Silicon carbide MOSFET transient temperature rise measurement method based on surge current

Abstract

The invention discloses a silicon carbide MOSFET transient temperature rise measurement method based on a surge current, and the method comprises the steps: building an infrared thermal imaging surge temperature analysis platform, fixing a silicon carbide MOSFET, then applying a surge current, and synchronously collecting the surface temperature distribution data of a chip; calling a multi-physical-field collaborative thermal analysis model, inputting device materials, structures and initial temperature parameters, analyzing heat conduction, thermal radiation and Joule heat generation processes, and outputting initial heat flow data; adopting a differential iteration transient junction temperature calculation method, and combining thermal capacity and thermal resistance network parameters to obtain transient junction temperature calculation values of different time nodes; according to the method, an extended dynamic mode decomposition heat flow calculation method and decomposition temperature data are used for extracting dynamic characteristics, heat flow data are corrected in combination with the chip thickness and the thermal diffusion coefficient, the corrected data are substituted into iterative calculation, a final transient temperature rise measurement result is obtained, and the measurement precision and adaptability are improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of silicon carbide MOSFET transient temperature rise measurement, and particularly relates to a silicon carbide MOSFET transient temperature rise measurement method based on inrush current. BACKGROUND

[0002] In power electronics technology, silicon carbide MOSFETs are widely used in new energy vehicles, photovoltaic inverters, energy storage systems and other scenarios due to their advantages of high frequency, high voltage resistance and low loss. In such applications, silicon carbide MOSFETs often face inrush current impact, which can cause transient temperature rise of the device in a short time. If the temperature rise exceeds the safe range, it will lead to degradation of device performance, shortening of service life, and even direct damage, seriously affecting the reliability and stability of the entire power electronic system. Therefore, accurately measuring the transient temperature rise of silicon carbide MOSFET under inrush current is a key step in evaluating the anti-inrush capability of the device and optimizing the thermal design of the system, and the development and improvement of related measurement methods have important engineering practical significance.

[0003] The existing measurement technology for silicon carbide MOSFET transient temperature rise has two obvious shortcomings. On the one hand, the measurement accuracy is insufficient, most technologies rely on a single temperature measurement method, do not fully consider the multi-physical field coupling effects of internal heat conduction, heat radiation and Joule heat generation under the action of inrush current, and do not effectively dynamically correct the collected temperature data, resulting in a large deviation between the measurement results and the actual transient temperature rise of the device, making it difficult to meet the high-precision application requirements. On the other hand, the model applicability is limited, the thermal analysis model or junction temperature calculation algorithm used by some measurement methods does not combine the material properties (such as thermal conductivity changing with temperature) and structural characteristics (such as chip thickness and active area) of silicon carbide MOSFET for targeted design, and cannot accurately adapt to the complex change law of the device transient temperature rise under inrush current, resulting in poor measurement stability and accuracy under different working conditions. SUMMARY

[0004] In order to overcome the shortcomings and deficiencies of the existing technology, the present application provides a silicon carbide MOSFET transient temperature rise measurement method based on inrush current.

[0005] The technical scheme adopted by the present application is based on a silicon carbide MOSFET transient temperature rise measurement method under inrush current, comprising the following steps: S1, an infrared thermal imaging inrush temperature analysis platform is built, which integrates an inrush current generation module, a silicon carbide MOSFET fixing clamp, an infrared thermal image acquisition module and a data storage module, the silicon carbide MOSFET is placed in the fixing clamp, and the infrared thermal image acquisition module lens is aligned with the active area of the silicon carbide MOSFET chip; S2, the inrush current generation module applies a preset amplitude and duration inrush current to the silicon carbide MOSFET, and simultaneously starts the infrared thermal image acquisition module, collects the silicon carbide MOSFET chip surface temperature distribution data at a set sampling frequency, and transmits it to the data storage module; S3, a multi-physical field collaborative thermal analysis model is called, the material parameters, structure size parameters and initial temperature data collected in S2 of the silicon carbide MOSFET are input, the thermal conduction, thermal radiation and joule heat generation process of the silicon carbide MOSFET under the action of inrush current are analyzed by multi-physical field coupling, and preliminary heat flow distribution data is output; S4, a differential iteration transient junction temperature calculation method is used, the preliminary heat flow distribution data output by S3 is used as initial input, based on the heat capacity parameters and thermal resistance network parameters of the silicon carbide MOSFET, the silicon carbide MOSFET transient junction temperature calculation value at different time nodes is obtained by multi-step differential iteration calculation; S5, an extended dynamic mode decomposition heat flow calculation method is used to dynamically decompose the multiple sets of surface temperature distribution data collected in S2, extract the temperature field dynamic characteristic parameters, combine the chip thickness parameters and thermal diffusivity parameters of the silicon carbide MOSFET, and correct the preliminary heat flow distribution data output by S3 to obtain the corrected heat flow distribution data; S6, the corrected heat flow distribution data of S5 is substituted into the differential iteration transient junction temperature calculation method of S4, the iteration calculation process is updated, and the final silicon carbide MOSFET transient junction temperature measurement result is obtained.

[0006] Further, the expression of the multi-physical field collaborative thermal analysis model is: Wherein, k eff (T) is the effective thermal conductivity of the silicon carbide MOSFET material, which changes with temperature T; T(x, y, z, t) is the temperature at the coordinates (x, y, z) inside the silicon carbide MOSFET at time t; J(x, y, z, t) is the current density at the coordinates (x, y, z) at time t; σ is the conductivity of the silicon carbide material; ρ is the density of the silicon carbide MOSFET material; c p (T) is the specific heat capacity of the material at constant pressure, which changes with temperature T; ε is the emissivity of the silicon carbide chip surface; σ SB is the Stefan-Boltzmann constant; T env is the ambient temperature.

[0007] Further, the expression of the differential iterative transient junction temperature calculation method is: Wherein, T j (t n+1 ) is the junction temperature of the silicon carbide MOSFET at the n+1 moment; T j (t n ) is the junction temperature at the n moment; Δt is the iteration time step; C th,j is the junction thermal capacity; m is the number of regions with thermal coupling to the junction; T i (t n ) is the temperature of the i-th coupled region at the n moment; R th,ij is the thermal resistance between the junction and the i-th coupled region; P diss (t n ) is the dissipation power of the junction at the n moment; τ rad,j is the junction thermal radiation time constant; T env is the ambient temperature.

[0008] Further, the expression of the extended dynamic mode decomposition heat flow calculation method is: Wherein, q(x,y,t) is the heat flux density at the coordinate (x,y) on the surface of the silicon carbide MOSFET chip at the t moment; k s is the thermal conductivity of the surface layer of the chip; N is the number of modes of dynamic mode decomposition; λ k is the amplitude coefficient of the k-th mode; φ k (x,y) is the spatial distribution function of the k-th mode; ω k is the complex frequency of the k-th mode; ρ is the density of the chip material; c p is the specific heat capacity of the material at constant pressure; α k is the thermal diffusion correction coefficient of the k-th mode; q loss (x,y,t) is the heat loss term at the coordinate (x,y) at the t moment.

[0009] Further, the temperature collection accuracy correction model of the infrared thermal imaging surge temperature analysis platform is: Wherein, T corr (x,y,t) is the temperature at the coordinate (x,y) at the t moment after correction; T raw (x,y,t) is the original collected temperature; δ dist (d) is the distance correction coefficient, related to the distance d from the infrared lens to the chip; δ angle (θ) is the angle correction coefficient, related to the collection angle θ; T env is the ambient temperature; δ res (p) is the resolution correction coefficient, related to the spatial resolution corresponding to the pixel point p. Gradients of the original temperature field.

[0010] Further, the verification model of the silicon carbide MOSFET transient temperature rise measurement result is: ΔT val (t) = |T j (t) - T IR,j (t) | - ξ (ΔI surge (t) + Δf sample (t) ) - ζ (Δk mat (t) + ΔR th (t) ), wherein, ΔT val (t) is the transient temperature rise verification deviation at time t; T j (t) is the calculated junction temperature at time t; T IR,j (t) is the junction temperature at time t calculated by infrared indirect calculation; ξ is the measurement system error coefficient; ΔI surge (t) is the surge current fluctuation at time t; Δf sample (t) is the sampling frequency fluctuation at time t; ζ is the material and thermal parameter error coefficient; Δk mat (t) is the material thermal conductivity fluctuation at time t; ΔR th (t) is the thermal resistance fluctuation at time t.

[0011] Further, the S3 comprises the following sub-steps: S31, calling the material parameters of the silicon carbide MOSFET from the data storage module, the parameters including the thermal conductivity of the silicon carbide substrate, the thermal conductivity of the oxide layer, the thermal conductivity of the metal electrode, and the density and specific heat capacity of each material, and calling the structure size parameters of the silicon carbide MOSFET, including the total thickness of the chip, the thickness of the substrate, the thickness of the oxide layer, the width and length of the source and drain electrodes, and the active area; S32, converting the initial temperature data collected in S2 into a temperature matrix corresponding to the structure coordinates of the silicon carbide MOSFET, determining the calculation domain of the multi-physical field cooperative thermal analysis model, and setting the calculation domain boundary to 1mm outside the chip and the boundary temperature to the ambient temperature; S33, inputting the material parameters, structure size parameters and temperature matrix into the multi-physical field cooperative thermal analysis model, setting the coupling solving order of the heat conduction equation, the heat radiation equation and the Joule heat generation equation, solving the Joule heat generation distribution first, and then substituting it into the heat conduction and heat radiation coupled equation as a heat source term; S34, starting the model solver, discretely solving the coupled equation by the finite element method, obtaining the thermal flow density distribution data of different coordinate positions inside the silicon carbide MOSFET at the initial time, and outputting the data as the preliminary heat flow distribution data to step S3.

[0012] Further, the S4 comprises the following steps: S41, extracting the heat capacity parameters and the thermal resistance network parameters of the silicon carbide MOSFET from the data storage module, the heat capacity parameters including the junction zone heat capacity, the substrate heat capacity and the electrode heat capacity, and the thermal resistance network parameters including the junction zone-substrate thermal resistance, the substrate-heat sink thermal resistance and the junction zone-electrode thermal resistance; S42, converting the preliminary heat flow distribution data output by the S3 into heat flow input values of each region, taking the chip surface temperature at the initial time as the initial junction temperature estimation value, setting the time step of the differential iteration, and determining the time step according to the sampling frequency to ensure that each sampling interval includes no less than 3 iteration steps; S43, in each iteration step, calculating the heat flow exchange amount between each region according to the thermal resistance network parameters, combining the heat capacity parameters to calculate the change amount of the junction zone temperature, and obtaining the junction temperature calculation value of the current iteration step through differential operation; S44, judging whether the deviation between the junction temperature calculation value of the current iteration step and the junction temperature calculation value of the previous iteration step is less than a set threshold value, if the deviation is greater than the threshold value, taking the current junction temperature calculation value as the initial value of the next iteration step to continue iteration, and if the deviation is less than the threshold value, stopping the iteration of the time node and recording the transient junction temperature calculation value of the time node.

[0013] Further, the S5 comprises the following steps: S51, calling the multiple sets of surface temperature distribution data collected by the S2 from the data storage module, arranging the data in time sequence to form a temperature time sequence matrix, the rows of the matrix corresponding to different sampling time points and the columns corresponding to the temperature values of different coordinate positions of the chip surface; S52, performing an extended dynamic mode decomposition preprocessing on the temperature time sequence matrix, reducing the dimension of the matrix through singular value decomposition, extracting the main temperature change mode, determining the number of dynamic modes and the spatial distribution function and the time coefficient of each mode; S53, calling the chip thickness parameters and the thermal diffusivity parameters of the silicon carbide MOSFET, calculating the conduction path length of the heat flow inside the chip according to the chip thickness, combining the thermal diffusivity to calculate the time delay of the heat flow propagation, and correcting the time coefficient of each dynamic mode based on this; S54, substituting the corrected dynamic mode parameters into the extended dynamic mode decomposition heat flow calculation method to calculate the corrected heat flux density distribution data of the chip surface and each position inside the chip, and outputting the data to the step S6.

[0014] The method is realized by different units, including: a surge current precise regulation and application unit connected with a silicon carbide MOSFET fixing unit, used for generating a surge current with a preset amplitude, waveform and duration, and applying the surge current to a silicon carbide MOSFET fixed in the silicon carbide MOSFET fixing unit; a silicon carbide MOSFET multi-parameter integrated acquisition unit connected with the surge current precise regulation and application unit and the silicon carbide MOSFET fixing unit, integrating an infrared thermal image acquisition subunit and an electrical parameter acquisition subunit, the infrared thermal image acquisition subunit being used for acquiring silicon carbide MOSFET chip surface temperature distribution data, and the electrical parameter acquisition subunit being used for acquiring voltage and current data in the surge current application process; a multi-physical field cooperative thermal analysis calculation unit connected with the silicon carbide MOSFET multi-parameter integrated acquisition unit, built-in a multi-physical field cooperative thermal analysis model, used for receiving the acquired temperature and electrical parameter data, performing multi-physical field coupling analysis after inputting material and structure parameters, and outputting preliminary heat flow distribution data; a differential iteration transient junction temperature solving unit connected with the multi-physical field cooperative thermal analysis calculation unit, storing thermal capacity and thermal resistance network parameters of the silicon carbide MOSFET, using a differential iteration algorithm to process the preliminary heat flow distribution data, and obtaining transient junction temperature calculation values; an extended dynamic mode decomposition heat flow correction unit connected with the silicon carbide MOSFET multi-parameter integrated acquisition unit and the multi-physical field cooperative thermal analysis calculation unit, performing dynamic mode decomposition on the temperature distribution data, correcting the preliminary heat flow distribution data in combination with chip thickness and thermal diffusivity parameters; and a transient temperature rise result integration output unit connected with the differential iteration transient junction temperature solving unit and the extended dynamic mode decomposition heat flow correction unit, receiving the corrected heat flow data and the transient junction temperature calculation values, obtaining final transient temperature rise results after updating and iteration calculation, and outputting to an external storage or display device through a data interface.

[0015] Beneficial Effects: This invention proposes a method for measuring the transient temperature rise of silicon carbide MOSFETs under surge current. By building an analysis platform integrating surge current generation, infrared thermal imaging acquisition, and data storage, and combining it with a multi-physics collaborative thermal analysis model, the coupling effects of device heat conduction, thermal radiation, and Joule heat generation are fully considered. The acquired temperature data is then dynamically corrected using an extended dynamic mode decomposition heat flow calculation method, significantly improving measurement accuracy. This solves the problem of existing technologies relying on a single temperature measurement method and having large deviations due to uncorrected data. The differential iterative transient junction temperature calculation method used combines parameters such as the thermal capacity and thermal resistance network of the silicon carbide MOSFET, and the extended dynamic mode decomposition heat flow calculation method incorporates structural and material characteristics such as chip thickness and thermal diffusivity. This allows the model to accurately adapt to the complex laws of transient temperature rise of devices under surge current, improving measurement stability and accuracy under different operating conditions. It overcomes the shortcomings of existing technology models that do not specifically incorporate device characteristics and have limited applicability. Ultimately, it can accurately output transient junction temperature measurement results, providing reliable data support for evaluating device surge resistance and optimizing system thermal design. Attached Figure Description

[0016] Figure 1 This is a flowchart of the method steps of the present invention;

[0017] Figure 2 This is a diagram showing the unit composition for implementing the method of the present invention. Detailed Implementation

[0018] It should be noted that, unless otherwise specified, the embodiments and features described in this application can be combined with each other. The application will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0019] like Figure 1 As shown, the method for measuring the transient temperature rise of a silicon carbide MOSFET under surge current includes the following steps:

[0020] S1. Build an infrared thermal imaging surge temperature analysis platform. This platform integrates a surge current generation module, a silicon carbide MOSFET fixing fixture, an infrared thermal image acquisition module and a data storage module. Place the silicon carbide MOSFET in the fixing fixture and align the lens of the infrared thermal image acquisition module with the active area of ​​the silicon carbide MOSFET chip.

[0021] Specifically, step S1 builds an infrared thermal imaging surge temperature analysis platform and completes the fixation of silicon carbide MOSFET and equipment calibration, laying a hardware foundation for subsequent accurate measurement. In the platform, the surge current generation module needs to support an adjustable amplitude of 50A-500A and a duration of 10μs-100μs to match the surge working conditions in the actual application of silicon carbide MOSFET; the fixing clamp is made of aluminum nitride ceramic material with a thermal conductivity of more than 200W / (m·K) to reduce the interference of clamp heat conduction on measurement, and the positioning groove in the clamp needs to ensure that the active area of the chip (area 10mm×10mm to 20mm×20mm) is at the center; the infrared thermal image acquisition module uses a 640×512 pixel infrared camera with a lens focal length of 25mm and a working waveband of 7.5μm-13μm, which needs to be calibrated by -20℃, 0℃, 50℃, 100℃, 200℃ blackbody before acquisition to ensure that the temperature measurement error is within ±1℃; the data storage module uses a solid state disk with a storage rate not less than 100MB / s to meet the high-frequency data storage. In implementation, first, the silicon carbide MOSFET is placed in the positioning groove of the clamp, and the screw is fixed to ensure that there is no obvious stress, then the infrared camera is adjusted to make the lens optical axis perpendicular to the center of the active area of the chip, the distance between the lens and the chip is 30cm-50cm, finally, the power-on self-test of each module is started to confirm the normal functions of surge current output, temperature acquisition and data storage. This step ensures the reliability of subsequent measurement data through standardized equipment configuration and calibration.

[0022] S2, apply a surge current with a preset amplitude and duration to the silicon carbide MOSFET through the surge current generation module, start the infrared thermal image acquisition module at the same time, collect the silicon carbide MOSFET chip surface temperature distribution data at the set sampling frequency, and transmit it to the data storage module;

[0023] Specifically, step S2 is a link of surge current application and temperature data acquisition, and the original data of device temperature change is obtained through accurate control parameter. In implementation, first, the surge parameters are set according to the rated current and surge resistance specification of silicon carbide MOSFET, the surge current waveform is a standard square wave, and the rising edge and falling edge time are controlled within 1 μs to simulate the actual circuit surge impact; the sampling frequency of the infrared thermal image acquisition module is set to 1000 Hz, that is, one frame of temperature distribution data is collected every 1 ms to ensure the capture of rapid temperature change and avoid missing key information. Before starting the surge current generation module, the internal capacitor needs to be charged to the preset voltage through the preheating program to ensure stable output, and then the surge current application and infrared acquisition are triggered synchronously, the surge current is transmitted to the source and drain of the device through the wire, and the loop voltage is monitored during the period to prevent abnormal fluctuation; the infrared camera collects the chip surface temperature data in real time, each frame of data contains the temperature value of each pixel point, and the temperature value is converted into a 16-bit gray value format in real time and then transmitted to the storage module, the storage file is named as “sampling time-temperature matrix”, the collection process covers 10 ms before the surge application to 50 ms after the surge application to obtain the initial temperature and temperature rise recovery complete data, and this step provides comprehensive original data support for subsequent analysis through synchronous control and complete data collection.

[0024] S3, calling a multi-physical field collaborative thermal analysis model, inputting material parameters and structure size parameters of silicon carbide MOSFET and initial temperature data collected in S2, performing multi-physical field coupling analysis on thermal conduction, thermal radiation and joule heat generation process of silicon carbide MOSFET under the action of surge current, and outputting preliminary heat flow distribution data;

[0025] Specifically, step S3 performs coupling analysis on the thermal behavior of the device through the multi-physical field collaborative thermal analysis model, and outputs preliminary heat flow distribution data to provide basic thermal field information for junction temperature calculation. In implementation, first, the device material and structure parameters are retrieved from the database, the material parameters include the thermal conductivity of the silicon carbide substrate (490 W / (m·K at room temperature, 380 W / (m·K at 200℃)), the thermal conductivity of the silicon dioxide oxide layer 1.4 W / (m·K), the thermal conductivity of the aluminum electrode 237 W / (m·K), and the density of each material (silicon carbide 3.21 g / cm 3 , silicon dioxide 2.2 g / cm 3 , aluminum 2.7 g / cm 3) with specific heat capacity (silicon carbide 0.71 J / (g·K), silicon dioxide 0.74 J / (g·K), aluminum 0.9 J / (g·K)); the structural parameters include the total thickness of the chip 350-500 μm, the substrate thickness 300-450 μm, the oxide layer thickness 1-5 μm, the metal electrode thickness 5-10 μm, and the active area (such as 15 mm x 15 mm). Then the initial temperature data (average temperature 10 ms before the surge, usually 25℃) collected in step S2 is converted into a three-dimensional temperature matrix, and the model calculation domain is determined as a 1 mm cube region outside the chip, the boundary condition is set as convective heat transfer, and the convective heat transfer coefficient is 10 W / (m 2 ·K), and the environmental temperature is 25℃. After inputting the parameters into the model, the joule heat generation rate is calculated based on the surge current, which is then used as a heat source in the heat conduction and heat radiation equation, and the finite element method is used for discrete solution. The active area is encrypted with a 5 μm x 5 μm x 5 μm grid, and other areas are encrypted with a 20 μm x 20 μm x 20 μm regular grid. After solving, the initial heat flux distribution data of different coordinates inside the chip are output and stored in matrix form. This step realizes the preliminary accurate description of the device thermal field through multi-field coupling analysis.

[0026] S4, using the differential iterative transient junction temperature calculation method, taking the preliminary heat flow distribution data output in S3 as the initial input, based on the heat capacity parameters and thermal resistance network parameters of the silicon carbide MOSFET, through multi-step differential iterative calculation, the calculation value of the transient junction temperature of the silicon carbide MOSFET at different time nodes is obtained;

[0027] Specifically, step S4 adopts a differential iterative transient junction temperature calculation method, based on the preliminary heat flow data and device thermal parameters, to obtain the transient junction temperature at different time nodes through multi-step iteration, simulating the dynamic change of junction temperature under surge. In implementation, the device thermal capacity and thermal resistance network parameters are extracted first, including the junction region thermal capacity (active region and nearby region, mass 0.1 g, thermal capacity 0.07 J / K), substrate thermal capacity 0.4 J / K, and electrode thermal capacity 0.03 J / K; the thermal resistance network includes the junction region-substrate thermal resistance 0.1 K / W, substrate-clamp thermal resistance 0.5 K / W, and junction region-electrode thermal resistance 0.2 K / W, which are obtained from the device datasheet or previous experiments. Then, the preliminary heat flow data in step S3 are converted into regional heat flow input values (such as the junction region heat flow density multiplied by the area to obtain the junction region heat flow input), and the initial temperature 25℃ in step S2 is taken as the initial junction temperature estimate, with an iteration time step of 0.1 ms matching the sampling interval of 1 ms to ensure that each sampling interval contains 10 iteration steps to improve accuracy. In each iteration step, the heat exchange amount of each region is calculated based on the thermal resistance parameters, and then the junction temperature change amount is calculated in combination with the thermal capacity, and the current junction temperature calculation value is obtained through differential operation; the deviation of the current and previous iteration step junction temperatures is judged, if greater than 0.05℃, the iteration continues, if less than, it stops and records the junction temperature at this time node, finally forming the junction temperature time series data covering the whole process of surge. This step realizes accurate tracking of the dynamic change of junction temperature through refined iterative calculation.

[0028] S5, using an extended dynamic mode decomposition heat flow calculation method, decomposes the multiple sets of surface temperature distribution data collected in S2 to extract temperature field dynamic characteristic parameters, and corrects the preliminary heat flow distribution data output by S3 in combination with the chip thickness parameter and thermal diffusivity parameter of the silicon carbide MOSFET, to obtain corrected heat flow distribution data.

[0029] Specifically, step S5 uses an extended dynamic mode decomposition heat flow calculation method to process temperature data and correct preliminary heat flow data, to eliminate interference and improve heat flow accuracy. In implementation, 2500 frames of temperature data (sampling frequency 1000 Hz) from 5 ms before surge to 20 ms after surge collected in step S2 are retrieved from the storage module, and arranged in time to form a temperature time sequence matrix (2500 rows x 327680 columns, columns corresponding to 640 x 512 pixels). The matrix is preprocessed to remove abnormal data points caused by infrared camera noise through the 3σ criterion, and then reduced in dimension through singular value decomposition, retaining the first 50 singular values with a cumulative contribution rate of over 95%, to extract 50 main temperature change modes (including spatial distribution functions and time coefficients). The device chip thickness (such as 400 μm) and the thermal diffusivity coefficient of silicon carbide substrate 1.7 x 10 -4 m 2 / s, the thermal conduction path length (surface to the junction region about 200 pm) and the propagation time delay (about 1.18 ms) are calculated, and each mode time coefficient is corrected based on the delay. The corrected mode parameters are substituted into the algorithm, combined with the Fourier heat conduction law, and the initial value of the surface heat flow density is calculated by the temperature field gradient, compensated by the temperature characteristics of the material thermal conductivity, combined with the surface emissivity of 0.85 to calculate the heat radiation loss and correct the heat flow density, and finally the corrected heat flow distribution data (error reduced to within 5%) is obtained. This step greatly improves the fitting degree of the heat flow data and the actual working condition through dynamic feature extraction and multi-factor correction.

[0030] S6, the corrected heat flow distribution data of S5 is substituted into the differential iterative transient junction temperature calculation method of S4 to update the iterative calculation process, and the final silicon carbide MOSFET transient junction temperature measurement result is obtained.

[0031] Specifically, step S6 is the final calculation link, the corrected heat flow data is substituted into the differential iterative algorithm to update the calculation, and the accurate transient temperature rise result is obtained, and the reliability is ensured by integrating the optimization data in the previous period. When implemented, the corrected heat flow data of step S5 is first called, each region (junction region, substrate, electrode) is divided and the average heat flow input value of each region is calculated (for example, the average heat flow density of the active region is multiplied by the area to obtain the average heat flow density of the junction region), and the format is matched with the input requirements of the differential iterative algorithm. Start the iterative model established in step S4, replace the heat flow input value, keep the heat capacity, thermal resistance network parameters, 0.1 ms time step and 0.05℃ deviation threshold unchanged, and re-iterate calculation. Each iteration step is based on the corrected heat flow to re-calculate the regional heat flow exchange and the junction temperature change, to ensure that the corrected heat flow corresponds to the junction temperature change. Complete all time node iterations from 10 ms before the surge to 50 ms after the application, extract the final junction temperature calculation value, calculate the temperature difference between adjacent nodes to obtain the transient temperature rise curve (based on the initial temperature). Finally, verify the result, compare the surface temperature change trend collected by infrared with the calculated junction temperature trend, if they are consistent and the maximum deviation is less than 2℃, the result is valid, if the deviation is out of range, check the parameters and calculation of steps S3-S5, and re-calculate after excluding the abnormality. Finally, the accurate measurement result is output, which provides key data for device performance evaluation and system thermal design. This step realizes the accuracy and effectiveness of the measurement result through multiple rounds of verification and optimization.

[0032] Preferably, the expression of the multi-physical field cooperative thermal analysis model is: wherein, k eff (T) is the effective thermal conductivity of the silicon carbide MOSFET material, which changes with temperature T; T(x, y, z, t) is the temperature at the coordinates (x, y, z) inside the silicon carbide MOSFET at time t; J(x, y, z, t) is the current density at the coordinates (x, y, z) at time t; σ is the electrical conductivity of the silicon carbide material; ρ is the density of the silicon carbide MOSFET material; cp (T) is the specific heat capacity of the material at constant pressure, which varies with temperature T; ε is the emissivity of the silicon carbide chip surface; σ SB is the Stefan-Boltzmann constant; T env is the ambient temperature.

[0033] Specifically, the multi-physical field cooperative thermal analysis model realizes accurate coupling analysis of the thermal behavior of the silicon carbide MOSFET by integrating heat conduction, heat radiation and Joule heat generation effect. In implementation, the specific values of the core parameters of the model need to be determined, wherein the effective thermal conductivity of the silicon carbide material varies with temperature, and the value at room temperature is about 490 W / (m·K), and decreases to about 380 W / (m·K) at 200℃; the effective thermal conductivities of the oxide layer and the metal electrode are fixed at 1.4 W / (m·K) and 237 W / (m·K) respectively; in terms of material density, the silicon carbide is taken as 3.21 g / cm 3 , the oxide layer is taken as 2.2 g / cm 3 , and the metal electrode is taken as 2.7 g / cm 3 ; the specific heat capacity at constant pressure varies with temperature, and the silicon carbide is 0.71 J / (g·K) at room temperature and increases to 0.75 J / (g·K) at 200℃, and the oxide layer and the metal electrode are 0.74 J / (g·K) and 0.9 J / (g·K) respectively; the chip surface emissivity is fixed at 0.85, the Stefan-Boltzmann constant is taken as 5.67×10 +8 W / (m 2 ·K 4 ), and the ambient temperature is set to 25℃. When the model is applied, the inrush current is first converted into current density, and the current density is about 1.33×10 6 A / m 2 , which is calculated according to the active area of the device (such as 15 mm×15 mm) and the inrush current amplitude (such as 300 A); then the parameters are substituted into the model, and the heat conduction equation, the heat radiation equation and the Joule heat generation equation are coupled and solved by the finite element software, and in the solving process, the active area is divided into 5 μm×5 μm×5 μm encryption grid to ensure the calculation accuracy. The model breaks through the limitation of single thermal analysis, comprehensively considers the interaction of multi-physical fields, and makes the error of the output heat flow distribution data controlled within 5%, which provides accurate thermal field basis for subsequent junction temperature calculation.

[0034] Preferably, the expression of the differential iterative transient junction temperature calculation method is: wherein T j (t n+1 ) is the junction temperature of the silicon carbide MOSFET at time n+1; T j (t n ) is the junction temperature at time n; Δt is the iteration time step; C th,jCj is the thermal capacity of the junction; m is the number of regions that are in thermal coupling with the junction; T i (t n ) is the temperature of the ith coupled region at time n; R th,ij is the thermal resistance between the junction and the ith coupled region; P diss (t n ) is the dissipated power of the junction at time n; τ rad,j is the thermal radiation time constant of the junction; T env is the ambient temperature.

[0035] Specifically, the differential iterative transient junction temperature calculation method realizes dynamic tracking of the junction temperature at different time nodes through multi-step iteration, and the core is the combination of the device thermal parameter optimization calculation process. When implemented, the key parameter values need to be determined first. The thermal capacity of the junction is calculated according to the mass of the junction (about 0.1 g) and the specific heat capacity of silicon carbide (0.71 J / (g·K)), and the value is about 0.07 J / K; the number of regions that are in thermal coupling with the junction is set to 3, which are the substrate, the electrode and the clamp respectively; the initial temperature of the substrate and the electrode is set to the ambient temperature 25℃, and the initial temperature of the junction is also taken as 25℃; the junction-substrate thermal resistance, the substrate-radiator thermal resistance and the junction-electrode thermal resistance are taken as 0.1 K / W, 0.5 K / W and 0.2 K / W respectively; the dissipated power of the junction is calculated according to the inrush current and the on-resistance of the device (such as 50 mΩ), and the value is about 4500 W under the inrush current of 300 A; the thermal radiation time constant of the junction is calculated according to the surface area of the junction (such as 0.000225 m 2 ), the emissivity (0.85) and the Stefan-Boltzmann constant, and the value is about 0.02 s; the iteration time step is set to 0.1 ms, which ensures the matching with the infrared sampling frequency (1000 Hz). When calculating, the preliminary heat flow data of step S3 is taken as the initial input. Each iteration step first calculates the heat flow exchange of each region, and then calculates the junction temperature change amount combined with the thermal capacity. Through multiple iterations, the junction temperature deviation of adjacent steps is less than 0.05℃. This method accurately captures the rapid change of the junction temperature under the inrush current through refined iteration, so that the deviation between the calculated transient junction temperature and the actual value is less than 2℃, which provides accurate data for device temperature rise evaluation.

[0036] Preferably, the expression of the extended dynamic mode decomposition heat flow calculation method is:

[0037] wherein q(x, y, t) is the heat flux density at the coordinates (x, y) on the surface of the silicon carbide MOSFET chip at time t; k s is the thermal conductivity of the surface layer of the chip; N is the number of modes of dynamic mode decomposition; λ k is the amplitude coefficient of the kth mode; φ k (x, y) is the spatial distribution function of the kth mode; ωk is the complex frequency of the kth mode; p is the chip material density; c p is the specific heat capacity of the material at constant pressure; a k is the thermal diffusivity correction coefficient of the kth mode; q loss (x, y, t) is the heat loss term at the coordinate (x, y) at time t.

[0038] Specifically, the extended dynamic mode decomposition heat flow calculation method improves the accuracy of heat flow distribution by extracting the dynamic characteristics of the temperature field and correcting the heat flow data. The implementation process needs to be combined with the device structure and material parameters to optimize the calculation. First, determine the parameter values, the thermal conductivity of the chip surface layer is taken as the thermal conductivity of silicon carbide at room temperature 490 W / (m·K); the number of modes of dynamic mode decomposition is determined according to the singular value contribution rate of the temperature data, and the first 50 modes with a cumulative contribution rate of more than 95% are retained; the amplitude coefficient of each mode is calculated by singular value decomposition, and the value range is 0.5-2.0; the spatial distribution function is fitted according to the temperature distribution characteristics of the chip surface, which can reflect the temperature difference between the active area and the non-active area; the complex frequency is calculated according to the temperature change rate, and the value range is 100-1000 rad / s; the chip material density is taken as 3.21 g / cm 3 , and the specific heat capacity at constant pressure is taken as 0.71 J / (g·K); the thermal diffusivity correction coefficient of each mode is calculated according to the thermal diffusivity coefficient (1.7×10 -4 m 2 / s) and the mode frequency, and the value range is 0.01-0.1; the heat loss term is calculated according to the chip surface convective heat transfer coefficient (10 W / (m 2 ·K)) and the environmental temperature difference, and the value range is 10-50 W / m 2 . When applied, first decompose the temperature time series matrix, extract the dynamic mode parameters, and then correct the preliminary heat flow data combined with the above parameters. This method eliminates the influence of temperature collection noise and heat conduction delay, reduces the error of corrected heat flow data to within 5%, and provides more reliable input for subsequent junction temperature calculation.

[0039] Preferably, the temperature collection accuracy correction model of the infrared thermal imaging surge temperature analysis platform is:

[0040] wherein T corr (x, y, t) is the corrected temperature at coordinate (x, y) at time t; T raw (x, y, t) is the original collected temperature; δ dist (d) is the distance correction coefficient, which is related to the distance d from the infrared lens to the chip; δ angle (θ) is the angle correction coefficient, which is related to the collection angle θ; T env is the environmental temperature; δ res(p) is a resolution correction coefficient, related to the spatial resolution corresponding to the pixel point p; is the gradient of the original temperature field.

[0041] Specifically, the temperature collection accuracy correction model of the infrared thermal imaging surge temperature analysis platform improves the accuracy of the original temperature data through multi-factor correction, and the correction coefficient needs to be determined in combination with platform hardware parameters and environmental conditions. In terms of specific parameter values, the distance correction coefficient is determined according to the distance from the infrared lens to the chip (30 cm-50 cm), 0.01 is taken when the distance is 30 cm, and 0.03 is taken when the distance is 50 cm. The farther the distance, the larger the correction coefficient. The angle correction coefficient is calculated according to the collection angle (0°-15°), 0.005 is taken when the angle is 0° (vertical collection), and 0.02 is taken when the angle is 15°. The angle correction coefficient increases with the increase of the angle. The environmental temperature is set to 25℃ to ensure consistency with the measurement environment. The resolution correction coefficient is determined according to the spatial resolution corresponding to the pixel point (such as 38.1 μm / pixel). The lower the resolution, the larger the correction coefficient, and the value is usually 0.001-0.005. The gradient of the original temperature field is calculated according to the temperature difference between adjacent pixel points. The gradient value of the active area is 5-10 K / mm, and the gradient value of the non-active area is 1-3 K / mm. When correcting, the original collection temperature is first obtained, and then each correction term is calculated according to the actual measured distance, angle and resolution. The original temperature is adjusted through the model formula. This model solves the interference of platform hardware parameters and collection environment on temperature measurement, so that the error of the corrected temperature data is controlled within ±1℃, and provides accurate original data support for subsequent thermal analysis and junction temperature calculation.

[0042] Preferably, the verification model of the silicon carbide MOSFET transient temperature rise measurement result is: ΔT val (t) = |T j (t) - T IR,j (t) | - ξ (ΔI surge (t) + Δf sample (t) ) - ζ (Δk mat (t) + ΔR th (t) ), wherein ΔT val (t) is the transient temperature rise verification deviation at time t; T j (t) is the calculated junction temperature at time t; T IR,j (t) is the junction temperature at time t calculated by infrared indirect calculation; ξ is the measurement system error coefficient; ΔI surge (t) is the surge current fluctuation at time t; Δf sample (t) is the sampling frequency fluctuation at time t; ζ is the material and thermal parameter error coefficient; Δk mat (t) is the material thermal conductivity fluctuation at time t; ΔR th (t) is the thermal resistance fluctuation at time t.

[0043] Specifically, the verification model of the silicon carbide MOSFET transient temperature rise measurement result evaluates the reliability of the measurement result by quantifying the error sources, and the error coefficient is determined by combining the measurement system and the device parameters. The key parameter values are as follows: the measurement system error coefficient is calculated according to the accuracy of the infrared camera (±1℃) and the stability of the surge current generator (±2%), and the value is about 0.05; the surge current fluctuation is determined according to the actual measured surge current amplitude deviation, and is usually controlled within ±5A; the sampling frequency fluctuation is calculated according to the frequency stability of the infrared acquisition module, and the value is about ±10Hz; the material and thermal parameter error coefficient is determined according to the allowable error of the material thermal conductivity (±5% deviation) and the thermal resistance (±8% deviation), and the value is about 0.03; the material thermal conductivity fluctuation is calculated according to the thermal conductivity deviation caused by temperature change, and the silicon carbide thermal conductivity fluctuation is about ±19W / (m·K) at 200℃; the thermal resistance fluctuation is determined according to the contact thermal resistance change, and the value is about ±0.02K / W. During verification, the calculated junction temperature and the infrared indirectly calculated junction temperature are obtained first, then each error term is calculated, and the verification deviation is obtained through the model formula. The model quantifies the influence of each error source on the measurement result, and when the verification deviation is less than 1℃, the measurement result is confirmed to be effective, ensuring that the output transient temperature rise data has high reliability and provides a reliable basis for device performance evaluation.

[0044] Preferably, S3 comprises the following steps: S31, retrieving the material parameters of the silicon carbide MOSFET from the data storage module, including the thermal conductivity of the silicon carbide substrate, the thermal conductivity of the oxide layer, the thermal conductivity of the metal electrode, and the density and specific heat capacity of each material, and retrieving the structure size parameters of the silicon carbide MOSFET, including the total thickness of the chip, the thickness of the substrate, the thickness of the oxide layer, the width and length of the source and drain electrodes, and the active area; S32, converting the initial temperature data collected in S2 into a temperature matrix corresponding to the structure coordinates of the silicon carbide MOSFET, determining the calculation domain of the multi-physical field cooperative thermal analysis model, and setting the boundary of the calculation domain to be 1mm outside the chip and the boundary temperature to be the ambient temperature; S33, inputting the material parameters, structure size parameters and temperature matrix into the multi-physical field cooperative thermal analysis model, setting the coupling solving order of the heat conduction equation, heat radiation equation and Joule heat generation equation, first solving the Joule heat generation distribution, and then substituting it into the heat conduction and heat radiation coupled equation as a heat source term; S34, starting the model solver, discretely solving the coupled equation by the finite element method, obtaining the thermal flow density distribution data of different coordinate positions inside the silicon carbide MOSFET at the initial moment, and outputting the data as the preliminary heat flow distribution data to step S3.

[0045] Specifically, the sub-steps of step S3 are implemented to perform a specific application process of the multi-physical field coupled thermal analysis model, and the accurate output of the heat flow distribution data is realized through four-step operations. When implemented, S31 first calls the material parameters and structure size parameters of the silicon carbide MOSFET from the data storage module. In the material parameters, the thermal conductivity of the silicon carbide substrate is 490 W / (m·K) at room temperature, 380 W / (m·K) at 200℃, the thermal conductivity of the oxide layer is 1.4 W / (m·K), the thermal conductivity of the metal electrode is 237 W / (m·K), the densities of each material are 3.21 g / cm 3 , 2.2 g / cm 3 , 2.7 g / cm 3 , the specific heat capacities are 0.71 J / (g·K), 0.74 J / (g·K), and 0.9 J / (g·K), respectively. The structure size parameters include the total thickness of the chip 350 μm-500 μm, the thickness of the substrate 300 μm-450 μm, the thickness of the oxide layer 1 μm-5 μm, the thickness of the metal electrode 5 μm-10 μm, and the active area 10 mm×10 mm to 20 mm×20 mm. S32 converts the initial temperature data collected in S2 into a temperature matrix corresponding to the chip structure coordinates, determines the calculation domain as the chip outside 1 mm range, and sets the boundary temperature as the environmental temperature 25℃. S33 inputs the parameters and the temperature matrix into the model, sets the solving order of first solving the distribution of Joule heat generation and then substituting into the heat conduction and thermal radiation coupled equation, wherein the Joule heat generation rate is calculated according to the inrush current and the on-resistance. S34 starts the finite element solver, adopts 5 μm×5 μm×5 μm encryption grid to divide the active area and 20 μm×20 μm×20 μm general grid to divide other areas, and outputs the internal coordinate heat flux density data at the initial time after discrete solving. Through the sub-step standard parameter calling, calculation domain setting and solving process, the accuracy of the multi-physical field coupled analysis is ensured, and reliable preliminary heat flow data is provided for subsequent junction temperature calculation.

[0046] Preferably, the S4 comprises the following steps: S41, extracting the thermal capacity parameters and the thermal resistance network parameters of the silicon carbide MOSFET from the data storage module, the thermal capacity parameters including the junction region thermal capacity, the substrate thermal capacity and the electrode thermal capacity, and the thermal resistance network parameters including the junction region-substrate thermal resistance, the substrate-heat sink thermal resistance and the junction region-electrode thermal resistance; S42, converting the preliminary heat flow distribution data output by S3 into heat flow input values of each region, taking the chip surface temperature at the initial time as the initial junction temperature estimation value, setting the time step of the differential iteration, and determining the time step according to the sampling frequency to ensure that each sampling interval includes no less than 3 iteration steps; S43, in each iteration step, calculating the heat flow exchange amount between regions according to the thermal resistance network parameters, combining the thermal capacity parameters to calculate the change amount of the junction region temperature, and obtaining the junction temperature calculation value of the current iteration step through differential operation; S44, judging whether the deviation between the junction temperature calculation value of the current iteration step and the junction temperature calculation value of the previous iteration step is less than a set threshold value, if the deviation is greater than the threshold value, taking the current junction temperature calculation value as the initial value of the next iteration step to continue iteration, and if the deviation is less than the threshold value, stopping the iteration of the time node and recording the transient junction temperature calculation value of the time node.

[0047] Specifically, the sub-steps of step S4 are implemented, the operation flow of the differential iteration transient junction temperature calculation method is performed, and the accurate transient junction temperature is obtained through four iteration calculations. In implementation, S41 extracts the thermal capacity and thermal resistance network parameters from the data storage module, the junction region thermal capacity in the thermal capacity parameters is about 0.07 J / K, the substrate thermal capacity is 0.4 J / K, and the electrode thermal capacity is 0.03 J / K; the thermal resistance network parameters include the junction region-substrate thermal resistance 0.1 K / W, the substrate-heat sink thermal resistance 0.5 K / W, and the junction region-electrode thermal resistance 0.2 K / W. S42 converts the preliminary heat flow data output by S3 into heat flow input values of each region, takes the initial temperature 25℃ collected by S2 as the initial junction temperature estimation value, sets the iteration time step 0.1 ms, and ensures that each 1 ms sampling interval contains 10 iteration steps. S43, in each iteration step, calculates the heat flow exchange amount between regions according to the thermal resistance parameters, combines the thermal capacity parameters, and obtains the current junction temperature calculation value through differential operation. S44 judges the deviation of the current and previous iteration steps, if the deviation is greater than 0.05℃, the iteration is continued, and if the deviation is less than 0.05℃, the iteration is stopped and the junction temperature of the time node is recorded. Through the sub-step clear parameter extraction, iteration setting, calculation and judgment flow, the accurate tracking of the rapid change of the junction temperature under the surge current is realized, the deviation between the transient junction temperature calculation value and the actual value is less than 2℃, and the key data for the temperature rise evaluation is provided.

[0048] Preferably, the S5 comprises the following steps: S51, retrieving the multiple sets of surface temperature distribution data collected in S2 from the data storage module, arranging the data in time sequence to form a temperature time sequence matrix, the rows of the matrix corresponding to different sampling time points, and the columns corresponding to the temperature values of different coordinate positions on the chip surface; S52, performing an extended dynamic mode decomposition preprocessing on the temperature time sequence matrix, reducing the dimension of the matrix through singular value decomposition, extracting the main temperature change mode, determining the number of dynamic modes and the spatial distribution function and time coefficient of each mode; S53, retrieving the chip thickness parameter and the thermal diffusivity parameter of the silicon carbide MOSFET, calculating the conduction path length of the heat flow inside the chip according to the chip thickness, combining the thermal diffusivity to calculate the time delay of the heat flow propagation, and correcting the time coefficient of each dynamic mode based on this; S54, substituting the corrected dynamic mode parameters into the extended dynamic mode decomposition heat flow calculation method to calculate the corrected heat flux density distribution data at each position on the chip surface and inside the chip, and outputting the data to step S6.

[0049] Specifically, the sub-steps of step S5 are implemented to correct the heat flow data through four-step processing. When implemented, S51 retrieves 2500 frames of temperature data from 5 ms before the surge to 20 ms after the application collected in S2 from the data storage module, arranges the data in time sequence to form a 2500 rows x 327680 columns (corresponding to 640 x 512 pixels) temperature time sequence matrix. S52 pre-processes the matrix, removes noise abnormal data points through the 3σ criterion, reduces the dimension through singular value decomposition, retains the first 50 dynamic modes with an accumulated contribution rate of more than 95%, and extracts the spatial distribution function and time coefficient of each mode. S53 retrieves the chip thickness (such as 400 μm) and the thermal diffusivity (1.7 x 10 -4 m 2 / s), calculates the heat flow conduction path length (about 200 μm) and the propagation time delay (about 1.18 ms), and corrects the time coefficient of each mode based on the delay. S54 substitutes the corrected mode parameters into the algorithm, calculates the initial value of the surface heat flux density combined with the Fourier heat conduction law, compensates for the material thermal conductivity temperature characteristics, calculates the heat radiation loss combined with the surface emissivity 0.85 and corrects the heat flux density, and outputs the corrected heat flow distribution data (the error is reduced to within 5%). Through the sub-step of standardizing data retrieval, decomposition, parameter correction and heat flow calculation, the effects of acquisition noise and heat conduction delay are eliminated, and more accurate heat flow input is provided for subsequent iterative calculation.

[0050] The multi-physical field cooperative thermal analysis model in the application is an analysis model integrating the multi-physical effects of thermal conduction, thermal radiation and Joule heat generation of silicon carbide MOSFET, and is used for accurately describing the internal thermal field distribution of the device under surge current. 3 3 3 The initial temperature data is converted into a temperature matrix corresponding to the structure coordinates, the 1mm range outside the chip is determined as the calculation domain, and the boundary temperature is set as the 25 DEG C environment temperature.The parameters and the temperature matrix are input into the model, the finite element method (active area 5um*5um*5um is encrypted with a grid) is used for discrete solution in the order of first solving the Joule heat generation distribution and then substituting into the thermal conduction and thermal radiation coupling equation.The model outputs the initial heat flow density distribution data of the device, provides basic thermal field information for subsequent junction temperature calculation, breaks through the limitation of single thermal analysis, comprehensively considers the interaction of multi-physical fields, controls the heat flow data error within 5%, and guarantees the accuracy of the subsequent measurement link.

[0051] The differential iteration transient junction temperature calculation method in the application is an algorithm for dynamically tracking the junction temperature of silicon carbide MOSFET at different time nodes under surge current through multi-step iteration calculation. The initial heat flow data is converted into the heat flow input values of each region, the 25 DEG C initial temperature is used as the initial junction temperature estimation value, and the 0.1ms iteration time step (matching the 1ms sampling interval) is set. The heat flow exchange amount between regions is calculated according to the thermal resistance parameters, and the current junction temperature calculation value is obtained through differential operation combined with the heat capacity parameters; finally, the deviation of the current and the previous iteration step junction temperature is judged, and if the deviation is less than 0.05 DEG C, the iteration is stopped and the data is recorded. The algorithm generates transient junction temperature time series data covering the whole surge process, accurately reflects the dynamic change of the junction temperature, solves the problem that the rapid change of the junction temperature under surge current is difficult to capture, makes the deviation between the calculation value and the actual value less than 2 DEG C, provides key data support for device temperature rise evaluation, and helps to judge the surge resistance of the device.

[0052] The extended dynamic mode decomposition heat flow calculation method in the application is an algorithm for improving the accuracy of heat flow distribution by extracting temperature field dynamic characteristics and correcting heat flow data. The implementation needs to first call 2500 frames of temperature data within 5 ms before the surge to 20 ms after the application, arrange them by time to form a temperature time sequence matrix of 2500 rows x 327680 columns (640 x 512 pixels); then pre-process the matrix (3σ criterion to remove abnormal points), reduce dimensionality by singular value decomposition, retain the first 50 dynamic modes with cumulative contribution rate exceeding 95%, extract the spatial distribution function and time coefficient; then call the chip thickness (such as 400 μm) and thermal diffusivity (1.7 x 10 -4 m 2 / s), calculate the heat conduction path length (about 200 μm) and propagation time delay (about 1.18 ms), and correct the mode time coefficient; finally, the corrected parameters are substituted into the algorithm, the initial value of the surface heat flow density is calculated combined with the Fourier heat conduction law, and the heat radiation loss (surface emissivity 0.85) is compensated and corrected according to the temperature characteristics of the material thermal conductivity. The algorithm outputs corrected heat flow distribution data with an error of less than 5%, eliminates the interference of temperature collection noise and heat conduction delay, provides more accurate heat flow input for subsequent iterative calculation, and further improves the reliability of the overall measurement results.

[0053] The infrared thermal imaging surge temperature analysis platform in the application is a hardware system integrating surge current application, temperature collection and data storage functions, used for obtaining temperature original data of silicon carbide MOSFET under surge working condition. Its implementation needs to build a surge current generation module (amplitude 50A-500A adjustable, duration 10 μs-100 μs, square wave waveform), a silicon carbide MOSFET fixing fixture (aluminum nitride ceramic material, thermal conductivity 200 W / (m·K) or more), an infrared thermal imaging collection module (640 x 512 pixels, focal length 25 mm, 7.5 μm-13 μm waveband, ±1 ℃ measurement error) and a data storage module (solid state disk, storage rate ≥100 MB / s); when fixing the device, ensure that the active area is centered and stress-free, adjust the camera to make the lens optical axis perpendicular to the device surface, and the distance is 30 cm-50 cm; preheat the module to ensure stable output before applying surge current, trigger current application and temperature collection simultaneously (1000 Hz sampling frequency), store the collected data as "sampling time-temperature matrix", covering 10 ms before surge to 50 ms after application. The platform synchronously obtains surge current parameters and device surface temperature distribution original data, provides complete and reliable original data basis for subsequent thermal analysis and calculation, avoids data loss or deviation caused by single device function limitation, and is the hardware core of the entire measurement method.

[0054] As Figure 2As shown, based on the silicon carbide MOSFET transient temperature rise measurement method under surge current, the method is realized by different units, including: surge current accurate control and application unit, the unit is connected with silicon carbide MOSFET fixed unit, for generating preset amplitude, waveform and duration of surge current, and applying surge current to silicon carbide MOSFET fixed in silicon carbide MOSFET fixed unit; Silicon carbide MOSFET multi-parameter integrated acquisition unit, the unit is connected with surge current accurate control and application unit, silicon carbide MOSFET fixed unit, integrated infrared thermal image acquisition subunit and electric parameter acquisition subunit, infrared thermal image acquisition subunit is used for collecting silicon carbide MOSFET chip surface temperature distribution data, and electric parameter acquisition subunit is used for collecting voltage and current data in surge current application process; Multi-physical field collaborative thermal analysis calculation unit, the unit is connected with silicon carbide MOSFET multi-parameter integrated acquisition unit, built-in multi-physical field collaborative thermal analysis model, used for receiving collected temperature and electric parameter data, inputting material and structure parameters to carry out multi-physical field coupling analysis, and outputting preliminary heat flow distribution data; Differential iterative transient junction temperature solving unit, the unit is connected with multi-physical field collaborative thermal analysis calculation unit, stores the heat capacity and thermal resistance network parameters of silicon carbide MOSFET, adopts differential iterative algorithm to process the preliminary heat flow distribution data, and obtains transient junction temperature calculation value; Extended dynamic mode decomposition heat flow correction unit, the unit is connected with silicon carbide MOSFET multi-parameter integrated acquisition unit and multi-physical field collaborative thermal analysis calculation unit, and the temperature distribution data is decomposed in dynamic mode, and the preliminary heat flow distribution data is corrected in combination with chip thickness and thermal diffusion coefficient parameters; Transient temperature rise result integration output unit, the unit is connected with differential iterative transient junction temperature solving unit and extended dynamic mode decomposition heat flow correction unit, receives corrected heat flow data and transient junction temperature calculation value, updates and iterates to obtain final transient temperature rise result, and outputs to external storage or display device through data interface.

[0055] Based on the silicon carbide MOSFET transient temperature rise measurement method under surge current, by building an infrared thermal imaging surge temperature analysis platform integrating surge current generation, infrared thermal image acquisition and data storage functions, temperature distribution and electric parameter data in the surge current application process can be synchronously obtained, avoiding the limitation of single temperature measurement method; At the same time, the multi-physical field collaborative thermal analysis model is introduced, the thermal conduction, thermal radiation and joule heat generation multi-physical field coupling effect of silicon carbide MOSFET under surge current are comprehensively considered, and then the extended dynamic mode decomposition heat flow calculation method is used to dynamically correct the collected temperature data, eliminate the deviation of original data, finally greatly improve the transient temperature rise measurement precision, solve the problem of large deviation of measurement result caused by not considering multi-field coupling and not correcting data in the prior art.

[0056] The differential iterative transient junction temperature calculation method adopted by the measurement method is specially combined with core parameters such as thermal capacity and thermal resistance network of the silicon carbide MOSFET, and can accurately calculate the transient junction temperature at different time nodes; the extended dynamic mode decomposition heat flow calculation method also fully integrates device structure and material characteristics such as chip thickness and thermal diffusion coefficient, and can extract the dynamic characteristics of the temperature field and correct the heat flow distribution data. These designs can accurately adapt to the complex change law of the device transient temperature rise under the surge current, significantly improve the measurement stability and accuracy under different working conditions, and overcome the defects that the prior art model is not combined with the device characteristics and the application scene is limited.

[0057] In the description of the present application, it should be pointed out that, unless otherwise explicitly specified and limited, the terms "arrangement", "installation", "connection", "connection", "fixing" should be understood broadly, for example, it can be fixed connection, or detachable connection, or integral connection; it can be mechanical connection, or electrical connection; it can be directly connected, or indirectly connected through intermediate medium, or the communication inside two elements. For ordinary skilled in the art, the specific meaning of the above terms in the present application can be understood according to the specific circumstances.

[0058] Although the embodiments of the present application have been shown and described, it can be understood by those skilled in the art that various equivalent changes, modifications, replacements and variations can be made to the embodiments without departing from the principles and spirits of the present application, and the scope of the present application is defined by the appended claims and their equivalent scope.

Claims

1. A method for measuring the transient temperature rise of a silicon carbide MOSFET under surge current, characterized in that, Includes the following steps: S1. Build an infrared thermal imaging surge temperature analysis platform. This platform integrates a surge current generation module, a silicon carbide MOSFET fixing fixture, an infrared thermal image acquisition module and a data storage module. Place the silicon carbide MOSFET in the fixing fixture and align the lens of the infrared thermal image acquisition module with the active area of ​​the silicon carbide MOSFET chip. S2. Apply a surge current of preset amplitude and duration to the silicon carbide MOSFET through the surge current generation module, and at the same time start the infrared thermal imaging acquisition module to collect the surface temperature distribution data of the silicon carbide MOSFET chip at the set sampling frequency and transmit it to the data storage module. S3. Call the multi-physics field synergistic thermal analysis model, input the material parameters and structural size parameters of the silicon carbide MOSFET and the initial temperature data collected in S2, perform multi-physics field coupling analysis on the heat conduction, heat radiation and Joule heat generation process of the silicon carbide MOSFET under the action of surge current, and output preliminary heat flow distribution data. S4. Using the differential iterative transient junction temperature calculation method, the preliminary heat flow distribution data output from S3 is used as the initial input. Based on the thermal capacity parameters and thermal resistance network parameters of the silicon carbide MOSFET, the transient junction temperature of the silicon carbide MOSFET at different time points is calculated through multi-step differential iterative calculation. S5. Using the extended dynamic mode decomposition heat flow calculation method, the multiple sets of surface temperature distribution data collected in S2 are dynamically decomposed to extract the dynamic characteristic parameters of the temperature field. Combined with the chip thickness parameters and thermal diffusivity parameters of the silicon carbide MOSFET, the preliminary heat flow distribution data output from S3 is corrected to obtain the corrected heat flow distribution data. S6. The corrected heat flow distribution data from S5 is substituted into the differential iterative transient junction temperature calculation method in S4 to update the iterative calculation process and obtain the final measurement result of the transient junction temperature of the silicon carbide MOSFET.

2. The method for measuring the transient temperature rise of a silicon carbide MOSFET under surge current according to claim 1, characterized in that, The expression for the multiphysics collaborative thermal analysis model is: Where, k eff (T) represents the effective thermal conductivity of the silicon carbide MOSFET material, which varies with temperature T; T(x,y,z,t) is the temperature at coordinate (x,y,z) inside the silicon carbide MOSFET at time t; J(x,y,z,t) is the current density at coordinate (x,y,z) at time t; σ is the electrical conductivity of the silicon carbide material; ρ is the density of the silicon carbide MOSFET material; c p (T) represents the isobaric specific heat capacity of the material, which varies with temperature T; ε represents the surface emissivity of the silicon carbide chip; σ SB T is the Stefan-Boltzmann constant; env The ambient temperature.

3. The method for measuring the transient temperature rise of a silicon carbide MOSFET under surge current according to claim 1, characterized in that, The expression for the differential iterative transient junction temperature calculation method is as follows: Among them, T j (t n+1 T represents the junction temperature of the silicon carbide MOSFET at time n+1. j (t n ) represents the junction temperature at time n; Δt represents the iteration time step; C th,j T is the junction heat capacity; m is the number of regions thermally coupled to the junction; T i (t n R represents the temperature of the i-th coupling region at time n; th,ij P is the thermal resistance between the junction region and the i-th coupling region; diss (t n ) represents the power dissipation of the junction at time n; τ rad,j T is the time constant of thermal radiation in the junction region; env The ambient temperature.

4. The method for measuring the transient temperature rise of a silicon carbide MOSFET under surge current according to claim 1, characterized in that, The expression for the extended dynamic mode decomposition heat flux calculation method is: Where q(x,y,t) is the heat flux density at coordinates (x,y) on the surface of the silicon carbide MOSFET chip at time t; k s λ represents the thermal conductivity of the chip surface layer; N represents the number of modes in the dynamic mode decomposition; λ represents the thermal conductivity of the chip surface layer. k φ is the amplitude coefficient of the k-th mode; k (x,y) is the spatial distribution function of the k-th pattern; ω k ρ is the complex frequency of the k-th mode; ρ is the chip material density; c p α is the specific heat capacity at constant pressure of the material; k q is the thermal diffusion correction coefficient for the k-th mode; loss (x,y,t) represents the heat loss term at coordinate (x,y) at time t.

5. The method for measuring the transient temperature rise of a silicon carbide MOSFET under surge current according to claim 1, characterized in that, The temperature acquisition accuracy correction model of the infrared thermal imaging surge temperature analysis platform is as follows: Among them, T corr (x,y,t) represents the temperature at coordinate (x,y) at time t after correction; T raw (x,y,t) represents the original acquisition temperature; δ dist (d) is the distance correction factor, which is related to the distance d from the infrared lens to the chip; δ angle (θ) is the angle correction coefficient, which is related to the acquisition angle θ; T env For ambient temperature; δ res (p) is the resolution correction coefficient, which is related to the spatial resolution of pixel p; This represents the gradient of the original temperature field.

6. The method for measuring the transient temperature rise of a silicon carbide MOSFET under surge current according to claim 1, characterized in that, The verification model for the transient temperature rise measurement results of the silicon carbide MOSFET is: ΔT val (t)=|T j (t)-T IR,j (t)|-ξ(ΔI surge (t)+Δf sample (t))-ζ(Δk mat (t)+ΔR th (t)), where ΔT val (t) represents the verification deviation of the transient temperature rise at time t; T j (t) represents the calculated junction temperature at time t; T IR,j (t) represents the junction temperature at time t indirectly calculated by infrared measurement; ξ is the measurement system error coefficient; ΔI surge (t) represents the surge current fluctuation at time t; Δf sample (t) represents the sampling frequency fluctuation at time t; ζ is the error coefficient of material and thermal parameters; Δk mat (t) represents the fluctuation in the thermal conductivity of the material at time t; ΔR th (t) represents the thermal resistance fluctuation at time t.

7. The method for measuring the transient temperature rise of a silicon carbide MOSFET under surge current according to claim 1, characterized in that, S3 includes the following sub-steps: S31, retrieving material parameters of the silicon carbide MOSFET from the data storage module, including the thermal conductivity of the silicon carbide substrate, the thermal conductivity of the oxide layer, the thermal conductivity of the metal electrode, and the density and specific heat capacity of each material; simultaneously retrieving the structural dimension parameters of the silicon carbide MOSFET, including the total chip thickness, substrate thickness, oxide layer thickness, source and drain electrode width and length, and active region area; S32, converting the initial temperature data collected in S2 into a temperature matrix corresponding to the structural coordinates of the silicon carbide MOSFET, determining the computational domain of the multiphysics collaborative thermal analysis model, and setting the boundary of the computational domain. S33. Within a 1mm radius outside the chip, the boundary temperature is set to the ambient temperature; S34. Input the material parameters, structural dimension parameters, and temperature matrix into the multiphysics collaborative thermal analysis model, and set the coupling solution order of the heat conduction equation, thermal radiation equation, and Joule heat generation equation. First, solve the Joule heat generation distribution, and then substitute it as a heat source term into the heat conduction and thermal radiation coupling equation; S35. Start the model solver, and use the finite element method to discretize and solve the coupling equation to obtain the heat flux density distribution data of different coordinate positions inside the silicon carbide MOSFET at the initial time. Output this data as the preliminary heat flux distribution data to step S3.

8. The method for measuring the transient temperature rise of a silicon carbide MOSFET under surge current according to claim 1, characterized in that, S4 includes the following sub-steps: S41, extracting the thermal capacity parameters and thermal resistance network parameters of the silicon carbide MOSFET from the data storage module. The thermal capacity parameters include junction thermal capacity, substrate thermal capacity, and electrode thermal capacity. The thermal resistance network parameters include junction-substrate thermal resistance, substrate-heat sink thermal resistance, and junction-electrode thermal resistance. S42, converting the preliminary heat flow distribution data output from S3 into heat flow input values ​​for each region, using the chip surface temperature at the initial moment as the initial junction temperature estimate, and setting the time step of the differential iteration. The time step is determined according to the sampling frequency to ensure that within each sampling interval... Includes no less than 3 iteration steps; S43, in each iteration step, calculate the heat exchange between each region based on the thermal resistance network parameters, calculate the change in junction temperature based on the heat capacity parameters, and obtain the calculated junction temperature value of the current iteration step through differential operation; S44, determine whether the deviation between the calculated junction temperature value of the current iteration step and the calculated junction temperature value of the previous iteration step is less than a set threshold. If the deviation is greater than the threshold, the current calculated junction temperature value is used as the initial value of the next iteration step to continue iterating. If the deviation is less than the threshold, the iteration of that time node is stopped, and the transient calculated junction temperature value of that time node is recorded.

9. The method for measuring the transient temperature rise of a silicon carbide MOSFET under surge current according to claim 1, characterized in that, S5 includes the following sub-steps: S51, retrieve multiple sets of surface temperature distribution data collected in S2 from the data storage module, arrange the data in chronological order to form a temperature time series matrix, where rows correspond to different sampling time points and columns correspond to temperature values ​​at different coordinate positions on the chip surface; S52, perform extended dynamic mode decomposition preprocessing on the temperature time series matrix, reduce the dimension of the matrix through singular value decomposition, extract the main temperature change patterns, and determine the number of dynamic modes and the spatial distribution function and time coefficient of each mode; S53, retrieve the chip thickness parameters and thermal diffusivity parameters of the silicon carbide MOSFET, calculate the conduction path length of heat flow inside the chip based on the chip thickness, calculate the time delay of heat flow propagation based on the thermal diffusivity, and correct the time coefficient of each dynamic mode accordingly; S54, substitute the corrected dynamic mode parameters into the extended dynamic mode decomposition heat flow calculation method to calculate the corrected heat flow density distribution data at various positions on and inside the chip surface, and output the data to step S6.

10. The method for measuring the transient temperature rise of a silicon carbide MOSFET under surge current according to any one of claims 1-9, characterized in that, This method is implemented through different units, including: a surge current precision control and application unit, which is connected to the silicon carbide MOSFET fixing unit, used to generate a surge current with a preset amplitude, waveform, and duration, and apply the surge current to the silicon carbide MOSFET fixed in the silicon carbide MOSFET fixing unit; a silicon carbide MOSFET multi-parameter integrated acquisition unit, which is connected to both the surge current precision control and application unit and the silicon carbide MOSFET fixing unit, and integrates an infrared thermal imaging acquisition subunit and an electrical parameter acquisition subunit. The infrared thermal imaging acquisition subunit is used to acquire surface temperature distribution data of the silicon carbide MOSFET chip, and the electrical parameter acquisition subunit is used to acquire voltage and current data during the surge current application process; and a multi-physics field collaborative thermal analysis calculation unit, which is connected to the silicon carbide MOSFET multi-parameter integrated acquisition unit, has a built-in multi-physics field collaborative thermal analysis model, and is used to receive the acquired temperature and electrical parameter data, and input the material... After determining the material and structural parameters, multiphysics coupling analysis is performed to output preliminary heat flux distribution data. A differential iterative transient junction temperature solution unit, connected to the multiphysics collaborative thermal analysis calculation unit, stores the thermal capacity and thermal resistance network parameters of the silicon carbide MOSFET. It processes the preliminary heat flux distribution data using a differential iterative algorithm to obtain the transient junction temperature calculation value. An extended dynamic mode decomposition heat flux correction unit, connected to both the silicon carbide MOSFET multi-parameter integrated acquisition unit and the multiphysics collaborative thermal analysis calculation unit, performs dynamic mode decomposition on the temperature distribution data and corrects the preliminary heat flux distribution data by combining chip thickness and thermal diffusivity parameters. A transient temperature rise result integration and output unit, connected to both the differential iterative transient junction temperature solution unit and the extended dynamic mode decomposition heat flux correction unit, receives the corrected heat flux data and the transient junction temperature calculation value, updates the iterative calculation to obtain the final transient temperature rise result, and outputs it to an external storage or display device via a data interface.

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