SiC power device on-orbit single event hard error risk probability estimation method

Abstract

The application discloses a SiC power device on-orbit single event hard error risk probability estimation method, relates to the anti-radiation guarantee technical field of power device space application, and is based on ground radiation test and Weibull data statistical law, combines simulation software simulation of space radiation environment, can low-cost estimate the probability of single event hard error of SiC device space on-orbit application, simultaneously realizes lower difficulty, reduces the cost of on-orbit task of a spacecraft, enhances reliability during on-orbit, guarantees safety of on-orbit flight, belongs to the anti-radiation guarantee technical field of power device space application, has very high economic value, and has important significance for promoting development of a new type of space power supply.

Description

Technical Field

[0001] This invention relates to the field of radiation protection technology for power devices in space applications, and in particular to a method for predicting the probability of on-orbit single-event hard error risk for SiC power devices. Background Technology

[0002] Silicon carbide (SiC), as a third-generation semiconductor material, possesses superior electrical and thermal properties compared to silicon (Si), including higher thermal conductivity, higher breakdown field strength, higher electron saturation velocity, and stronger radiation resistance. These excellent properties make it a promising candidate for high-voltage power device materials in aerospace systems. Therefore, in the aerospace field, power SiC devices may gradually replace silicon-based power devices due to their superior performance, and have the potential for future applications in power electronic systems in satellites, spacecraft, detectors, and space.

[0003] However, the space radiation environment, composed of Earth's radiation belts, galactic cosmic rays, and solar cosmic rays, contains a large number of high-energy particles. These high-energy space particles can cause radiation effects on devices within space equipment. These effects are mainly categorized into total dose effects, displacement effects, and single-event effects. Single-event effects can range from minor issues like leakage in circuit systems leading to reduced reliability, to severe issues like hard errors causing device malfunctions, seriously threatening the normal operation of space equipment. Power SiC devices have strong resistance to total dose, but they are sensitive to single-event effects when operating under high voltage, making them prone to single-event burn-out (SEB) and single-event gate breakdown (SEGR). Therefore, single-event effect testing is necessary. However, ground-based simulations can only obtain single-event burn-out and single-event gate breakdown phenomena, failing to accurately reflect the on-orbit radiation environment and single-event risks. Existing technologies also present the problem of accurately estimating the probability of single-event hard errors in power SiC devices during space applications. Summary of the Invention

[0004] The technical problem to be solved by this invention is to overcome the shortcomings of the prior art and provide a method for predicting the probability of single-event hard errors in SiC power devices in orbit. Based on ground-based heavy ion irradiation experiments, this invention combines existing space radiation environment simulation software and statistical laws of device failure cross-section data to accurately predict the number of single-event hard errors that occur in power SiC devices in orbit per unit time, thus solving the reliability assessment problem of single-event effects caused by heavy ion irradiation during the operation of power SiC devices in orbit in the prior art.

[0005] To solve the above-mentioned technical problems, the present invention adopts the following technical solution:

[0006] A method for predicting the probability of on-orbit single-event hard error risk in SiC power devices, proposed according to the present invention, includes:

[0007] Step 1: Perform single-event hard error irradiation testing on SiC power devices, and monitor the relationship between drain or gate current and flux in real time during irradiation to obtain... Curve data showing the relationship between drain or gate current and flux.

[0008] Step 2: Filter out the single-event failure flux data of SiC power devices when single-event burn-out and single-event gate penetration occur from the curve data.

[0009] Step 3: Perform Weibull statistical analysis on the single-event failure fluence data to obtain the Weibull statistical distribution map of the single-event failure fluence of SiC power devices. Then, perform linear fitting based on the Weibull statistical distribution map of the single-event failure fluence, and obtain the Weibull shape parameters using the fitting slope and intercept. and scale parameters ;

[0010] Step 4: Based on Weibull shape parameters and scale parameters Obtain average failure flux data under a single linear energy transfer (LET). ;

[0011] Step 5: Analyze the average failure injection volume data for a single LET. Taking the reciprocal yields the failure section;

[0012] Repeated single-event hard fault (LET) irradiation tests were performed on other SiC power devices of the same type until the failure cross sections under different LET values ​​were obtained. , , The number of LETs; one single-event hard error irradiation test corresponds to one LET. For the first Failure section under LET And for the failed section By fitting the data, the failure section function is obtained. ;

[0013] Step 6: Analyze the failure section function. Multiply Processing yields failure cross-sectional functions under different incident angles. , The angle between the particle incident path and the particle's perpendicular incident path to the SiC power device is determined by... By integrating the failure section formula over the entire space, the average failure section is obtained. ;

[0014] Step 7: Using space radiation environment software, input the spacecraft's orbital data, add an environment model including solar particles, geomagnetic trapping particles, and galactic cosmic ray particles, and obtain differential flux energy spectrum data of the space radiation environment inside the spacecraft cabin. ;

[0015] Step 8: Based on the average failure section Total flux data of space radiation environment Calculate the on-orbit single-event hard error rate .

[0016] As a further optimization of the on-orbit single-event hard error risk probability prediction method for SiC power devices described in this invention, the test method used in step one for the single-event hard error irradiation test is based on the single-event burn-out and single-event gate penetration test described in the 1080 method of the US military standard MIL-STD-750.

[0017] As a further optimization of the on-orbit single-event hard error risk probability prediction method for SiC power devices described in this invention, step two is as follows:

[0018] Step 201: First set the reference current. From each curve data in step one, obtain all currents greater than the reference current. The data points are then selected; the slope of the selected data points is calculated, and the maximum slope is chosen from the slopes. This yields the injection volume data corresponding to the maximum slope.

[0019] Step 202, repeat step 201 to obtain Single-event failure fluence data of drain or gate current variation curves , , For the first Single-event failure flux data of drain or gate current variation curves. .

[0020] As a further optimization of the on-orbit single-event hard error risk probability prediction method for SiC power devices described in this invention, the shielding layer thickness of the spacecraft is 3 mm of aluminum.

[0021] Compared with the prior art, the present invention, employing the above technical solution, has the following technical effects:

[0022] This invention combines ground-based irradiation experiments and data statistics to propose a method for predicting the probability of single-event hard errors occurring in power SiC devices during orbit. This method addresses the reliability assessment challenge of power SiC devices experiencing single-event effects due to heavy-ion irradiation during orbital operation, and further predicts the performance degradation and device lifetime of SiC devices in space applications. Attached Figure Description

[0023] Figure 1 This is a flowchart illustrating the implementation of the method of this invention;

[0024] Figure 2 These are typical device single-event hard error irradiation test results;

[0025] Figure 3 This is a Weibull statistical distribution diagram of the single-event failure fluence of the device;

[0026] Figure 4 These are average failure cross-sectional diagrams of devices under different LET conditions;

[0027] Figure 5 It is the differential flux (LET) spectrum of the space radiation environment in the GEO orbit. Detailed Implementation

[0028] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be described in detail below with reference to the accompanying drawings and specific embodiments.

[0029] This invention provides a method for predicting the probability of single-event hard errors (SEE) occurring in SiC power devices in orbit, belonging to the field of radiation protection technology for power device applications in space. First, the failure flux of the device is obtained through ground-based heavy ion irradiation experiments. Then, statistical analysis of Weibull data and the influence of angle effects are used to obtain statistical results of the SEE failure cross-section. Combined with the particle flux spectrum inside the spacecraft in a specific orbit, the risk probability of SEE events occurring in SiC power devices in orbit is predicted. This will significantly reduce the cost of space missions and improve the reliability of spacecraft operation in orbit.

[0030] Specifically, this invention involves: obtaining the total flux spectrum of particles inside a spacecraft cabin on a typical orbit based on existing space radiation environment simulation databases; acquiring device failure cross-section data through Weibull statistical analysis and fitting of ground-based heavy ion irradiation test data; and, due to the three-dimensional spatial randomness and equal probability of particle motion in each direction, adding the influence of the particle incident angle to the failure cross-section to ensure the omnidirectionality of space particles. Finally, integrating the above data yields the probability of single-event hard errors occurring in SiC devices during on-orbit space applications. This significantly reduces the cost of space missions, improves the reliability of spacecraft during on-orbit operation, and is of great significance for promoting the development of new aerospace power supplies.

[0031] A method for predicting the probability of on-orbit single-event hard error in SiC power devices includes:

[0032] Step 1: Perform single-event hard error irradiation testing on SiC power devices, and monitor the relationship between drain or gate current and flux in real time during irradiation to obtain... Curve data showing the relationship between drain or gate current and flux.

[0033] Step 2: Filter out the flux data when single-event burn-out and single-event gate penetration occur in SiC power devices from the acquired curve data.

[0034] Step 201: First set the reference current. From each curve data in step one, obtain all currents greater than the reference current. The data points are then selected; the slope of the selected data points is calculated, and the maximum slope is chosen from the slopes. This yields the injection volume data corresponding to the maximum slope.

[0035] Step 202, repeat step 201 to obtain Single-event failure flux data of drain or gate current variation curves, i.e. , , For the first Single-event failure flux data of drain or gate current variation curves. ;

[0036] Step 3: Analyze the single-event failure fluence data. Weibull statistical analysis was performed to obtain the Weibull statistical distribution map of single-event failure fluence in SiC power devices. Linear fitting was then performed based on the Weibull statistical distribution map of single-event failure fluence, and the Weibull shape parameters were obtained using the fitting slope and intercept. and scale parameters ;

[0037] The Weibull data statistical formula is:

[0038]

[0039] in, The median of the invalid data (and) related), For injection volume data, For Weibull shape parameters, For Weibull scale parameters;

[0040] Step 4: Based on Weibull shape parameters and scale parameters According to the formula The average failure flux data under a single linear energy transfer (LET) was obtained. ,in, It is a gamma function;

[0041] Step 5: Calculate the average failure injection volume data for a single LET. The failure section is obtained by taking the reciprocal. Single-event hard error (LET) irradiation tests were repeated on other SiC power devices of the same type until the failure cross-sections under different LET values ​​were obtained. , , The number of LETs; one single-event hard error irradiation test corresponds to one LET. For the first Failure section under LET The failure section function is obtained by fitting the failure section. ;

[0042] Step 6: Analyze the failure section function. Multiply Processing yields failure cross-sectional functions under different incident angles. , Let the angle between the particle incident path and the particle's perpendicular incident path to the SiC power device be defined by the formula. By performing a full-space integration on the failure section formula, the average failure section is finally obtained. ;

[0043] Step 7: Using space radiation environment software, input the spacecraft's orbital data, add an environment model including solar particles, geomagnetic trapping particles, and galactic cosmic ray particles. The shielding layer thickness is 3 mm of aluminum. Finally, obtain the differential flux energy spectrum data of the space radiation environment inside the spacecraft cabin. ;

[0044] Step 8: Based on the average failure section Total flux data of space radiation environment Through formula Calculate the on-orbit single-event hard error rate .

[0045] This specification addresses the on-orbit application requirements of space SiC devices by providing a method for estimating the probability of single-event hard errors (SEE) in power SiC devices. This method can be used to solve the reliability assessment problem of single-event effects caused by heavy ion irradiation during on-orbit operation of power SiC devices in the prior art, and belongs to the field of radiation protection assurance for power devices.

[0046] like Figure 1As shown in the flowchart, this invention provides a method for predicting the probability of a single-event hard error occurring in an on-orbit SiC device, comprising the following steps:

[0047] Step 1: Select commercial SiC power devices and conduct single-event irradiation tests on them. The test method is based on the single-event burn-out and single-event gate breakdown tests described in the US military standard MIL-STD-750, method 1080. During irradiation, monitor the relationship between drain or gate current and flux in real time and obtain the drain or gate current versus flux curve.

[0048] In this embodiment of the invention, the source-drain voltage V DS A SiC MOSFET at 900 V was subjected to krypton (Kr) ion treatment: LET = 33 MeV·cm⁻¹. 2 Irradiation tests were conducted at a concentration of / mg, with the beam perpendicular to the device surface, and a total fluence of 10 was obtained. 10 cm -2 The device gate current variation data, such as Figure 2 As shown.

[0049] Step 2: Process the acquired curves to obtain injection volume data. As shown in Table 1.

[0050] The specific process is as follows:

[0051] against Figure 2 Gate current versus flux variation curve, setting a reference current value. 100 nA (data points with gate current exceeding 100 nA exhibit single-event hard errors), select a gate current exceeding the reference current value. Data points (Assuming there are i points), calculate the slope at each point. Select the one with the largest slope This yields the injection volume data corresponding to the data point with the maximum slope. Figure 2 There are 9 curves in the data, which ultimately yields the injection volume data. The injection volume data is shown in Table 1.

[0052] Table 1. Rank F(f) statistics of injection volume data and failure data.

[0053]

[0054] Step 3, as follows Figure 3 As shown, we have the injection volume data Perform statistical analysis on the Weibull data to obtain the shape parameters from the Weibull parameters. and scale parameters ;

[0055] The specific process is as follows:

[0056] First, calculate the rank of the failed data. The rank in the failure data is the median, and the formula is as follows. The resulting data is shown in Table 1.

[0057]

[0058] in, Here, k represents the injection volume, and k is the fault number. In the sample experiment, the fault number is determined according to the injection volume data. Sort the samples by size, where n is the total number of samples.

[0059] Next, we transform the Weibull formula, which becomes:

[0060]

[0061] in: For Weibull shape parameters, For the Weibull scale parameter.

[0062] Formula transformation about Linear functions of variables:

[0063]

[0064] in: , .

[0065] according to Figure 3 The slope and intercept of the fitted linear function, combined with the transformed Weibull formula, are related to... The variable is a linear function; its shape parameters are calculated. and scale parameters The specific value.

[0066] Step 4: Based on shape parameters and scale parameters We obtain LET = 33 MeV·cm 2 Average failure injection volume data at / mg .

[0067] The specific process is as follows:

[0068] The shape parameters are obtained from step three. and scale parameters Calculate the average failure injection volume The formula for average failure injection volume is:

[0069]

[0070] Step 5: Based on a single LET The failure section was calculated. Repeat the experiment and the above steps to obtain failure section data under at least three different LET conditions. The failure section data was then fitted to obtain the fitted failure section. , Figure 4 This is the fitted average failure cross-section diagram.

[0071] The specific process is as follows:

[0072] Based on average failure volume data Obtain the failure section The formula is:

[0073]

[0074] The failure cross section under different LET conditions was obtained by repeating the test process. The data was fitted, and the resulting formula is shown below:

[0075]

[0076] in, The linear energy transfer value at the initial stage of a single-event hard error. .

[0077] Step Six: Correlation of Failure Section Angle. Because particle motion directions exhibit spatial randomness and equal probability in all directions, to ensure the omnidirectional nature of spatial particles, the influence of the particle incident angle must be added to the failure section. Adding the influence of the incident angle, we generally believe that single-event hard errors (SEEs) are more severe due to perpendicular incidence. As the incident angle increases, the probability of an SEE gradually decreases. There is an inverse relationship between the directional cross-section and the incident angle, following the cosine law. This represents the angle of incidence. Therefore, the failure section... The formula is:

[0078]

[0079] Then according to the formula By performing a full-space integration on the failure section formula, the average failure section is finally obtained. :

[0080]

[0081] Step 7: To simulate the space radiation environment for on-orbit applications of SiC devices, we used the ESA's SPENVIS radiation environment software tool, calling its space radiation environment particle spectrum calculation module, inputting typical space orbit data, adding particle environments such as solar particles, trapped protons, and galactic cosmic rays, and setting the spacecraft's shielding layer thickness to 3 mm of aluminum. We obtained the function of the differential flux energy spectrum of the space radiation environment in the GEO orbit (geosynchronous orbit) after the spacecraft was shielded, as a function of LET. ,like Figure 5 The LET spectrum represents the differential flux of the typical orbital GEO space radiation environment.

[0082] Step 8: Combine with average failure section Total flux data of space radiation environment The calculated probability of a single-event hard error occurring in orbit is 0.01658 bits / day. The specific formula is:

[0083]

[0084] in: The LET spectrum represents the differential flux of the space radiation environment behind the shielding layer. The average failure cross section, This represents the number of single-event hard errors that occur in a day, measured in bits per day.

[0085] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for predicting the probability of on-orbit single-event hard error in SiC power devices, characterized in that, include: Step 1: Perform single-event hard error irradiation testing on SiC power devices, and monitor the relationship between drain or gate current and flux in real time during irradiation to obtain... Curve data showing the relationship between drain or gate current and flux. Step 2: Filter out the single-event failure flux data of SiC power devices when single-event burn-out and single-event gate breakdown occur from the curve data. Step 3: Perform Weibull statistical analysis on the single-event failure fluence data to obtain the Weibull statistical distribution map of the single-event failure fluence of SiC power devices. Then, perform linear fitting based on the Weibull statistical distribution map of the single-event failure fluence, and obtain the Weibull shape parameters using the fitting slope and intercept. and scale parameters ; Step 4: Based on Weibull shape parameters and scale parameters Obtain average failure flux data under a single linear energy transfer (LET). ; Step 5: Analyze the average failure injection volume data for a single LET. Taking the reciprocal yields the failure section; Repeated single-event hard fault (LET) irradiation tests were performed on other SiC power devices of the same type until the failure cross sections under different LET values ​​were obtained. , , The number of LETs; one single-event hard error irradiation test corresponds to one LET. For the first Failure section under LET And for the failed section By fitting the data, the failure section function is obtained. ; Step 6: Analyze the failure section function. Multiply Processing yields failure cross-sectional functions under different incident angles. , The angle between the particle incident path and the particle's perpendicular incident path to the SiC power device is determined by... By integrating the failure section formula over the entire space, the average failure section is obtained. ; Step 7: Using space radiation environment software, input the spacecraft's orbital data, add an environment model including solar particles, geomagnetic trapping particles, and galactic cosmic ray particles, and obtain differential flux energy spectrum data of the space radiation environment inside the spacecraft cabin. ; Step 8: Based on the average failure section and Calculate the on-orbit single-event hard error rate .

2. The method for predicting the probability of on-orbit single-event hard error in SiC power devices according to claim 1, characterized in that, The single-event hard error irradiation test in step one uses a test method based on the single-event burn-out and single-event grid penetration test described in the 1080 method of the US military standard MIL-STD-750.

3. The method for predicting the probability of on-orbit single-event hard error in SiC power devices according to claim 1, characterized in that, Step two is as follows: Step 201: First set the reference current. From each curve data in step one, obtain all currents greater than the reference current. The data points are then selected; the slope of the selected data points is calculated, and the maximum slope is chosen from the slopes. This yields the injection volume data corresponding to the maximum slope. Step 202, repeat step 201 to obtain Single-event failure fluence data of drain or gate current variation curves , , For the first Single-event failure flux data of drain or gate current variation curves. .

4. The method for predicting the probability of on-orbit single-event hard error in SiC power devices according to claim 1, characterized in that, The spacecraft's shielding layer is 3 millimeters thick.

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