Accelerated life test method for electronic component

Abstract

The invention discloses an accelerated life test method for an electronic component. Comprising the following steps: firstly, under a preset service life test working condition, collecting performance characterization data of different preset service life stages in a service life test process of the electronic component; then, according to the performance characterization data of different preset life stages, calculating to obtain difference curves corresponding to different performance characterization data, thereby obtaining all difference curves of the current electronic component; then, extracting preset target life stage features corresponding to each difference curve, and establishing a feature set; and finally, inputting the feature set into a life prediction model, and outputting a prediction result of a corresponding life test by the model. The time cost and the economic cost consumed by electronic component service life testing can be remarkably reduced, and the research and development efficiency of electronic components is improved.

Description

Technical Field

[0001] The invention belongs to a life test method in the field of electronic components, and in particular relates to an accelerated life test method and system for electronic components. Background Art

[0002] Life testing (also known as durability testing) plays an important role in the research and development of electronic components and is widely used in the research and development of new materials and new configurations. Life testing is the most costly of all R&D test projects for electronic components, including economic cost and time cost. Accelerating the speed of life testing can greatly reduce R&D costs and improve the R&D efficiency of electronic components. At present, electronic components mainly accelerate life testing by designing accelerated test conditions, such as electrical overstress accelerated test conditions, thermal overstress accelerated test conditions, and mechanical overstress accelerated test conditions. In addition to using accelerated test conditions, life prediction technology can also be used to shorten life testing time. In the field of lithium battery research and development, researchers have used life prediction technology to reduce the life test that originally took more than 500 days to complete to 16 days, which greatly reduced the R&D test cost of lithium battery fast charging strategy and improved R&D efficiency. However, at present, there is no accelerated life testing method based on life prediction in the field of electronic components. Summary of the invention

[0003] In order to solve the problem of high cost and low efficiency of life testing in the development process of electronic components, the present invention proposes an accelerated life testing method and system for electronic components. The present invention reduces the economic cost and time cost of life testing of electronic components and improves the development efficiency of electronic components.

[0004] The technical solution adopted by the present invention is:

[0005] 1. An accelerated life test system for electronic components

[0006] 1) Under the preset life test conditions, collect performance characterization data of electronic components at different preset life stages during the life test process;

[0007] 2) According to the performance characterization data of different preset life stages, the difference curves corresponding to different performance characterization data are calculated, thereby obtaining all the difference curves of the current electronic components;

[0008] 3) Extracting the preset target life stage characteristics corresponding to each difference curve and establishing a feature set;

[0009] 4) The feature set is input into the life prediction model, and the model outputs the corresponding prediction results of the life test.

[0010] The preset life stage includes a preset test time value and a preset life index value. The life index of an electronic component, such as the remaining capacitance (C retain ) and the equivalent series resistance (ESR), the on-resistance (R DS(ON) ) and threshold voltage (V thr ), the tail collector-emitter current (I CE ) and breakdown voltage (V bre ) etc. Performance characterization data of electronic components such as electrochemical impedance spectroscopy (EIS) data and charge / discharge voltage vs. time data of capacitors, R DS(ON) vs. Temperature (R DS(ON) / T) Data and V thr vs. Drain Current (V thr / I drain ) data, IGBT switching current vs. time data and V bre vs. Leakage Current (V bre / I leak ) data, etc.

[0011] The different preset life stages are generated through expert experience, filtering method, packaging method or embedding method.

[0012] The 2) is specifically:

[0013] After standardizing all performance characterization data of different preset life stages, standardized performance characterization data are obtained; for each type of standardized performance characterization data, the performance characterization data of the later preset life stage is subtracted from the performance characterization data of the previous preset life stage to obtain the corresponding difference curve; after traversing and processing all performance characterization data of all different preset life stages, all difference curves of the current electronic components are obtained, and the difference curves of different performance characterization data are also different. For example, the difference curve corresponding to the EIS data of the capacitor is the real impedance difference (ΔRe) curve and the imaginary impedance difference (ΔRe) curve, and the V thr / I drain The difference curve corresponding to the data is the threshold voltage difference (ΔV thr ) curve, IGBT's V bre / I leak The difference curve corresponding to the data is the breakdown voltage difference (ΔV bre )curve.

[0014] In the above 3), the preset target life stage characteristics corresponding to each difference curve include one or more of instantaneous characteristics, statistical characteristics and model parameter characteristics, that is, it can be a single feature or a combination of characteristics. Therefore, the feature set composed of the preset target life stage characteristics corresponding to different difference curves can include a combination of the same type of features and a combination of different types of features.

[0015] In the above 3), when the preset target life stage is a preset test time value, the preset target life stage characteristics are characteristics related to the life index value corresponding to the preset test time value; when the preset target life stage is a preset life index value, the preset target life stage characteristics are characteristics related to the test time value corresponding to the preset life index value.

[0016] In the above 4), when the preset target life stage is the preset test time value, the life prediction model used is the life index prediction model, and the life index prediction model outputs the life index value of the electronic component when it reaches the preset test time value, and obtains the prediction result of the life test; when the preset target life stage is the preset life index value, the life prediction model used is the test time prediction model, and the test time prediction model outputs the test time value required for the electronic component to reach the preset life index value, and obtains the prediction result of the life test.

[0017] The life prediction model is an integration of one or more machine learning models, empirical models or semi-empirical models.

[0018] 2. An accelerated life test system for electronic components

[0019] A performance characterization data acquisition unit, used to acquire performance characterization data at different preset life stages during the life test of electronic components;

[0020] A difference curve generating unit, used for generating difference curve data of corresponding performance characterization data according to different performance characterization data obtained at different preset life stages;

[0021] A feature extraction unit, used to extract preset target life stage features from the calculated difference curve data and establish a feature set;

[0022] The test result generating unit is used to input the feature set into the life prediction model, and the model outputs the predicted value of the corresponding life test result.

[0023] 3. A computer device

[0024] The device includes a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of the accelerated life test method for electronic components when executing the computer program.

[0025] 4. A computer-readable storage medium

[0026] The medium stores a computer program, and when the computer program is executed by a processor, the steps of the method for accelerating life testing of electronic components are implemented.

[0027] The beneficial effects of the present invention are:

[0028] By adopting the method of the present invention, it is not necessary to carry out a complete life test of electronic components. Only a small amount of preliminary tests need to be completed to accurately predict the life index value of the electronic components when the preset test time is reached or the test time required to reach the preset life index value. Compared with the use of a complete life test, it can greatly save time and economic costs and significantly improve the research and development efficiency of electronic components. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] Figure 1 It is the overall flow chart of the present invention.

[0030] Figure 2 Schematic diagram of a method for calculating a real impedance difference (ΔRe) curve in an embodiment of the present invention.

[0031] Figure 3 3 is a schematic diagram of the calculation results of the real impedance difference (ΔRe) curve in the embodiment of the present invention.

[0032] Figure 4 Schematic diagram of a method for calculating an imaginary impedance difference (ΔIm) curve in an embodiment of the present invention.

[0033] Figure 5 1 is a schematic diagram of the calculation results of the imaginary impedance difference (ΔIm) curve in an embodiment of the present invention.

[0034] Figure 6 is the capacitance of the capacitor at 3063h in the embodiment of the present invention (C retain )’s prediction results. DETAILED DESCRIPTION

[0035] The present invention is described in detail below with reference to the accompanying drawings and specific embodiments.

[0036] Example 1

[0037] The preset life stage of this embodiment is a preset test time value, which is generated through expert experience and includes the 0th hour and the 128th hour. Figure 1 As shown, the specific process of the accelerated life test method is as follows:

[0038] like Figure 1 As shown, the present invention comprises the following steps:

[0039] 1) Under the preset life test conditions, collect performance characterization data of electronic components at different preset test time values ​​during the life test. In this embodiment, capacitors are used as the research object, and the life index is the retained capacitance (C retain ), the performance characterization data uses electrochemical impedance spectroscopy (EIS) data. The preset life test condition is the electrical overstress acceleration test condition, the capacitor is charged and discharged with a 1Hz square wave, the amplitude is 10V, 12V or 14V, and the load is 100Ω.

[0040] 2) According to the performance characterization data collected at different preset test time values, calculate and obtain the difference curves of different performance characterization data. The performance characterization data collected at different preset test time values ​​are uniformly standardized, and the difference curve is obtained by subtracting the performance characterization data collected at the previous preset test time from the performance characterization data collected at the later preset test time value. The difference curves of different performance characterization data are also different. For example, the difference curve corresponding to the EIS data of the capacitor is the real impedance difference (ΔRe) curve and the imaginary impedance difference (ΔRe) curve. After traversing and processing all the performance characterization data collected at all different preset life indicator values, all the difference curves of the current fuel cell are obtained. In this embodiment, the capacitor is taken as the research object. Since the frequency points of all EIS data are set to the same during collection, there is no need for unified standardization. The ΔRe curve is calculated by subtracting the EIS real impedance data at the 128th hour from the EIS real impedance data at the 0th hour, such as Figure 2 The calculation results of ΔRe curve are shown in Figure 3 The ΔIm curve is calculated by subtracting the EIS imaginary impedance data at 0h from the EIS imaginary impedance data at 128h, as shown in Figure 4 The calculation results of the ΔIm curve are shown in Figure 5 shown.

[0041] 3) Extract the preset target life stage features corresponding to each difference curve and establish a feature set; specifically, the features extracted from the ΔRe curve and the ΔIm curve are all single statistical features, and are all corresponding to the life index C at the preset target test time 3063h retain The numerical value of the feature.

[0042] The method for extracting a single statistical feature on the ΔRe curve is:

[0043] (1) Subtract the real impedance values ​​corresponding to any two frequency values ​​on the ΔRe curve and take the absolute value as the candidate two-point features; (2) Traverse all combinations of two frequency values ​​on the ΔRe curve, and a total of (n 2-n) / 2 candidate two-point features, where n is the number of frequency values ​​on the difference curve and n=51; (3) Calculate each candidate two-point feature and the corresponding life index C of the capacitor at 3063h retain The Pearson correlation coefficient between the values ​​is calculated, and the feature with the largest absolute value of the correlation coefficient is selected as the best two-point feature of the ΔRe curve.

[0044] The method for extracting a single statistical feature on the ΔIm curve is:

[0045] (1) Subtract the imaginary impedance values ​​corresponding to any two frequency values ​​on the ΔIm curve and take the absolute value as the candidate two-point feature; (2) Traverse all combinations of two frequency values ​​on the ΔIm curve, and a total of (n 2 -n) / 2 candidate two-point features, where n is the number of frequency values ​​on the difference curve and n=51; (3) Calculate each candidate two-point feature and the corresponding life index C of the capacitor at 3063h retain The Pearson correlation coefficient between the values ​​is calculated, and the feature with the largest absolute value of the correlation coefficient is selected as the best two-point feature of the ΔIm curve.

[0046] 4) Input the feature set into the life prediction model, and the model outputs the corresponding prediction results of the life test. The specific life prediction model used is the life index prediction model, namely C retain The prediction model uses the random forest regression model. retain The input of the prediction model is the best two-point features of the ΔRe curve and the best two-point features of the ΔIm curve, and the output is the C retain The value of Figure 6 shown.

[0047] Example 2

[0048] The preset life stage in this embodiment is a preset life index value, which is generated through expert experience and includes C retain The value and C when attenuation is 0% retain The value when the attenuation is 1%. Figure 1 As shown, the specific process of the accelerated life test method is as follows:

[0049] 1) Under the preset life test conditions, collect performance characterization data when the electronic components reach different preset life index values ​​during the life test process. In this embodiment, capacitors are used as the research object, and the life index used is C retain , the performance characterization data uses EIS data.

[0050] 2) According to the performance characterization data collected when different preset life index values ​​are reached, the difference curves of different performance characterization data are calculated. The performance characterization data collected when different preset life index values ​​are reached are uniformly standardized, and the difference curve is obtained by subtracting the performance characterization data collected when the previous preset life index value is obtained from the performance characterization data collected when the later preset life index value is reached. The difference curves of different performance characterization data are also different. For example, the difference curve corresponding to the EIS data of the capacitor is the real impedance difference (ΔRe) curve and the imaginary impedance difference (ΔRe) curve. After traversing and processing all the performance characterization data collected for all different preset life index values, all the difference curves of the current fuel cell are obtained. In this embodiment, the capacitor is taken as the research object. Since the frequency points of all EIS data are set to the same during collection, there is no need for unified standardization. The ΔRe curve is obtained using C retain EIS real impedance data at 1% attenuation minus C retain The ΔIm curve is calculated using the EIS real impedance data when the attenuation is 0%. retain EIS imaginary impedance data at 1% attenuation minus C retain The EIS imaginary impedance data at 0% attenuation was calculated.

[0051] 3) Extract the preset target life stage features corresponding to each difference curve and establish a feature set; the features extracted from the ΔRe curve and the ΔIm curve are all single statistical features, and are all consistent with the preset target life index value (C retain The value at 10% attenuation) corresponds to the characteristics related to the test time value.

[0052] The method for extracting a single statistical feature on the ΔRe curve is:

[0053] (1) Subtract the real impedance values ​​corresponding to any two frequency values ​​on the ΔRe curve and take the absolute value as the candidate two-point features; (2) Traverse all combinations of two frequency values ​​on the ΔRe curve, and a total of (n 2 -n) / 2 candidate two-point features, where n is the number of frequency values ​​on the difference curve and n=51; (3) Calculate the correlation between each candidate two-point feature and capacitor C retain The Pearson correlation coefficient between the corresponding test time values ​​when the attenuation is 10%, and the feature with the largest absolute value of the correlation coefficient is selected as the best two-point feature of the ΔRe curve.

[0054] The method for extracting a single statistical feature on the ΔIm curve is:

[0055] (1) Subtract the imaginary impedance values ​​corresponding to any two frequency values ​​on the ΔIm curve and take the absolute value as the candidate two-point feature; (2) Traverse all combinations of two frequency values ​​on the ΔIm curve, and a total of (n2 -n) / 2 candidate two-point features, where n is the number of frequency values ​​on the difference curve and n=51; (3) Calculate the correlation between each candidate two-point feature and capacitor C retain The Pearson correlation coefficient between the corresponding test time values ​​when the attenuation is 10%, and the feature with the largest absolute value of the correlation coefficient is selected as the best two-point feature of the ΔIm curve.

[0056] 4) Input the feature set into the life prediction model, and the model outputs the corresponding life test prediction results. The specific life prediction model used is the test time prediction model, namely C retain The test time prediction model is attenuated by 10%, and the random forest regression model is used. retain The input of the test time prediction model with 10% attenuation is the best two-point feature of the ΔRe curve and the best two-point feature of the ΔIm curve, and the output of the model is C retai The test time value corresponding to the attenuation reaching 10%.

[0057] Finally, it should be noted that the above embodiments and explanations are only used to illustrate the technical solution of the present invention rather than to limit it. Those skilled in the art should understand that the technical solution of the present invention can be modified or replaced by equivalents without departing from the spirit and scope disclosed in the technical solution of the present invention, which should be included in the scope of protection of the claims of the present invention.

Claims

1. An accelerated life test method for electronic components, characterized in that: The following steps are involved: 1) Under the preset life test conditions, collect performance characterization data of electronic components at different preset life stages during the life test process; 2) According to the performance characterization data of different preset life stages, the difference curves corresponding to different performance characterization data are calculated, thereby obtaining all the difference curves of the current electronic components; 3) Extracting the preset target life stage characteristics corresponding to each difference curve and establishing a feature set; 4) The feature set is input into the life prediction model, and the model outputs the corresponding prediction results of the life test.

2. The accelerated life test method of electronic components according to claim 1, characterized in that: The preset life stage includes a preset test time value and a preset life index value.

3. The accelerated life test method of electronic components according to claim 1, characterized in that: The different preset life stages are generated through expert experience, filtering method, packaging method or embedding method.

4. The accelerated life test method of electronic components according to claim 1, characterized in that: The 2) is specifically: After standardizing all performance characterization data of different preset life stages respectively, the standardized performance characterization data are obtained; for each type of standardized performance characterization data, the performance characterization data of the later preset life stage is subtracted from the performance characterization data of the earlier preset life stage to obtain the corresponding difference curve; after traversing and processing all performance characterization data of all different preset life stages, all difference curves of the current electronic components are obtained.

5. The accelerated life test method of electronic components according to claim 1, characterized in that: In the above 3), the preset target life stage characteristics corresponding to each difference curve include one or more of instantaneous characteristics, statistical characteristics and model parameter characteristics.

6. The accelerated life test method of electronic components according to claim 1, characterized in that: In the above 3), when the preset target life stage is a preset test time value, the preset target life stage feature is a feature related to the life index value corresponding to the preset test time value; When the preset target life stage is a preset life index value, the preset target life stage characteristic is a characteristic related to the test time value corresponding to the preset life index value.

7. The accelerated life test method for electronic components according to claim 1, characterized in that: In the above 4), when the preset target life stage is the preset test time value, the life prediction model used is the life index prediction model, and the life index prediction model outputs the life index value of the electronic component when it reaches the preset test time value, and obtains the prediction result of the life test; when the preset target life stage is the preset life index value, the life prediction model used is the test time prediction model, and the test time prediction model outputs the test time value required for the electronic component to reach the preset life index value, and obtains the prediction result of the life test.

8. An accelerated life test system for electronic components, characterized in that: include: A performance characterization data acquisition unit, used to acquire performance characterization data at different preset life stages during the life test of electronic components; A difference curve generating unit, used for generating difference curve data of corresponding performance characterization data according to different performance characterization data obtained at different preset life stages; A feature extraction unit, used to extract preset target life stage features from the calculated difference curve data and establish a feature set; The test result generating unit is used to input the feature set into the life prediction model, and the model outputs the predicted value of the corresponding life test result.

9. A computer device, characterized in that: The device comprises a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of the accelerated life test method for electronic components as claimed in any one of claims 1 to 7 when executing the computer program.

10. A computer-readable storage medium, characterized in that: The medium stores a computer program, and when the computer program is executed by a processor, the steps of the accelerated life test method for electronic components according to any one of claims 1 to 7 are implemented.

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