SiC MOS dynamic characteristic prediction method and system and medium

Abstract

The invention discloses a SiC MOS dynamic characteristic prediction method and system and a medium, and relates to the technical field of characteristic prediction, and the method comprises the steps: collecting the multi-dimensional characteristic information of a SiC MOS device, and constructing a SiC MOS coupling model based on the multi-dimensional characteristic information; obtaining a simulation record through the SiC MOS coupling model, and introducing a predetermined verification strategy to verify and analyze the simulation record to obtain a simulation verification result; and when the simulation verification result meets the preset simulation constraint, calling the SiC MOS coupling model to simulate to obtain the predicted dynamic characteristics of the SiC MOS device. According to the method, the technical problem that the dynamic characteristics of the SiC MOS device cannot be effectively predicted before switching in the prior art is solved, and the technical effect of accurately predicting the dynamic characteristics is achieved by constructing the coupling model and performing simulation verification analysis.

Description

Technical Field

[0001] The present invention relates to the technical field of characteristic prediction, and in particular to a method, system and medium for predicting the dynamic characteristics of SiC MOS. Background Art

[0002] SiC MOS devices offer excellent performance in high-speed, high-frequency applications, and their dynamic characteristics directly impact system efficiency and device reliability. In practical applications, the switching process is influenced by multiple factors, including drive parameters, device structure, and operating environment, resulting in complex and variable dynamic behavior. Due to the lack of accurate pre-switching prediction methods, drive parameter tuning often relies on empirical experience, making it difficult to adjust to varying operating conditions. This can lead to increased switching losses, unstable device operation, and even shortened device lifespan. Summary of the Invention

[0003] The present application provides a SiC MOS dynamic characteristics prediction method, system, and medium, which are used to solve the technical problem that the existing technology cannot effectively predict the dynamic characteristics of SiC MOS devices before switching.

[0004] In view of the above problems, the present application provides a method, system and medium for predicting the dynamic characteristics of SiC MOS.

[0005] In a first aspect of the present application, a method for predicting dynamic characteristics of SiC MOS is provided, the method comprising: The multi-dimensional characteristic information of the SiC MOS device is collected and a SiC MOS coupling model is constructed based on the multi-dimensional characteristic information; a simulation record is obtained through the SiC MOS coupling model, and a predetermined verification strategy is introduced to verify and analyze the simulation record to obtain a simulation verification result; when the simulation verification result meets the predetermined simulation constraint, the SiCMOS coupling model is called to simulate and obtain the predicted dynamic characteristics of the SiC MOS device; wherein the simulation record includes gate charge simulation data, switching characteristic simulation data and reverse recovery simulation data, and the SiC A MOS coupling model obtains a simulation record, and a predetermined verification strategy is introduced to verify and analyze the simulation record to obtain a simulation verification result, including: extracting a static verification plan in the predetermined verification strategy; measuring and obtaining static verification information according to the static verification plan, wherein the static verification information includes multiple groups of charging characteristics with temperature identifiers, and each group of charging characteristics with temperature identifiers includes transfer characteristics and output characteristics; extracting a dynamic verification plan in the predetermined verification strategy; measuring and obtaining dynamic verification information according to the dynamic verification plan, wherein the dynamic verification information includes switching characteristics and reverse recovery characteristics; analyzing the transfer characteristics and the output characteristics, the switching characteristics and the reverse recovery characteristics to obtain the simulation verification result.

[0006] In one possible implementation, multidimensional feature information of a SiC MOS device is collected and a SiC MOS coupling model is constructed based on the multidimensional feature information, including: constructing a structural parameter model based on geometric parameters in the multidimensional feature information; constructing a gate oxide layer characteristic model based on oxide layer traps in the multidimensional feature information; and constructing a temperature-dependent model based on temperature characteristic parameters in the multidimensional feature information; wherein the geometric parameters include trench depth, trench width, and trench spacing, the oxide layer traps include interface electron defects and internal electron traps in the oxide layer, and the temperature characteristic parameters include mobility temperature coefficient, carrier lifetime temperature coefficient, and saturation velocity temperature coefficient; and the SiC MOS coupling model is obtained by collaboratively combining the structural parameter model, the gate oxide layer characteristic model, and the temperature-dependent model.

[0007] In one possible implementation, after analyzing the transfer characteristics and the output characteristics, the switching characteristics and the reverse recovery characteristics to obtain the simulation verification results, it also includes: extracting the temperature verification plan in the predetermined verification strategy; according to the temperature verification plan, obtaining an arbitrary temperature, and matching the arbitrary charging characteristics corresponding to the arbitrary temperature in the multiple groups of charging characteristics with temperature identifications; matching the arbitrary charging simulation data corresponding to the arbitrary temperature in the gate charging simulation data; comparing the arbitrary charging characteristics with the arbitrary charging simulation data to obtain a comparison result; taking the mean of the comparison results and performing normalization processing to obtain a temperature characteristic coefficient; and calibrating and adjusting the simulation verification results using the temperature characteristic coefficient as a weight.

[0008] In one possible implementation, the arbitrary charging characteristic is compared with the arbitrary charging simulation data to obtain a comparison result, including: comparing any transfer characteristic in the arbitrary charging characteristic with any simulated transfer characteristic in the arbitrary charging simulation data, and obtaining an arbitrary transfer characteristic deviation; comparing any output characteristic in the arbitrary charging characteristic with any simulated output characteristic in the arbitrary charging simulation data, and obtaining an arbitrary output characteristic deviation; performing weighted calculation on the standardized arbitrary transfer characteristic deviation and the arbitrary output characteristic deviation to obtain an arbitrary deviation index; and using the arbitrary deviation index as the comparison result.

[0009] In one possible implementation, when the simulation verification result satisfies predetermined simulation constraints, the SiC MOS coupling model is called to simulate and obtain predicted dynamic characteristics of the SiC MOS device, including: collecting operating condition information of the SiC MOS device; using the operating condition information as a simulation constraint, simulating and obtaining the predicted dynamic characteristics through the SiC MOS coupling model; wherein the operating condition information includes electrical conditions, environmental conditions, and circuit topology conditions; the electrical conditions include a gate drive voltage range, an operating frequency range, and a load current range; the environmental conditions include an operating temperature range, heat dissipation conditions, and ambient humidity; and the circuit topology conditions include an application circuit topology, parasitic parameters, and drive circuit parameters.

[0010] In one possible implementation, the method further includes: performing a timing analysis on the predicted dynamic characteristics to obtain a predicted timing; performing a polynomial regression fitting analysis on the predicted timing to obtain a prediction fitting formula; reading a predetermined dynamic characteristic threshold, and combining the prediction fitting formula to obtain a dynamic characteristic lifetime of the SiC MOS device, recorded as a predicted lifetime; wherein the predicted lifetime is used to represent a predicted service life value of the SiC MOS device.

[0011] In one possible implementation, a polynomial regression fitting analysis is performed on the prediction time series to obtain a prediction fitting formula, including: obtaining the prediction time corresponding to the prediction dynamic characteristics; forming the prediction time series according to the correspondence between the prediction dynamic characteristics and the prediction time, and generating a prediction scatter plot; generating a prediction curve of the prediction scatter plot based on the principle of random sampling consistency, and performing a polynomial regression fitting analysis on the prediction curve to obtain the prediction fitting formula.

[0012] A second aspect of the present application provides a SiC MOS dynamic characteristics prediction system, the system comprising: An information collection module is configured to collect multidimensional feature information of the SiC MOS device and construct a SiC MOS coupling model based on the multidimensional feature information. A verification and analysis module is configured to obtain simulation records using the SiC MOS coupling model and perform verification and analysis on the simulation records using a predetermined verification strategy to obtain simulation verification results. A prediction module is configured to, when the simulation verification results satisfy predetermined simulation constraints, invoke the SiC MOS coupling model to simulate and obtain predicted dynamic characteristics of the SiC MOS device.

[0013] According to a third aspect of the present application, a computer-readable storage medium is provided, which stores a computer program. When the program is executed by a processor, the method for predicting the dynamic characteristics of SiC MOS provided in the present application is implemented.

[0014] One or more technical solutions provided in this application have at least the following technical effects or advantages: This application collects multidimensional characteristic information of SiC MOS devices and constructs a SiCMOS coupling model based on this multidimensional characteristic information. Simulation records are obtained using the SiC MOS coupling model, and a predetermined verification strategy is introduced to verify and analyze the simulation records to obtain simulation verification results. When the simulation verification results meet predetermined simulation constraints, the SiC MOS coupling model is used to simulate and obtain the predicted dynamic characteristics of the SiC MOS device. This invention solves the technical problem of the existing art's inability to effectively predict the dynamic characteristics of SiC MOS devices before switching. By constructing a coupling model and performing simulation verification analysis, it achieves the technical effect of accurately predicting dynamic characteristics. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0016] Figure 1 A schematic flow chart of a method for predicting the dynamic characteristics of SiC MOS provided in an embodiment of the present application; Figure 2 Schematic diagram of the structure of the SiC MOS dynamic characteristics prediction system provided in an embodiment of the present application.

[0017] Description of the accompanying drawings: information collection module 11, verification and analysis module 12, prediction module 13. DETAILED DESCRIPTION

[0018] This application provides a SiC MOS dynamic characteristics prediction method, system, and medium to address the technical problem that existing technologies cannot effectively predict the dynamic characteristics of SiC MOS devices before switching. By constructing a coupling model and performing simulation verification and analysis, the application achieves the technical effect of accurately predicting dynamic characteristics.

[0019] The following will be combined with the accompanying drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only some of the embodiments of this application, not all of them. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0020] It should be noted that any variations of the terms "include" and "have" are intended to cover non-exclusive inclusions. For example, a process, method, system, product or server that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or modules that are not clearly listed or are inherent to these processes, methods, products or devices.

[0021] Example 1, as Figure 1 As shown, the present application provides a method for predicting dynamic characteristics of SiC MOS, the method comprising: Step S100: collecting multi-dimensional feature information of SiC MOS devices, and constructing a SiC MOS coupling model based on the multi-dimensional feature information.

[0022] In the embodiments of the present application, multidimensional characteristic information of SiC MOS devices is retrieved from a preset database. This information is pre-prepared based on a large number of device measurements and simulation results, covering the core physical parameters that affect the device's dynamic behavior. The multidimensional characteristic information includes geometric parameters reflecting the device geometry, such as trench depth, trench width, and trench spacing; oxide layer trap data describing gate oxide characteristics and defect distribution, including interface electron defects and internal electron traps in the oxide layer; and temperature characteristic parameters related to temperature response, such as mobility temperature coefficient, carrier lifetime temperature coefficient, and saturation velocity temperature coefficient.

[0023] Based on the above multi-dimensional feature information, the structural parameter model, gate oxide layer characteristic model and temperature correlation model are constructed respectively, and the three are integrated into a unified SiC MOS coupling model through collaborative modeling.

[0024] Furthermore, in the method provided in the embodiment of the application, multi-dimensional feature information of the SiC MOS device is collected and a SiC MOS coupling model is constructed based on the multi-dimensional feature information, further comprising: A structural parameter model is constructed based on the geometric parameters in the multidimensional feature information; a gate oxide layer characteristic model is constructed based on the oxide layer traps in the multidimensional feature information; and a temperature-dependent model is constructed based on the temperature characteristic parameters in the multidimensional feature information; wherein the geometric parameters include trench depth, trench width, and trench spacing, the oxide layer traps include interface electron defects and internal electron traps in the oxide layer, and the temperature characteristic parameters include mobility temperature coefficient, carrier lifetime temperature coefficient, and saturation velocity temperature coefficient; the SiC MOS coupling model is obtained by coordinating the structural parameter model, the gate oxide layer characteristic model, and the temperature-dependent model.

[0025] In the embodiment of the present application, geometric parameter data, including groove depth, groove width and groove spacing, are first extracted from a preset database. The groove depth represents the longitudinal dimension of the groove structure in the device, which affects the electric field distribution and the length of the conductive channel. The groove width refers to the physical width of the groove in the transverse direction, which determines the effective area of ​​the carrier channel; the groove spacing reflects the distance between adjacent grooves, which affects the overall layout density and capacitance characteristics of the device. Based on these geometric data, a numerical modeling method is used to construct a structural model of the device, and a three-dimensional grid and boundary conditions that match the actual structure are generated through a simulation platform, thereby obtaining a device structural parameter model that can be used for electric field simulation.

[0026] Subsequently, data related to oxide layer traps was retrieved, including interface electron defects and internal electron traps in the oxide layer. Interface electron defects describe unstable charge states at the interface between the gate oxide layer and the semiconductor material, which can affect the controllability and stability of the device. Internal electron traps refer to localized charge trapping centers within the oxide material, which affect the leakage current and long-term reliability of the device. By introducing this defect data, a physical model reflecting defect behavior was constructed to simulate the impact of defects on the device's electrical characteristics, especially threshold changes. Ultimately, a gate oxide characteristic model describing the device's defect response characteristics was obtained.

[0027] Next, the temperature characteristic parameters are obtained, including the mobility temperature coefficient, carrier lifetime temperature coefficient, and saturation velocity temperature coefficient. The mobility temperature coefficient reflects the rate at which carrier mobility decreases in a material as temperature increases; the carrier lifetime temperature coefficient represents the change in the average lifetime of carriers at different temperatures and is used to assess the effect of temperature on carrier recombination behavior; and the saturation velocity temperature coefficient indicates the degree of temperature influence when carriers reach their maximum drift velocity under high electric field conditions. Based on these parameters, a temperature response model is constructed to characterize the device's conductivity and response time at different temperatures, ultimately forming a temperature-dependent model.

[0028] Finally, the structural parameter model, gate oxide layer characteristic model and temperature-related model are synergistically integrated, and a physical coupling relationship is established through a unified modeling platform. This allows the models to influence each other during the simulation process, and obtains a SiC MOS coupling model that comprehensively considers structural, electric field, defect and temperature factors.

[0029] Step S200: obtaining a simulation record through the SiC MOS coupling model, and introducing a predetermined verification strategy to perform verification analysis on the simulation record to obtain a simulation verification result.

[0030] In an embodiment of the present application, a simulation record is obtained under pre-set simulation conditions through a SiC MOS coupling model. The simulation record includes gate charging simulation data, switching characteristic simulation data, and reverse recovery simulation data. In order to verify the accuracy of the simulation record, a predetermined verification strategy is introduced to verify and analyze it. The verification strategy includes a static verification plan and a dynamic verification plan. The static verification plan is used to obtain multiple groups of charging characteristics with temperature identification, each group of charging characteristics includes transfer characteristics and output characteristics. The dynamic verification plan is used to obtain switching characteristics and reverse recovery characteristics. Finally, by analyzing the transfer characteristics, output characteristics, switching characteristics, and reverse recovery characteristics, the simulation data is compared with the actual measurement results to obtain the simulation verification results.

[0031] Furthermore, in the method provided in the embodiment of the application, the simulation record includes gate charging simulation data, switching characteristic simulation data, and reverse recovery simulation data. The simulation record is obtained by using the SiC MOS coupling model, and a predetermined verification strategy is introduced to verify and analyze the simulation record to obtain a simulation verification result. The method also includes: Extract the static verification plan in the predetermined verification strategy; measure and obtain static verification information according to the static verification plan, wherein the static verification information includes multiple groups of charging characteristics with temperature identifications, and each group of charging characteristics with temperature identifications includes transfer characteristics and output characteristics; extract the dynamic verification plan in the predetermined verification strategy; measure and obtain dynamic verification information according to the dynamic verification plan, wherein the dynamic verification information includes switching characteristics and reverse recovery characteristics; analyze the transfer characteristics and the output characteristics, the switching characteristics and the reverse recovery characteristics to obtain the simulation verification result.

[0032] In an embodiment of the present application, after generating gate charging simulation data, switching characteristic simulation data, and reverse recovery simulation data using the SiC MOS coupling model, to verify the accuracy of the simulation records, a static verification plan from a predetermined verification strategy is first extracted. The static verification plan is used to set parameter boundaries during the static simulation process, including temperature point settings, voltage scanning methods, and methods for extracting output variables. According to the static verification plan, multiple simulation temperature points are set in the SiC MOS coupling model, and static verification information is measured by performing gate-source voltage scans and drain-source voltage scans respectively. The static verification information is a data set simulated and output by the SiC MOS coupling model at each temperature point, including multiple sets of charging characteristics with temperature labels, each set of charging characteristics with temperature labels corresponding to a specific temperature value, and each set of charging characteristics with temperature labels includes transfer characteristics and output characteristics. The transfer characteristics are the drain current response curve caused by changes in gate-source voltage, which is used to reflect the device's turn-on threshold and gate control efficiency; the output characteristics are the drain current output curve caused by changes in drain-source voltage, which is used to reflect the device's conduction capability and current carrying capacity. Through this step, static verification information including transfer characteristics and output characteristics under multiple temperature conditions is finally obtained.

[0033] Then, the dynamic verification plan in the predetermined verification strategy is extracted. The dynamic verification plan is used to set the simulation parameters such as pulse excitation, load model, topology configuration and time nodes required for time domain simulation under dynamic working conditions. According to the dynamic verification plan, a switching action simulation scenario is constructed in the SiC MOS coupling model, and a driving signal is applied for timing simulation to measure and obtain dynamic verification information. The dynamic verification information is the response data output by the SiC MOS coupling model under dynamic conditions, including switching characteristics and reverse recovery characteristics. The switching characteristics are the time-varying behaviors of the drain voltage and current output by the SiC MOS coupling model during the conduction and shutdown processes, which are used to extract indicators such as turn-on time, turn-off time, voltage rise time, and current fall time; the reverse recovery characteristics are the conduction and cut-off processes of the parasitic diode or body diode after the current direction is reversed, and mainly extract the reverse recovery time and reverse recovery charge. Through this step, dynamic verification information including switching characteristics and reverse recovery characteristics is obtained.

[0034] Finally, the transfer and output characteristics, switching characteristics, and reverse recovery characteristics were analyzed. The characteristic curves in the static and dynamic verification information were compared with the gate charging simulation data, switching characteristic simulation data, and reverse recovery simulation data generated by the SiC MOS coupling model. A key point comparison method was used to extract and compare key physical parameters, such as the threshold voltage in the transfer characteristics, the conduction region slope in the output characteristics, the peak current in the switching characteristics, and the knee voltage in the reverse recovery characteristics. For example, in the transfer characteristics, if the threshold voltage output by the SiC MOS coupling model is 2.5V and the corresponding value in the static verification information is 2.55V, the deviation is 0.05V. If the tolerance range is set to ±0.1V, the result is considered to meet the requirements. In the switching characteristics, if the peak current output by the SiC MOS coupling model is 19.1A and the corresponding value in the dynamic verification information is 19.3A, the deviation is 0.2A, which is also within the ±0.5A tolerance range. This method aligns and evaluates the parameters of various transfer, output, switching, and reverse recovery characteristics, generating simulation verification results. These verification outputs, which include deviations from key physical parameters, demonstrate the accuracy and consistency of the SiCMOS coupling model's predictions of device behavior under various simulation conditions.

[0035] Furthermore, in the method provided in the embodiment of the application, after analyzing the transfer characteristics and the output characteristics, the switching characteristics and the reverse recovery characteristics to obtain the simulation verification results, the method further includes: Extracting a temperature verification plan in the predetermined verification strategy; obtaining an arbitrary temperature according to the temperature verification plan, and matching an arbitrary charging characteristic corresponding to the arbitrary temperature in the multiple groups of charging characteristics with temperature identifiers; matching arbitrary charging simulation data corresponding to the arbitrary temperature in the gate charging simulation data; comparing the arbitrary charging characteristic with the arbitrary charging simulation data to obtain a comparison result; taking the average of the comparison results and performing normalization processing to obtain a temperature characteristic coefficient; and calibrating and adjusting the simulation verification result using the temperature characteristic coefficient as a weight.

[0036] In the embodiment of the present application, a temperature verification plan in a predetermined verification strategy is first extracted, which sets a temperature-related calibration process, including a temperature point extraction method, a data matching method, a difference quantification rule, and a correction process.

[0037] Then, according to the temperature verification plan, an arbitrary temperature is obtained and the arbitrary charging characteristics corresponding to the arbitrary temperature are matched among multiple sets of charging characteristics with temperature identifiers. The arbitrary temperature can be set by the user or determined by the simulation environment, for example, set to 80°C. The characteristic data set closest to the temperature is searched among the multiple sets of charging characteristics with temperature identifiers. If two temperature points exist at 75°C and 85°C, linear interpolation can be performed to generate the transfer characteristics and output characteristics corresponding to 80°C, forming the arbitrary charging characteristics.

[0038] Next, the gate charging simulation data corresponding to any temperature is matched. This matching is based on the temperature index. The simulation results corresponding to 80°C are extracted from the gate charging simulation data of the SiC MOS coupling model, including the numerical output of the transfer characteristics and output characteristics under the simulation conditions, ensuring that the simulation data and verification data are aligned in the temperature dimension.

[0039] The arbitrary charging characteristics are then compared with the arbitrary charging simulation data. First, the transfer characteristics in the arbitrary charging characteristics are compared with the simulated transfer characteristics in the arbitrary charging simulation data to obtain the transfer characteristic deviation. The output characteristics are then processed in the same way to obtain the output characteristic deviation. These two deviation values ​​are normalized and weighted to form a comprehensive value as the comparison result.

[0040] The mean of the comparison results is then taken and normalized to obtain the temperature characteristic coefficient. The purpose of normalization is to convert the deviation value to a standard scale, reflecting its position within the maximum allowable deviation range. For example, if the mean of the comparison results is 0.05 and the maximum acceptable error is set to 0.2, the temperature characteristic coefficient obtained after normalization is 1-(0.05 / 0.2) = 0.75. This temperature characteristic coefficient is a dimensionless value used to quantify the simulation match of the model at that temperature point. The closer it is to 1, the better the simulation effect.

[0041] Finally, the simulation verification results are calibrated using the temperature characteristic coefficient as a weight. By multiplying the deviation in the original simulation verification results by the temperature characteristic coefficient, the overall verification error is corrected for temperature. For example, if the original deviation is ±6% and the temperature characteristic coefficient is 0.75, the corrected deviation is ±4.5%.

[0042] Furthermore, in the method provided in the embodiment of the application, comparing the arbitrary charging characteristics with the arbitrary charging simulation data to obtain a comparison result further includes: Comparing any transfer characteristic in the arbitrary charging characteristic with any simulated transfer characteristic in the arbitrary charging simulation data to obtain any transfer characteristic deviation; comparing any output characteristic in the arbitrary charging characteristic with any simulated output characteristic in the arbitrary charging simulation data to obtain any output characteristic deviation; performing weighted calculation on the standardized arbitrary transfer characteristic deviation and the arbitrary output characteristic deviation to obtain an arbitrary deviation index; and using the arbitrary deviation index as the comparison result.

[0043] In the embodiment of the present application, the arbitrary transfer characteristic in the arbitrary charging characteristic is first compared with the arbitrary simulated transfer characteristic in the arbitrary charging simulation data to obtain the arbitrary transfer characteristic deviation. This step uses the threshold voltage comparison method, that is, the voltage value when the gate voltage starts to turn on in the arbitrary charging characteristic and the arbitrary charging simulation data is extracted respectively, and the difference is calculated as the transfer characteristic deviation. For example, if the threshold voltage in the verification data is 2.5V and the simulation result is 2.2V, then the transfer characteristic deviation is 0.3V.

[0044] Next, the arbitrary output characteristic from the arbitrary charging characteristics is compared with the arbitrary simulated output characteristic from the arbitrary charging simulation data to obtain the arbitrary output characteristic deviation. This step uses the saturation current deviation method, which extracts the maximum drain current value of the two sets of output characteristic curves when the drain voltage tends to be stable and calculates the difference between them as the output characteristic deviation. For example, if the measured maximum drain current is 18A and the simulation result is 16.5A, the output characteristic deviation is 1.5A.

[0045] Next, any transfer characteristic deviations and any output characteristic deviations are normalized. This step uses the normalized maximum method, dividing each deviation by its set maximum allowable error. For example, if the transfer characteristic tolerance is 0.5V and the output characteristic tolerance is 3A, these deviations are normalized to 0.6 and 0.5, respectively. This step ultimately yields the standardized transfer characteristic deviations and output characteristic deviations, which are used to unify dimensions and scales.

[0046] Subsequently, the normalized arbitrary transfer characteristic deviation and arbitrary output characteristic deviation are weighted to obtain the arbitrary deviation index. This step uses a fixed-weight method. For example, setting the transfer characteristic weight to 0.6 and the output characteristic weight to 0.4, and calculating the weighted sum using a linear weighting method, the result is 0.6 × 0.6 + 0.4 × 0.5 = 0.56. This step ultimately results in an arbitrary deviation index that reflects the comprehensive difference between the two characteristics.

[0047] Finally, the arbitrary deviation index is used as the comparison result.

[0048] Step S300: When the simulation verification result satisfies a predetermined simulation constraint, the SiC MOS coupling model is retrieved to simulate and obtain predicted dynamic characteristics of the SiC MOS device.

[0049] In an embodiment of the present application, before making a prediction, the simulation verification results are first judged according to predetermined simulation constraints. The predetermined simulation constraints are a set of criteria used to determine whether the model accuracy meets the requirements, including indicators such as the static verification deviation threshold, the dynamic verification deviation threshold, and the lower limit of the temperature characteristic coefficient. When the static verification deviation and the dynamic verification deviation in the simulation verification results are both lower than the corresponding set thresholds, and the temperature characteristic coefficient is higher than the set reliability lower limit, the predetermined simulation constraints are determined to be met.

[0050] After the above criteria are met, the SiC MOS coupling model is retrieved for predictive simulation. This process first collects information about the SiC MOS device's operating conditions as input constraints for the model simulation. This operating condition information includes electrical conditions, environmental conditions, and circuit topology conditions. Electrical conditions cover the gate drive voltage range, operating frequency range, and load current range; environmental conditions include the operating temperature range, heat dissipation conditions, and ambient humidity; and circuit topology conditions include the application circuit topology, parasitic parameters, and drive circuit parameters.

[0051] After inputting the aforementioned operating condition information into the SiC MOS coupling model, a co-simulation is performed based on the integrated structural parameter model, gate oxide characteristic model, and temperature-dependent model. The final output is the predicted dynamic characteristics of the SiC MOS device under the target operating conditions. This predicted dynamic characteristics includes gate charging simulation data, switching characteristic simulation data, and reverse recovery simulation data, which reflect the dynamic response behavior of the device in a specific application scenario.

[0052] Furthermore, in the method provided in the embodiment of the application, when the simulation verification result satisfies the predetermined simulation constraint, the SiC MOS coupling model is called to simulate and obtain the predicted dynamic characteristics of the SiC MOS device, further comprising: Working condition information of the SiC MOS device is collected; and the predicted dynamic characteristics are obtained by simulating the SiC MOS coupling model using the working condition information as a simulation constraint; wherein the working condition information includes electrical conditions, environmental conditions, and circuit topology conditions; the electrical conditions include a gate drive voltage range, an operating frequency range, and a load current range; the environmental conditions include an operating temperature range, heat dissipation conditions, and ambient humidity; and the circuit topology conditions include an application circuit topology, parasitic parameters, and drive circuit parameters.

[0053] In an embodiment of the present application, after the SiC MOS coupling model is verified and meets predetermined simulation constraints, operating condition information of the SiC MOS device is collected to enable simulation calculations to predict dynamic characteristics. This operating condition information is a set of pre-defined parameters configured based on typical application scenarios, device design specifications, or system-level requirements. This information is directly derived from design specification documents, reference circuit manuals, or simulation templates. The operating condition information includes three types of parameters: electrical conditions, environmental conditions, and circuit topology conditions. The electrical conditions are set as a set of typical electrical excitation parameters, such as a gate drive voltage range of +15V to +20V, an operating frequency range of 20kHz to 250kHz, and a load current range of 5A to 100A, to cover response conditions under different workloads and switching frequencies. Environmental conditions include an operating temperature range (e.g., –40°C to 150°C), heat dissipation conditions (e.g., natural convection, air cooling, or liquid cooling), and ambient humidity (e.g., 85% RH industrial damp heat conditions) to simulate changes in device electrical properties under thermal and climatic stress. The circuit topology conditions are determined by the preset application circuit architecture, such as half-bridge, full-bridge, synchronous buck, or three-phase inverter topology. They also include the parasitic inductance (such as 10nH), parasitic capacitance (such as 100pF), and drive circuit parameters (such as rise time 50ns, drive impedance 5Ω, etc.) set in the simulation to ensure that the modeling environment is close to the actual application system.

[0054] The preset operating conditions are input as simulation constraints into the SiC MOS coupling model. The model then simulates and solves the problem using the integrated structural parameter model, gate oxide characteristic model, and temperature-dependent model. The final output predicts dynamic characteristics, including gate charging simulation data, switching characteristic simulation data, and reverse recovery simulation data. The gate charging simulation data reflects the gate voltage-charge response. The switching characteristic simulation data reflects the current and voltage changes during the device's turn-on and turn-off processes. The reverse recovery simulation data demonstrates the recovery behavior of the device's internal body diode or freewheeling path when the current direction reverses.

[0055] Furthermore, the method provided in the application embodiment also includes: Performing a timing analysis on the predicted dynamic characteristics to obtain a predicted timing; performing a polynomial regression fitting analysis on the predicted timing to obtain a prediction fitting formula; reading a predetermined dynamic characteristic threshold, and combining the prediction fitting formula to obtain a dynamic characteristic lifetime of the SiC MOS device, recorded as a predicted lifetime; wherein the predicted lifetime is used to represent a predicted service life value of the SiC MOS device.

[0056] In the embodiments of the present application, a time series analysis method is first used to analyze the timing of the predicted dynamic characteristics. This method is based on the gate charging simulation data, switching characteristic simulation data, and reverse recovery simulation data output by the simulation. Through signal extraction and key node identification operations, the time domain characteristic values ​​of each dynamic behavior are obtained. For example, the turn-on delay time is extracted from the rising edge of the gate-source voltage, the turn-off time is extracted from the falling segment of the drain current, and the reverse recovery time and recovery current peak are extracted from the reverse current waveform. Each data point corresponds to a clear timestamp, forming a complete response curve, and ultimately generating a predicted time series with a time series structure.

[0057] Next, we perform a polynomial regression fit analysis on the predicted time series. This process first obtains the predicted time corresponding to the predicted dynamic characteristics and establishes a corresponding relationship between it and the response data to form a predicted time series. This is then used to generate a prediction scatter plot. Then, based on the principle of random sampling consistency, a prediction curve is constructed. This curve is then used for polynomial regression fit analysis to obtain the prediction fitting formula.

[0058] The predetermined dynamic characteristic thresholds are then read. These thresholds represent performance boundary parameters set for evaluating device reliability, such as the maximum allowable turn-on delay, maximum reverse recovery current, and maximum gate charge. The predetermined dynamic characteristic thresholds are substituted into the prediction fitting formula one by one, and the reverse solution is used to determine the time point or number of cycles at which the output value of the prediction fitting formula first reaches or exceeds the corresponding threshold. This solution is implemented using numerical methods, such as using an iterative method to gradually approximate the point where the output of the prediction fitting formula equals the threshold. The resulting time point represents the expected operating time for the dynamic characteristic to reach the preset performance upper limit.

[0059] Ultimately, this time point or number of cycles is defined as the dynamic lifetime of the SiC MOS device, recorded as the predicted lifetime. The predicted lifetime is a numerical result, measured in hours or cycles, and represents the device's usable lifetime, maintaining stable performance under current operating conditions.

[0060] Furthermore, in the method provided in the embodiment of the application, a polynomial regression fitting analysis is performed on the predicted time series to obtain a prediction fitting formula, which also includes: Obtain the prediction time corresponding to the prediction dynamic characteristics; form the prediction time series according to the corresponding relationship between the prediction dynamic characteristics and the prediction time, and generate a prediction scatter plot; generate a prediction curve of the prediction scatter plot based on the principle of random sampling consistency, and perform polynomial regression fitting analysis on the prediction curve to obtain the prediction fitting formula.

[0061] In the embodiments of the present application, the predicted time corresponding to the predicted dynamic characteristics is first obtained. The predicted dynamic characteristics include gate charging simulation data, switching characteristics simulation data, and reverse recovery simulation data, all of which are generated by simulating the SiC MOS coupling model, and each data point is accompanied by a time tag. By reading the time coordinates corresponding to key points with characteristic significance (such as peaks, inflection points, zero crossings, etc.) in these simulation data, the predicted time reflecting the change in dynamic behavior is extracted, thereby obtaining the predicted time corresponding to the predicted dynamic characteristics.

[0062] Subsequently, a predicted time series is generated based on the correspondence between the predicted dynamic characteristics and the predicted time. This time series consists of multiple data pairs with time as the independent variable and response value as the dependent variable, covering the entire dynamic behavior process. Based on this, a two-dimensional prediction scatter plot is drawn with the predicted time as the horizontal axis and the response value as the vertical axis. This intuitively displays the changing trajectory of dynamic parameters such as turn-on delay time, turn-off time, and reverse recovery time during the simulation process. This prediction scatter plot provides the raw data foundation for subsequent characteristic trend modeling.

[0063] After obtaining the prediction scatter plot, the data is screened and the prediction curve is extracted based on the principle of random sampling consistency. Specifically, three non-collinear data points are randomly selected from the prediction scatter plot, and an initial curve model is generated by fitting. Subsequently, the model is used as a benchmark to calculate the vertical distance from all data points in the graph to the curve, and an error threshold is set. For example, the point with an error less than 1% of the maximum response value is judged as an "inlier". The number of inliers that meet this condition is counted. When the number exceeds 80% of the total number of points, the model is considered valid, and the inlier set and its corresponding model are recorded. The process is repeated 300 times, and each time the group of data points with the most error inliers is retained as the final prediction curve point set, thereby generating a prediction curve reflecting the main trend of the dynamic characteristics.

[0064] Finally, a polynomial regression fitting analysis is performed on the above prediction curve. Using the least squares method, the extracted prediction curve data points are numerically processed. A third-order or fourth-order polynomial model is selected as the fitting function form. Based on the principle of minimizing the sum of squared residuals, a continuous and differentiable mathematical expression is fitted to form the prediction fitting formula.

[0065] In the embodiments of the present application, in summary, the embodiments of the present application have at least the following technical effects: This application collects multidimensional characteristic information of SiC MOS devices and constructs a SiCMOS coupling model based on this multidimensional characteristic information. Simulation records are obtained using the SiC MOS coupling model, and a predetermined verification strategy is introduced to verify and analyze the simulation records to obtain simulation verification results. When the simulation verification results meet predetermined simulation constraints, the SiC MOS coupling model is used to simulate and obtain the predicted dynamic characteristics of the SiC MOS device. This invention solves the technical problem of the existing art's inability to effectively predict the dynamic characteristics of SiC MOS devices before switching. By constructing a coupling model and performing simulation verification analysis, it achieves the technical effect of accurately predicting dynamic characteristics.

[0066] Embodiment 2 is based on the same inventive concept as the SiC MOS dynamic characteristic prediction method in the above embodiment. Figure 2 As shown, the present application provides a SiC MOS dynamic characteristics prediction system. The system and method embodiments in the present application are based on the same inventive concept. The system includes: An information collection module 11 is configured to collect multidimensional feature information of a SiC MOS device and construct a SiC MOS coupling model based on the multidimensional feature information. A verification and analysis module 12 is configured to obtain simulation records using the SiC MOS coupling model and perform verification and analysis on the simulation records using a predetermined verification strategy to obtain simulation verification results. A prediction module 13 is configured to, when the simulation verification results satisfy predetermined simulation constraints, invoke the SiC MOS coupling model to simulate and obtain predicted dynamic characteristics of the SiC MOS device.

[0067] Furthermore, the system is also used to implement the following functions: Constructing a structural parameter model according to the geometric parameters in the multidimensional feature information; Constructing a gate oxide layer characteristic model according to the oxide layer traps in the multi-dimensional feature information; Constructing a temperature-related model based on the temperature characteristic parameters in the multidimensional feature information; The geometric parameters include trench depth, trench width and trench spacing, the oxide layer traps include interface electron defects and internal electron traps in the oxide layer, and the temperature characteristic parameters include mobility temperature coefficient, carrier lifetime temperature coefficient and saturation velocity temperature coefficient; The SiC MOS coupling model is obtained by coordinating the structural parameter model, the gate oxide layer characteristic model and the temperature dependency model.

[0068] Furthermore, the system is also used to implement the following functions: Extracting a static verification plan from the predetermined verification strategy; According to the static verification plan, static verification information is measured and obtained, wherein the static verification information includes multiple sets of charging characteristics with temperature identifiers, and each set of charging characteristics with temperature identifiers includes a transfer characteristic and an output characteristic; Extracting a dynamic verification plan from the predetermined verification strategy; According to the dynamic verification plan, dynamic verification information is measured and obtained, wherein the dynamic verification information includes switching characteristics and reverse recovery characteristics; The transfer characteristic and the output characteristic, the switching characteristic and the reverse recovery characteristic are analyzed to obtain the simulation verification result.

[0069] Furthermore, the system is also used to implement the following functions: Extracting a temperature verification plan from the predetermined verification strategy; According to the temperature verification plan, obtaining an arbitrary temperature, and matching an arbitrary charging characteristic corresponding to the arbitrary temperature in the multiple sets of charging characteristics with temperature identifiers; Matching any charging simulation data corresponding to the arbitrary temperature in the gate charging simulation data; comparing the arbitrary charging characteristics with the arbitrary charging simulation data to obtain a comparison result; Taking the average of the comparison results and performing normalization processing to obtain the temperature characteristic coefficient; The simulation verification result is calibrated and adjusted using the temperature characteristic coefficient as a weight.

[0070] Furthermore, the system is also used to implement the following functions: comparing any transfer characteristic in the arbitrary charging characteristic with any simulated transfer characteristic in the arbitrary charging simulation data, and determining a deviation of the arbitrary transfer characteristic; comparing any output characteristic in the arbitrary charging characteristic with any simulated output characteristic in the arbitrary charging simulation data, and determining a deviation of the arbitrary output characteristic; performing weighted calculation on the normalized arbitrary transfer characteristic deviation and the arbitrary output characteristic deviation to obtain an arbitrary deviation index; The arbitrary deviation index is used as the comparison result.

[0071] Furthermore, the system is also used to implement the following functions: Collecting operating condition information of the SiC MOS device; Using the working condition information as a simulation constraint, simulating the SiC MOS coupling model to obtain the predicted dynamic characteristics; Among them, the working condition information includes electrical conditions, environmental conditions and circuit topology conditions; the electrical conditions include gate drive voltage range, operating frequency range and load current range, the environmental conditions include operating temperature range, heat dissipation conditions and ambient humidity, and the circuit topology conditions include application circuit topology, parasitic parameters and drive circuit parameters.

[0072] Furthermore, the system is also used to implement the following functions: Performing a time series analysis on the predicted dynamic characteristics to obtain a predicted time series; Performing a polynomial regression fitting analysis on the predicted time series to obtain a prediction fitting formula; Reading a predetermined dynamic characteristic threshold value, and combining it with the prediction fitting formula to obtain the dynamic characteristic lifetime of the SiC MOS device, which is recorded as the predicted lifetime; The predicted lifetime is used to characterize a predicted service life value of the SiC MOS device.

[0073] Furthermore, the system is also used to implement the following functions: Obtaining a prediction time corresponding to the predicted dynamic characteristic; Forming the prediction time series according to the corresponding relationship between the prediction dynamic characteristics and the prediction time, and generating a prediction scatter plot; A prediction curve of the prediction scatter plot is generated based on the principle of random sampling consistency, and a polynomial regression fitting analysis is performed on the prediction curve to obtain the prediction fitting formula.

[0074] In a third embodiment, based on the same inventive concept as the SiC MOS dynamic characteristic prediction method in the aforementioned embodiment, the present application further provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed, the steps of the method described in any one of the aforementioned embodiments are implemented.

[0075] It should be noted that the order in which the embodiments of the present application are presented is for illustrative purposes only and does not necessarily represent the superiority or inferiority of the embodiments. Furthermore, the foregoing descriptions of specific embodiments of this specification are provided. The processes depicted in the accompanying drawings do not necessarily require the specific order or sequential sequence shown to achieve the desired results. In certain embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0076] The above description is only a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present application shall be included in the scope of protection of the present application.

[0077] This specification and drawings are merely illustrative of the present application and are intended to cover any and all modifications, variations, combinations, or equivalents within the scope of this application. Obviously, those skilled in the art may make various modifications and variations to this application without departing from the scope of this application. Thus, this application is intended to include such modifications and variations as fall within the scope of this application and its equivalents.

Claims

1. A method for predicting SiC MOS dynamic characteristics, characterized in that: include: Collecting multidimensional feature information of the SiC MOS device and constructing a SiC MOS coupling model based on the multidimensional feature information; Obtaining simulation records through the SiC MOS coupling model, and introducing a predetermined verification strategy to perform verification analysis on the simulation records to obtain simulation verification results; When the simulation verification result satisfies the predetermined simulation constraint, calling the SiC MOS coupling model to simulate and obtain the predicted dynamic characteristics of the SiC MOS device; The simulation record includes gate charging simulation data, switching characteristic simulation data, and reverse recovery simulation data. The simulation record is obtained by the SiC MOS coupling model, and a predetermined verification strategy is introduced to verify and analyze the simulation record to obtain a simulation verification result, including: Extracting a static verification plan from the predetermined verification strategy; According to the static verification plan, static verification information is measured and obtained, wherein the static verification information includes multiple sets of charging characteristics with temperature identifiers, and each set of charging characteristics with temperature identifiers includes a transfer characteristic and an output characteristic; Extracting a dynamic verification plan from the predetermined verification strategy; According to the dynamic verification plan, dynamic verification information is measured and obtained, wherein the dynamic verification information includes switching characteristics and reverse recovery characteristics; The transfer characteristic and the output characteristic, the switching characteristic and the reverse recovery characteristic are analyzed to obtain the simulation verification result.

2. The SiC MOS dynamic characteristics prediction method according to claim 1, wherein: Collecting multidimensional feature information of the SiC MOS device and constructing a SiC MOS coupling model based on the multidimensional feature information, including: Constructing a structural parameter model according to the geometric parameters in the multidimensional feature information; Constructing a gate oxide layer characteristic model according to the oxide layer traps in the multi-dimensional feature information; Constructing a temperature-related model based on the temperature characteristic parameters in the multidimensional feature information; The geometric parameters include trench depth, trench width and trench spacing, the oxide layer traps include interface electron defects and internal electron traps in the oxide layer, and the temperature characteristic parameters include mobility temperature coefficient, carrier lifetime temperature coefficient and saturation velocity temperature coefficient; The SiCMOS coupling model is obtained by coordinating the structural parameter model, the gate oxide layer characteristic model and the temperature dependency model.

3. The SiC MOS dynamic characteristics prediction method according to claim 1, wherein: After analyzing the transfer characteristic, the output characteristic, the switching characteristic, and the reverse recovery characteristic to obtain the simulation verification result, the method further includes: Extracting a temperature verification plan from the predetermined verification strategy; According to the temperature verification plan, obtaining an arbitrary temperature, and matching an arbitrary charging characteristic corresponding to the arbitrary temperature in the multiple sets of charging characteristics with temperature identifiers; Matching any charging simulation data corresponding to the arbitrary temperature in the gate charging simulation data; comparing the arbitrary charging characteristics with the arbitrary charging simulation data to obtain a comparison result; Taking the average of the comparison results and performing normalization processing to obtain the temperature characteristic coefficient; The simulation verification result is calibrated and adjusted using the temperature characteristic coefficient as a weight.

4. The SiC MOS dynamic characteristics prediction method according to claim 3, wherein: Comparing the arbitrary charging characteristics with the arbitrary charging simulation data to obtain a comparison result, including: comparing any transfer characteristic in the arbitrary charging characteristic with any simulated transfer characteristic in the arbitrary charging simulation data, and determining a deviation of the arbitrary transfer characteristic; comparing any output characteristic in the arbitrary charging characteristic with any simulated output characteristic in the arbitrary charging simulation data, and determining a deviation of the arbitrary output characteristic; performing weighted calculation on the normalized arbitrary transfer characteristic deviation and the arbitrary output characteristic deviation to obtain an arbitrary deviation index; The arbitrary deviation index is used as the comparison result.

5. The SiC MOS dynamic characteristics prediction method according to claim 1, wherein: When the simulation verification result satisfies the predetermined simulation constraint, calling the SiC MOS coupling model to simulate and obtain predicted dynamic characteristics of the SiC MOS device, including: Collecting operating condition information of the SiC MOS device; Using the working condition information as a simulation constraint, simulating the SiC MOS coupling model to obtain the predicted dynamic characteristics; Among them, the working condition information includes electrical conditions, environmental conditions and circuit topology conditions; the electrical conditions include gate drive voltage range, operating frequency range and load current range, the environmental conditions include operating temperature range, heat dissipation conditions and ambient humidity, and the circuit topology conditions include application circuit topology, parasitic parameters and drive circuit parameters.

6. The SiC MOS dynamic characteristics prediction method according to claim 5, wherein: Also includes: Performing a time series analysis on the predicted dynamic characteristics to obtain a predicted time series; Performing a polynomial regression fitting analysis on the predicted time series to obtain a prediction fitting formula; Reading a predetermined dynamic characteristic threshold value, and combining it with the prediction fitting formula to obtain the dynamic characteristic lifetime of the SiC MOS device, which is recorded as the predicted lifetime; The predicted lifetime is used to characterize a predicted service life value of the SiC MOS device.

7. The SiC MOS dynamic characteristics prediction method according to claim 6, wherein: Performing polynomial regression fitting analysis on the predicted time series to obtain a prediction fitting formula, including: Obtaining a prediction time corresponding to the predicted dynamic characteristic; Forming the prediction time series according to the corresponding relationship between the prediction dynamic characteristics and the prediction time, and generating a prediction scatter plot; A prediction curve of the prediction scatter plot is generated based on the principle of random sampling consistency, and a polynomial regression fitting analysis is performed on the prediction curve to obtain the prediction fitting formula.

8. SiC MOS dynamic characteristics prediction system, characterized by: The system is used to execute the SiC MOS dynamic characteristics prediction method according to any one of claims 1 to 7, and the system includes: An information collection module, configured to collect multi-dimensional feature information of SiC MOS devices and construct a SiC MOS coupling model based on the multi-dimensional feature information; A verification and analysis module, configured to obtain simulation records using the SiC MOS coupling model, and introduce a predetermined verification strategy to perform verification analysis on the simulation records to obtain simulation verification results; A prediction module is configured to, when the simulation verification result satisfies a predetermined simulation constraint, call the SiC MOS coupling model to simulate and obtain predicted dynamic characteristics of the SiC MOS device.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the SiC MOS dynamic characteristic prediction method according to any one of claims 1 to 7 is implemented.

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