of a spatial MOSFET of claim 1 wherein the temperature range is selected based on the temperature at which the space battery system is located.
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