A method for testing the life of an IGBT device based on multi-parameter distribution during manufacturing

Abstract

The present invention relates to an IGBT device life test method based on multi-parameter distribution during manufacturing, belonging to the technical field of structural component life test, and the present invention solves the problem of low test efficiency and low accuracy of the test method in the prior art. The method of the present invention starts from the manufacturing of the IGBT device, combines the use scenario, and samples the circuit working conditions of the IGBT device when manufacturing the IGBT device, monitors and extracts the change data of the IGBT dynamic junction temperature under the sampling working conditions, calculates the life by using the rain flow counting method and the life test model, and finally fits the calculated different lifespans to obtain the life distribution for adjusting the manufacturing process. The method of the present invention has high test efficiency and high accuracy, adjusts the process parameters based on the product life test during the manufacturing process, and then manufactures, with high accuracy, reducing the cost of the user; the product utilization rate of the acceptance sampling test is high, the amount of information is sufficient, the test cost is low, and the acceptance sampling has high accuracy in identifying batch products.

Description

Technical Field

[0001] The invention belongs to the technical field of semiconductor testing, and in particular relates to an IGBT device life testing method based on multi-parameter distribution during manufacturing. Background Art

[0002] Whether from the perspective of science and technology or economic development, the importance of semiconductor devices has a huge impact on modern society. Testing or measuring various performance or status during the manufacturing and production process of semiconductor devices can greatly improve the efficiency and quality of the final product and reduce costs. There are many testing or measurement methods, such as Chinese patent: CN110600390B, Chinese patent applications CN112366146A, CN116581061A and CN115708190A.

[0003] Power semiconductor devices IGBT are widely used in automobiles, energy, and rail transportation, and their reliability plays an important role in the normal operation of the entire device, equipment, and system. However, in the actual use of power semiconductor devices IGBT devices, the changes in temperature and time are complex and random. At the same time, there are many factors that affect the life of IGBT devices, which makes the existing life test methods need to consider the influence of too many parameters when conducting life tests, making the existing test methods low in efficiency and accuracy. In addition, most tests are performed after the power semiconductor device IGBT is produced and put into use, which will increase the user's cost of use and is not convenient for timely adjustment of the manufacturing parameters and processes during manufacturing. In addition, different users have different requirements for the quality and reliability of IGBT devices. Blindly testing samples will lead to insufficient utilization of products put into acceptance sampling tests, insufficient information, high test costs, and low accuracy in identifying batches of products by acceptance sampling. Summary of the invention

[0004] In view of the above problems, the present invention provides an IGBT device life test method based on multi-parameter distribution during manufacturing, which solves the problems of low test efficiency and accuracy of the test method in the prior art, and low accuracy when adjusting process parameters during the manufacturing process based on product life test. In addition, different users have different requirements for the quality and reliability of IGBT devices. Blindly testing samples will lead to insufficient utilization of products put into acceptance sampling tests, insufficient information, high test costs, and low accuracy of acceptance sampling in identifying batches of products.

[0005] The present invention provides an IGBT device life test method based on multi-parameter distribution during manufacturing, and the specific steps are as follows:

[0006] Step S1, determining the model and application scenario of the IGBT device;

[0007] Step S2, manufacturing an IGBT device according to the determined IGBT device model;

[0008] Step S3, determining the electrical parameters and ambient temperature of the circuit where the IGBT device is located, and the variation range of each electrical parameter according to the application scenario;

[0009] Step S4, sampling and obtaining IGBT devices of a determined model;

[0010] Step S5: based on the maximum value and minimum value of each electrical parameter, obtain the mean value and variance of each electrical parameter;

[0011] Step S6, based on the mean and variance of each electrical parameter, according to the Monte Carlo method, obtaining the absolute error of the parameter sampling result of the corresponding electrical parameter;

[0012] Step S7, obtaining the selected sampling times based on the absolute error values ​​of all electrical parameters;

[0013] Step S8, sampling each electrical parameter of the circuit where the IGBT device is located according to the selected sampling number to obtain a plurality of electrical parameter sampling values;

[0014] Step S9, under the determined ambient temperature and external heat dissipation conditions of the IGBT device, and under the multiple electrical parameter sampling values ​​obtained in step S8, respectively apply multiple different electrical stress values ​​to the sampled IGBT devices; use an infrared thermal imager to monitor the change of the junction temperature of the IGBT device in real time, and obtain a junction temperature curve corresponding to different electrical stress values; obtain a junction temperature data set of the IGBT device based on the junction temperature curve; obtain multiple sets of junction temperature fluctuation parameters of temperature cycles based on the junction temperature data set of the IGBT device;

[0015] Step S10, obtaining the life cycle number of the IGBT device based on the temperature cycle junction temperature fluctuation parameter;

[0016] Step S11: Based on the life cycle number obtained in step S10 N f Obtain the life cycle test value of the IGBT device;

[0017] Step S12: Determine whether the life span variation range and distribution of the IGBT device meet the user's requirements. If so, obtain the IGBT device finished product. If not, adjust the manufacturing process.

[0018] Optionally, the mean and variance of each electrical parameter in step S5 are expressed as follows:

[0019] mi -3 s i = minutes i

[0020] m i +3 s i = max i

[0021] in, m i Indicates i The mean value of the electrical parameters; s i Indicates i The variance of the electrical parameters; minutes i Indicates i Minimum value of electrical parameters; max i Indicates i The maximum value of an electrical parameter.

[0022] Optionally, the absolute error of the parameter sampling result of the electrical parameter in step S6 is expressed as:

[0023]

[0024] in, i Indicates i The absolute error of the parameter sampling results of the electrical parameters; i Indicates i Parameter sampling values ​​of various electrical parameters; i Indicates i The expected value of the parameter sampling value of the electrical parameter; N i Indicates i The number of parameter samplings for each electrical parameter, i= 1,2, …I , I The total number of categories representing electrical parameters; Represents a normal distribution a Quantile.

[0025] Optionally, the specific steps of obtaining the selected sampling times based on the absolute error values ​​of all electrical parameters in step S7 are:

[0026] Step S71, determining the range of parameter sampling results of each electrical parameter;

[0027] Step S72: Substitute the absolute error of the parameter sampling results of each electrical parameter into the absolute error inequality , get the sampling times corresponding to each electrical parameter N i ;

[0028] Step S73: Obtain the selected sampling number based on the sampling number corresponding to each electrical parameter N , the expression is:

[0029] N =max{ N 1, N 2,…, N I}.

[0030] Optionally, in step S9, a plurality of sets of junction temperature data sets are processed using a rain flow counting method to obtain temperature cycle junction temperature fluctuation parameters.

[0031] Optionally, in step S10, the life cycle number of the IGBT device is obtained based on the temperature cycle junction temperature fluctuation parameter. , the expression is:

[0032]

[0033] in, Indicates that based on j The life cycle number of the IGBT device with the junction temperature fluctuation parameter of the group temperature cycle; Δ T j Indicates j The temperature rise of the junction temperature cycle, T j,m Indicates j The intermediate temperature value of the junction temperature cycle, j =1,2,… n , n is the total number of groups of junction temperature cycle data; is the rated voltage of the IGBT device; is the voltage applied to the IGBT device; I rated is the rated current of the IGBT device, i applied is the current applied to the IGBT device; is the turn-on time of the IGBT device; A, , , and is the constant fitted by the power cycling experiment; is the Boltzmann constant; Ea is the excitation energy of the silicon chip.

[0034] Optionally, in step S11, the number of life cycles obtained in step 10 is The specific steps to obtain the life cycle test value of the IGBT device are:

[0035] Step S111: Based on the life cycle number Obtain the cumulative lifetime damage ratio of IGBT devices LC , the expression is:

[0036]

[0037] in, Indicates j Number of junction temperature cycles;

[0038] Step S112: obtaining a life cycle test value based on the accumulated life damage ratio of the IGBT device,

[0039] The expression is:

[0040]

[0041] in, Nr It is the life cycle test value of the IGBT device.

[0042] Optionally, a reference average absolute error value is used to replace the absolute error of the parameter sampling result of the electrical parameter; the reference average absolute error value is expressed as:

[0043]

[0044] in, It is i The reference average absolute error value of the absolute error of the electrical parameter sampling results; m i Indicates i The mean value of the electrical parameters.

[0045] Compared with the prior art, the present invention has at least the following beneficial effects:

[0046] (1) The method of the present invention has high testing efficiency and high accuracy.

[0047] (2) The method of the present invention has a high utilization rate of products put into acceptance sampling test, sufficient information, low test cost, and high accuracy in identifying batches of products through acceptance sampling. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] The drawings are only for the purpose of illustrating particular embodiments and are not to be construed as limiting the invention.

[0049] Figure 1The present invention is a flow chart of a method for testing the life of an IGBT device based on multi-parameter distribution during manufacture. DETAILED DESCRIPTION

[0050] In order to more clearly understand the above-mentioned purpose, features and advantages of the present invention, the present invention is further described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be noted that, in the absence of conflict, the embodiments of the present invention and the features in the embodiments can be combined with each other. In addition, the present invention can also be implemented in other ways different from those described herein, and therefore, the protection scope of the present invention is not limited by the specific embodiments disclosed below.

[0051] A specific embodiment of the present invention, as Figure 1 , provides an IGBT device life test method based on multi-parameter distribution during manufacturing, the specific steps are as follows:

[0052] Step S1, determining the model and application scenario of the IGBT device;

[0053] Step S2, manufacturing an IGBT device according to the determined IGBT device model;

[0054] Step S3, determining the electrical parameters of the circuit where the IGBT device is located, the ambient temperature and the external heat dissipation conditions of the IGBT device, as well as the variation range of each electrical parameter according to the application scenario;

[0055] Optionally, the variation range includes the maximum value and the minimum value of each electrical parameter;

[0056] Step S4, sampling and obtaining IGBT devices of a determined model;

[0057] Step S5: based on the maximum value and minimum value of each electrical parameter, obtain the mean value and variance of each electrical parameter;

[0058] The mean and variance of each electrical parameter are expressed as:

[0059] m i -3 s i = minutes i

[0060] m i +3 s i = max i

[0061] in, m i Indicates iThe mean value of the electrical parameters; s i Indicates i The variance of the electrical parameters; minutes i Indicates i Minimum value of electrical parameters; max i Indicates i The maximum value of an electrical parameter.

[0062] Step S6: Based on the mean and variance of each electrical parameter, the absolute error of the parameter sampling result of the corresponding electrical parameter is obtained according to the Monte Carlo method. The expression is:

[0063]

[0064] in, i Indicates i The absolute error of the parameter sampling results of the electrical parameters; i Indicates i Parameter sampling values ​​of various electrical parameters; Indicates i The expected value of the parameter sampling value of the electrical parameter;

[0065] Furthermore, the absolute error of the electrical parameter sampling result is subjected to benchmark averaging to obtain a benchmark average absolute error value, which is expressed as:

[0066]

[0067] in, It is i The reference average absolute error value of the absolute error of the electrical parameter sampling results; m i Indicates i The mean value of the electrical parameters.

[0068] The present invention performs benchmark averaging processing on the absolute error of a certain sampling result, so that the judgment of the sampling error is more intuitive and accurate.

[0069] Step S7, obtaining the selected sampling times based on the absolute error values ​​of all electrical parameters;

[0070] Preferably, the reference averaged absolute error value is used to replace the absolute error of the appliance parameter sampling result to perform subsequent steps.

[0071] Step S71, determining the range of parameter sampling results of each electrical parameter;

[0072] Step S72: Substitute the absolute error of the parameter sampling results of each electrical parameter into the absolute error inequality , get the sampling times corresponding to each electrical parameter N i .

[0073] in, N i Indicates i The number of parameter samplings for each electrical parameter, i= 1,2, …I , I The total number of categories representing electrical parameters; Represents a normal distribution a Quantile;

[0074] Furthermore, when the confidence level is 95%, =1.96. According to the standard normal distribution α quantile table, ,in, X represents a random variable, P represents probability;

[0075] Step S73: Obtain the selected sampling number based on the sampling number corresponding to each electrical parameter N , the expression is:

[0076] N =max{ N 1, N 2,…, N I}.

[0077] The present invention can determine the sampling times more scientifically and reduce errors.

[0078] Step S8: According to the selected sampling times N Sampling various electrical parameters of the circuit where the IGBT device is located to obtain multiple electrical parameter sampling values;

[0079] Step S9, under the determined ambient temperature and external heat dissipation conditions of the IGBT device, and under the multiple electrical parameter sampling values ​​obtained in step S8, apply multiple different electrical stress values ​​to the sampled IGBT devices respectively; use an infrared thermal imager to monitor the change of the junction temperature of the IGBT device in real time to obtain a junction temperature curve corresponding to different electrical stress values; obtain a junction temperature data set of the IGBT device based on the junction temperature curve; obtain multiple sets of junction temperature fluctuation parameters of temperature cycles based on the junction temperature data set of the IGBT device.

[0080] Optionally, a plurality of sets of junction temperature data sets are processed using a rain flow counting method to obtain temperature cycle junction temperature fluctuation parameters.

[0081] Among them, the temperature cycle junction temperature fluctuation parameters include the first j Temperature rise of junction temperature cycle Δ T j and j Intermediate temperature value of junction temperature cycle T j,m , j =1,2,… n , n is the total number of groups of junction temperature cycle data.

[0082] Step S10: Obtaining the life cycle number of the IGBT device based on the temperature cycle junction temperature fluctuation parameter , the expression is:

[0083]

[0084] in, Indicates that based on j The life cycle number of IGBT devices with junction temperature fluctuation parameters of group temperature cycle; is the rated voltage of the IGBT device; is the voltage applied to the IGBT device; I rated is the rated current of the IGBT device, i applied is the current applied to the IGBT device; is the turn-on time of the IGBT device; A, , , and is the constant fitted by the power cycling experiment; is the Boltzmann constant, specifically, = 8.134j / (mol·K); Ea is the excitation energy of the silicon chip, specifically, Ea Approximately equal to 0.8eV.

[0085] Step S11: Based on the life cycle number obtained in step 10 Obtain the life cycle test value of the IGBT device.

[0086] Step S111: Based on the life cycle number Obtain the cumulative lifetime damage ratio of IGBT devices LC , the expression is:

[0087]

[0088] in, It is the number of cycles of the jth group of junction temperature cycles obtained in the corresponding rain flow counting method.

[0089] Step S112: obtaining a life cycle test value based on the accumulated life damage ratio of the IGBT device,

[0090] The expression is:

[0091]

[0092] in, Nr It is the life cycle test value of the IGBT device.

[0093] Step S12: determine whether the life cycle test value of the IGBT device meets the use requirements of the user. If so, obtain the IGBT device finished product. If not, adjust the manufacturing process.

[0094] Step S13, obtaining multiple life cycle test values ​​Nr according to the method of steps 1-11, fitting the frequency histogram and life probability density distribution curve of the multiple life cycle test values ​​Nr, and obtaining the life variation range and distribution of the IGBT device.

[0095] It can be understood that if the life cycle test value is greater than or equal to the life cycle threshold, the IGBT device is qualified; if the life cycle test value is less than the life cycle threshold, the IGBT device is unqualified.

[0096] Furthermore, in step S3, when sampling to obtain the IGBT device of the determined model, the IGBT device of the determined model is sampled using the detection method for accepting the IGBT device, and the specific steps are as follows:

[0097] Step S31, constructing agreed risks, acceptance indicators and acceptance criteria for both parties;

[0098] Step S32, constructing a calculation formula for theoretical risks of both parties;

[0099] Step S33: construct a reliability acceptance sampling scheme design optimization model based on the agreed bilateral risks, acceptance indicators, acceptance criteria and the calculation formula of theoretical bilateral risks;

[0100] Step S34: constructing a batch product degradation model for simulating and obtaining degradation test data;

[0101] Step S35. Construct a reliability acceptance sampling degradation model, and obtain a reliability function of the batch product considering margin degradation according to the reliability acceptance sampling degradation model; then estimate the parameters of the reliability acceptance sampling degradation model according to the degradation test data, substitute them into the reliability function of the batch product considering margin degradation, and then calculate the reliability of the batch product considering margin degradation using the reliability function of the batch product considering margin degradation determined by the parameters;

[0102] Step S36, according to the reliability of the batch product considering the margin degradation, the theoretical bilateral risk is calculated using the calculation formula of the theoretical bilateral risk;

[0103] Step S37, based on the theoretical bilateral risk of step S36 and the agreed bilateral risk of step S31, the reliability acceptance sampling scheme of step S33 is used to design an optimization model, and a reliability acceptance sampling scheme that takes margin degradation into account is output.

[0104] Furthermore, the agreed risks of both parties in step S31 include the agreed production risks. and agreed user risks ; Among them, the agreed production party risk It is the maximum risk that the producer can bear, agreed upon by both parties in advance, and the risk of the user is agreed upon. It is the maximum risk that the user can bear, as agreed in advance by both parties;

[0105] The acceptance index in step S31 includes the production acceptance index and user acceptance indicators ; Among them, the production acceptance index is stricter than the user acceptance index, expressed as .

[0106] Furthermore, the receiving criteria in step S31 include and ;in, and are the preset initial value of performance degradation and the preset performance degradation rate, respectively, and and All are fixed values; is the initial value of product performance degradation; It is the performance degradation rate of the product under the stress level at any time.

[0107] Furthermore, the calculation formula of the theoretical risk of both parties in step S32 is expressed as:

[0108] ;

[0109] in, Indicates reliability; and They are the theoretical production risk and theoretical user risk to be approved respectively; represents the reliability function of the batch product considering margin degradation, which is calculated by the reliability acceptance sampling degradation model. s is the stress level, such as: preset temperature; Acceptance sampling plan for reliability; Sample size for reliability acceptance sampling; Time, specifically refers to the test time in reliability acceptance sampling; The detection time interval is equally spaced.

[0110] Furthermore, the step S33 specifically includes the following steps:

[0111] Reliability Acceptance Sampling Plan As the decision variable of the reliability acceptance sampling scheme design optimization model; the optimization goal of the reliability acceptance sampling scheme design optimization model is constructed as the minimum error between the agreed producer risk and the theoretical producer risk, and the minimum error between the agreed user risk and the theoretical user risk, and the decision variable design space is established, which is set as the reliability acceptance sampling sample size , Test time in reliability acceptance sampling and equally spaced detection time intervals The minimum and maximum values ​​of , so the reliability acceptance sampling scheme design optimization model is expressed as:

[0112]

[0113] in, and They represent the minimum and maximum values ​​of the reliability acceptance sampling sample size respectively; and They represent the minimum and maximum test time in reliability acceptance sampling respectively; and Respectively represent the minimum and maximum values ​​of equally spaced detection time intervals.

[0114] Furthermore, the reliability acceptance sampling sample degradation model in step S35 is expressed as:

[0115]

[0116] Among them, the reliability acceptance sampling sample degradation model represents a normal uncertain distribution; , , , All of them are the parameters of the reliability acceptance sampling sample degradation model that conforms to the characteristics of the degradation test data; is the initial value of the performance degradation of the product in the sampled product, and the mean is , the standard deviation is Normal uncertainty distribution of ; is the indicator function; The mean is , the standard deviation is Normal uncertainty distribution of ; C ( s ) represents a mean of 0 and a standard deviation of s Normal uncertainty distribution of ; is the diffusion coefficient; The mean is 0 and the standard deviation is The normal uncertainty distribution of To be determined based on degradation test data The index of .

[0117] Furthermore, the reliability function of the batch product considering margin degradation in step S35 is expressed as:

[0118] ;

[0119] in, is a predetermined product performance threshold; is the product performance margin; reliability function It is the confidence that the product performance margin is greater than 0.

[0120] Furthermore, the step S35 of estimating the parameters of the reliability acceptance sampling degradation model based on the degradation test data specifically refers to estimating several groups of unknown parameters in the reliability acceptance sampling degradation model using an uncertain weighted least squares algorithm based on the degradation test data; each group of unknown parameters includes , , , , , , and .

[0121] Further, the step S35 in which the reliability function of the batch product considering margin degradation is substituted, and then the reliability function of the batch product considering margin degradation is calculated using the reliability function of the batch product considering margin degradation determined by parameters to obtain the reliability of the batch product considering margin degradation, specifically refers to substituting several groups of unknown parameters into the reliability function of the batch product considering margin degradation, respectively obtaining several groups of reliability functions of the batch product considering margin degradation determined by parameters, and then calculating the reliability of the batch product considering margin degradation using the reliability function of the batch product considering margin degradation determined by parameters, and then respectively substituting them into the calculation formula of the theoretical two-party risk to calculate the theoretical production risk and theoretical user risk .

[0122] Further, step S37 specifically includes:

[0123] According to the calculated theoretical producer risk and theoretical user risk , combined with the agreed production party risk and agreed user risks , set the theoretical producer risk and agreed producer risks The maximum permissible error between the production e , Theoretical User Risk and agreed user risks The maximum allowable error between the users ;

[0124] calculate and Are both true? If so, use the reliability acceptance sampling scheme to design an optimization model to output the optimal reliability acceptance sampling scheme. If not, then in the decision variable design space, add 1 to the number of iterations of the reliability acceptance sampling scheme, return to step S34 to re-simulate and obtain the degradation test data for reliability acceptance sampling, and execute the subsequent steps in sequence until the reliability acceptance sampling scheme design optimization model is used to output the optimal reliability acceptance sampling scheme.

[0125] It can be understood that if the initial performance value and the performance degradation rate meet the requirements, the accepted IGBT device is qualified; if either the initial performance value or the performance degradation rate does not meet the requirements, the accepted IGBT device is unqualified.

[0126] In order to illustrate the effectiveness of the method proposed in the present invention, the above-mentioned technical scheme of the present invention is described in detail below through a specific embodiment. In the following description, for the purpose of illustration rather than limitation, specific details such as specific model parameters are proposed so as to thoroughly understand the embodiments of the present invention. However, it should be clear to those skilled in the art that the present invention can also be implemented in other embodiments without these specific details. In other cases, the detailed description of the well-known method is omitted to prevent unnecessary details from hindering the description of the present invention.

[0127] This embodiment provides a reliability acceptance sampling scheme design method for an IGBT device taking margin degradation into consideration. The margin degradation of the IGBT device refers to the margin degradation of the performance of the IGBT device.

[0128] Step A1. Establish agreed mutual risks, acceptance indicators and acceptance criteria;

[0129] Step A11. Establish agreed risks for both parties: =25% and =25%.

[0130] Step A12. Establish acceptance indicators: Production acceptance indicators =0.99, user acceptance index =0.51, where s =0.3242, which is the normalized stress level corresponding to 30℃, t = Hour,

[0131] Step A13. Establish acceptance criteria: and .

[0132] Step A2. Construct a theoretical formula for calculating the risk of both parties:

[0133] .

[0134] Step A3. According to the agreed bilateral risk, acceptance index, acceptance criteria and theoretical bilateral risk calculation formula, the reliability acceptance sampling scheme design optimization model of IGBT devices is constructed:

[0135] .

[0136] Step A4: construct a batch IGBT device degradation model for simulating and obtaining degradation test data.

[0137] Step A41. Construct a batch IGBT device degradation model as follows:

[0138] (26);

[0139] Among them, the degradation model parameters of this batch of IGBT devices are shown in Table 1, and the upper and lower limits of the stress level are 120°C and 0°C respectively.

[0140] Table 1 Designed batch IGBT device degradation model parameters

[0141]

[0142] Step A42: According to the batch IGBT device degradation model (12), 500 sets of degradation test data for reliability acceptance sampling are obtained through simulation.

[0143] Step A5. Construct a reliability acceptance sampling degradation model for IGBT devices, and obtain a reliability function of a batch of IGBT devices considering margin degradation based on the reliability acceptance sampling degradation model for IGBT devices; then estimate the parameters of the reliability acceptance sampling degradation model for IGBT devices based on degradation test data, substitute them into the reliability function of the batch of IGBT devices considering margin degradation, and then use the reliability function of the batch of IGBT devices considering margin degradation determined by the parameters to calculate the reliability of the batch of IGBT devices considering margin degradation.

[0144] Step A51. Construct a reliability acceptance sampling degradation model for IGBT devices:

[0145] .

[0146] Furthermore, the reliability function of a batch of IGBT devices with margin degradation is:

[0147] .

[0148] Step A52. Based on the degradation test data of step A4, use the uncertain weighted least squares algorithm to estimate 50 sets of unknown parameters in the reliability acceptance sampling degradation model. , k =1,2,…,50.

[0149] Step A53. The estimated 500 sets of unknown parameters Substitute them into the reliability function of the batch IGBT devices considering margin degradation respectively, and use the reliability function of the batch IGBT devices considering margin degradation determined by parameters to calculate the reliability of 50 groups of batch IGBT devices considering margin degradation. , that is, each Corresponding to one .

[0150] Step A6. Based on the reliability of the batch of IGBT devices with margin degradation, the theoretical bilateral risk is calculated using the calculation formula of the theoretical bilateral risk. Specifically, based on the reliability of 50 groups of batch IGBT devices with margin degradation, the theoretical bilateral risk is calculated. , and the theoretical double-party risk calculation formula, according to the uncertainty theory and uncertainty simulation operation rules, the theoretical production risk is calculated and theoretical user risk :

[0151] Step A7. Based on the theoretical bilateral risk of step A6 and the agreed bilateral risk of step A1, the reliability acceptance sampling scheme of the IGBT device of step A3 is used to design an optimization model, and the reliability acceptance sampling scheme of the IGBT device considering margin degradation is output. The maximum allowable error between the theoretical risk and the agreed risk is set. e =10%.

[0152] Finally, after calculation, we can get the risk of both parties meeting the agreement under the condition of meeting the error condition. and The reliability acceptance sampling plan for IGBT devices is = (8, 1000, 100), at this time, the manufacturer's risk and user's risk calculated by the reliability acceptance sampling plan of IGBT devices are (26.32%, 25%) respectively.

[0153] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any changes or substitutions that can be easily conceived by any technician familiar with the technical field within the technical scope disclosed by the present invention should be covered within the protection scope of the present invention.

Claims

1. A method for testing the life of an IGBT device based on multi-parameter distribution during manufacturing, characterized in that: The specific steps are as follows: Step S1, determining the model and application scenario of the IGBT device; Step S2, manufacturing an IGBT device according to the determined IGBT device model; Step S3, determining the electrical parameters and ambient temperature of the circuit where the IGBT device is located, and the variation range of each electrical parameter according to the application scenario; Step S4, sampling and obtaining IGBT devices of a determined model; Step S5: based on the maximum value and minimum value of each electrical parameter, obtain the mean value and variance of each electrical parameter; Step S6, based on the mean and variance of each electrical parameter, according to the Monte Carlo method, obtaining the absolute error of the parameter sampling result of the corresponding electrical parameter; Step S7, obtaining the selected sampling times based on the absolute error values ​​of all electrical parameters; Step S8, sampling each electrical parameter of the circuit where the IGBT device is located according to the selected sampling number to obtain a plurality of electrical parameter sampling values; Step S9, under the determined ambient temperature and external heat dissipation conditions of the IGBT device, and under the multiple electrical parameter sampling values ​​obtained in step S8, applying multiple different electrical stress values ​​to the sampled IGBT devices respectively; Use an infrared thermal imager to monitor the change of the junction temperature of the IGBT device in real time and obtain the junction temperature curve corresponding to different electrical stress values; Obtain a junction temperature data set of the IGBT device based on the junction temperature curve; obtain junction temperature fluctuation parameters of multiple groups of temperature cycles based on the junction temperature data set of the IGBT device; Step S10, obtaining the life cycle number of the IGBT device based on the temperature cycle junction temperature fluctuation parameter; Step S11: Based on the life cycle number obtained in step S10 N f Obtain the life cycle test value of the IGBT device; Step S12: determine whether the life cycle test value of the IGBT device meets the use requirements of the user. If so, obtain the IGBT device finished product. If not, adjust the manufacturing process.

2. The IGBT device life test method according to claim 1, characterized in that: The mean and variance of each electrical parameter in step S5 are expressed as: μ i -3 σ i = min i μ i +3 σ i = max i in, μ i Indicates i The mean value of the electrical parameters; σ i Indicates i The variance of the electrical parameters; min i Indicates i Minimum value of electrical parameters; max i Indicates i The maximum value of an electrical parameter.

3. The IGBT device life test method according to claim 2, characterized in that: The absolute error of the parameter sampling result of the electrical parameter in step S6 is expressed as: in, i Indicates i The absolute error of the parameter sampling results of the electrical parameters; i Indicates i Parameter sampling values ​​of various electrical parameters; i Indicates i The expected value of the parameter sampling value of an electrical parameter.

4. The IGBT device life test method according to claim 3, characterized in that: The specific steps of obtaining the selected sampling times based on the absolute error values ​​of all electrical parameters in step S7 are: Step S71, determining the range of parameter sampling results of each electrical parameter; Step S72: Substitute the absolute error of the parameter sampling results of each electrical parameter into the absolute error inequality , get the sampling times corresponding to each electrical parameter N i , i= 1,2, …I , I The total number of categories representing electrical parameters; Represents a normal distribution a Quantile Step S73: Obtain the selected sampling number based on the sampling number corresponding to each electrical parameter N , the expression is: N =max{ N 1, N 2,…, N I }; in, N 1 indicates the parameter sampling times of the first electrical parameter; N 2 represents the parameter sampling times of the second electrical parameter; N I Indicates I The parameter sampling times of various electrical parameters.

5. The IGBT device life test method according to claim 4, characterized in that: In step S9, a plurality of sets of junction temperature data sets are processed by using a rain flow counting method to obtain temperature cycle junction temperature fluctuation parameters.

6. The IGBT device life test method according to claim 5, characterized in that: In step S10, the life cycle number of the IGBT device is obtained based on the temperature cycle junction temperature fluctuation parameter. , the expression is: in, Indicates that based on j The life cycle number of the IGBT device with the junction temperature fluctuation parameter of the group temperature cycle; Δ T j Indicates j The temperature rise of the junction temperature cycle, T j,m Indicates j The intermediate temperature value of the junction temperature cycle, j =1,2,… n , n is the total number of groups of junction temperature cycle data; is the rated voltage of the IGBT device; is the voltage applied to the IGBT device; I rated is the rated current of the IGBT device, i applied is the current applied to the IGBT device; is the turn-on time of the IGBT device; A, , , and is the constant fitted by the power cycling experiment; is the Boltzmann constant; Ea is the excitation energy of the silicon chip.

7. The IGBT device life test method according to claim 6, characterized in that: In step S11, The number of life cycles obtained in step 10 The specific steps to obtain the life cycle test value of the IGBT device are: Step S111: Based on the life cycle number Obtain the cumulative lifetime damage ratio of IGBT devices LC , the expression is: in, Indicates j Number of junction temperature cycles; Step S112: obtaining a life cycle test value based on the accumulated life damage ratio of the IGBT device, The expression is: in, Nr It is the life cycle test value of the IGBT device.

8. The IGBT device life test method according to any one of claims 1 to 7, characterized in that: The reference average absolute error value is used to replace the absolute error of the parameter sampling result of the electrical parameter; the reference average absolute error value is expressed as: in, It is i The reference average absolute error value of the absolute error of the electrical parameter sampling results; μ i Indicates i The mean value of the electrical parameters.

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