Online aging state monitoring method and system for silicon carbide MOSFET (Metal-Oxide-Semiconductor Field Effect Transistor)

Abstract

The invention discloses an online aging state monitoring method and system for a silicon carbide MOSFET, and belongs to the technical field of semiconductor device monitoring, and the method specifically comprises the steps: collecting the working voltage, working current and junction temperature data of the silicon carbide MOSFET in real time, analyzing and processing the data through a data processing unit, and extracting the aging state characteristic parameters of the MOSFET; a dynamic aging state evaluation model fusing multi-source data and time sequence information is adopted to evaluate the aging state of the silicon carbide MOSFET, and a result is output to a display unit; according to the method, the aging state of the silicon carbide MOSFET is monitored, multi-level alarm strategies are set, different levels of alarm signals are triggered according to the aging state, meanwhile, feedback data are collected to optimize the state evaluation model, real-time and accurate monitoring of the aging state of the silicon carbide MOSFET is achieved, and the reliability and stability of a power electronic system are improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of semiconductor device monitoring, and in particular to an online aging state monitoring method and system for silicon carbide MOSFET. Background Art

[0002] Silicon carbide MOSFET has been widely used in the field of power electronics due to its advantages such as high breakdown electric field, high electron mobility, and low on-resistance. For example, electric vehicles, photovoltaic inverters, and industrial motor drives. However, in the long-term working process, silicon carbide MOSFET will be affected by factors such as electrical stress and thermal stress, which will cause its performance to gradually degrade and aging. Aging may cause the threshold voltage drift, on-resistance increase, leakage current increase, and other problems of MOSFET, which seriously affect the reliability and stability of power electronic systems. Therefore, real-time and accurate monitoring of the aging status of silicon carbide MOSFET is of great significance to ensure the normal operation of power electronic systems.

[0003] At present, the monitoring methods for the aging status of silicon carbide MOSFET mainly include offline detection and online detection. Offline detection requires the device to be removed from the circuit and parameter measurement to be performed on specific test equipment. This method is cumbersome to operate and cannot monitor the aging of the device in real time during actual work. Although the online detection method can monitor the device while it is working, most of the existing online monitoring methods have problems such as single monitoring parameters, low accuracy, and complex algorithms, which make it difficult to meet the needs of actual applications.

[0004] For example, a Chinese patent with publication number CN118112386A discloses an online aging status monitoring method for silicon carbide MOSFET, including: constructing a switch characteristic analysis model of silicon carbide MOSFET that comprehensively considers the package parasitic parameters; deriving the drain current increase rate according to the analysis model; defining the degradation precursor parameter according to the drain current increase rate, and monitoring whether the degradation precursor parameter is greater than zero: if so, it is determined that the silicon carbide MOSFET has degraded, otherwise it is determined that the silicon carbide MOSFET is healthy. The beneficial effect of this technical solution is that the online aging status monitoring method for silicon carbide MOSFET provided can comprehensively characterize two different degradation types of gate oxide degradation and bonding wire fatigue with only a single monitoring quantity, reduce the complexity of the monitoring system, and improve the reliability of the status monitoring system.

[0005] The above existing technologies all have the following problems: they only rely on the single-dimensional monitoring quantity of deriving the drain current increase rate and defining the degradation precursor parameters to determine the state of the silicon carbide MOSFET, which can only comprehensively characterize the two degradation types of gate oxide degradation and bonding wire fatigue, and lack comprehensive consideration of multiple factors; the applicable scenarios are relatively narrow, and there is a lack of multi-level warning and intuitive display. Summary of the invention

[0006] In view of the shortcomings of the prior art, the present invention proposes an online aging status monitoring method and system for silicon carbide MOSFET, which collects the operating voltage, operating current and junction temperature data of the silicon carbide MOSFET in real time, analyzes and processes the data through a data processing unit, and extracts the characteristic parameters of the MOSFET aging status; a dynamic aging status assessment model that integrates multi-source data and time series information is used to evaluate the aging status of the silicon carbide MOSFET, and the result is output to a display unit; a multi-level alarm strategy is set to trigger alarm signals of different levels according to the aging status. At the same time, feedback data is collected to optimize the status assessment model, thereby realizing real-time and accurate monitoring of the aging status of the silicon carbide MOSFET and improving the reliability and stability of the power electronic system.

[0007] To achieve the above object, the present invention provides the following technical solutions:

[0008] An online aging state monitoring method for silicon carbide MOSFET, comprising:

[0009] Step S1: When the silicon carbide MOSFET is working normally, the operating voltage, operating current and junction temperature data thereof are collected in real time through the sensor, and the collected data are transmitted to the data processing unit;

[0010] Step S2: the data processing unit analyzes and processes the received data to extract the MOSFET aging state characteristic parameters, which include the threshold voltage change, the on-resistance change rate, the leakage current increment and the junction temperature change rate;

[0011] Step S3: Based on the extracted characteristic parameters, the aging state of the silicon carbide MOSFET is evaluated using a pre-established dynamic aging state evaluation model that integrates multi-source data and time series information;

[0012] Step S4: according to the aging status evaluation result, the aging status information of the silicon carbide MOSFET is output to the display unit for display, and at the same time, a multi-level alarm strategy is set to automatically trigger different levels of alarm signals according to the severity of the aging status, the rate of change and the potential impact on the system;

[0013] Step S5: Collect feedback data from aging status monitoring, and optimize the aging status assessment model based on the feedback data.

[0014] Specifically, the extraction process of the MOSFET aging state characteristic parameters in step S2 includes: sending a specific test pulse signal to the silicon carbide MOSFET through the data processing unit, and measuring the gate-source voltage of the MOSFET using a voltage measurement circuit during the application of the test pulse. and drain-source voltage , at the same time, set the voltage fluctuation threshold ,like , then the corresponding gate-source voltage As the threshold voltage of MOSFET , then, the threshold voltage of the MOSFET The initial threshold voltage of the MOSFET pre-stored in the data processing unit Perform difference calculation to obtain the threshold voltage change .

[0015] Specifically, the process of extracting the characteristic parameters of the MOSFET aging state in step S2 further includes: according to the real-time collected working current and operating voltage Data, calculate the real-time on-resistance of MOSFET and with the initial on-resistance Compared with the above, the on-resistance change rate is obtained. ; Real-time monitoring of MOSFET leakage current , and the leakage current increment is obtained At the same time, according to the collected junction temperature data , calculate the junction temperature change rate ,in, represents the initial leakage current value, Indicates the initial junction temperature pre-stored in the data processing unit.

[0016] Specifically, the specific steps of step S3 include:

[0017] S3.1: Obtain MOSFET aging state characteristic parameter set ,and and use The obtained MOSFET aging state characteristic parameter set is interpolated and supplemented to obtain the MOSFET aging state characteristic parameter after interpolation and supplementation. ,in, represents the mth MOSFET aging state characteristic parameter, m represents the number of data in the MOSFET aging state characteristic parameter set, , , They represent the aging state characteristic parameters of the i-1th, i, and i+1th MOSFETs in X, respectively. , , Respectively represent the time points corresponding to the characteristic parameters of the aging state of the i-1th, i, and i+1th MOSFETs;

[0018] S3.2: Read characteristic parameter data within N sampling periods from the historical data storage area , and the interpolated MOSFET aging state characteristic parameters Add as a new line to , generate a feature parameter data set, and then arrange the data in the feature parameter data set in chronological order to form a feature parameter sequence matrix .

[0019] Specifically, the specific steps of step S3 also include:

[0020] S3.3: Get feature parameter sequence matrix , and Perform vectorization processing to obtain the characteristic parameter vector ;

[0021] S3.4: Load the pre-built recurrent neural network model architecture from the model storage area and perform initialization operations;

[0022] S3.5: Input into the recurrent neural network model architecture to train the recurrent neural network model to obtain a dynamic aging state assessment model;

[0023] S3.6: Real-time The dynamic aging state assessment model is used to infer the aging state of the MOSFET and output an assessment result vector representing the aging state. .

[0024] Specifically, the specific steps of S4 include:

[0025] S4.1: Obtain the output result of the dynamic aging status assessment model, organize the aging status information into a display data structure according to a predetermined format and layout, and transmit the aging status information to the display unit through the established communication connection for real-time update and display;

[0026] S4.2: After the display update is completed, judging whether the alarm condition is met according to the output result of the dynamic aging state evaluation model and the preset alarm thresholds of each level;

[0027] If the dynamic aging status assessment model determines that the MOSFET is in a non-aging state and the aging status information is less than the first-level alarm threshold, the MOSFET state is normal and continues to wait for the next assessment result update;

[0028] If the dynamic aging status assessment model determines that the MOSFET is in a mild aging state, and the aging status information is greater than or equal to the first-level alarm threshold and less than the second-level alarm threshold, the MOSFET status is normal, triggering the first-level alarm, and regular inspection and maintenance are performed;

[0029] If the dynamic aging status assessment model determines that the MOSFET is in a moderate aging state, and the aging status information is greater than or equal to the second-level alarm threshold and less than the third-level alarm threshold, the second-level alarm is triggered, and monitoring measures are taken to adjust the system operating parameters;

[0030] If the dynamic aging status assessment model determines that the MOSFET is in a serious aging state and the aging status information is greater than or equal to the third-level alarm threshold, a third-level alarm is triggered, the equipment is immediately stopped, and the MOSFET is replaced.

[0031] Specifically, the current aging status information of the MOSFET displayed in step S2 includes real-time characteristic parameter values, aging degree levels, and historical aging trend curves.

[0032] An online aging status monitoring system for silicon carbide MOSFET, comprising: a data acquisition module, a data analysis module, an aging status evaluation module, a display and alarm module, and a model optimization module;

[0033] The data acquisition module is used to collect the operating voltage, operating current and junction temperature data of the silicon carbide MOSFET in real time when the silicon carbide MOSFET is working normally;

[0034] The data analysis module is used to process and analyze the received data and extract the aging state characteristic parameters of the MOSFET;

[0035] The aging state evaluation module is used to evaluate the aging state of the silicon carbide MOSFET according to the extracted characteristic parameters;

[0036] The display and alarm module is used to output the aging status evaluation results to the display unit for display, and trigger alarm signals of different levels according to the evaluation results;

[0037] The model optimization module is used to collect feedback data from aging status monitoring and optimize the aging status assessment model based on these data.

[0038] Specifically, the aging state assessment module includes: a model selection unit and a state assessment unit;

[0039] The model selection unit is used to construct and train a dynamic aging state assessment model that integrates multi-source data and time series information;

[0040] The state evaluation unit is used to specifically evaluate the aging state of the MOSFET according to the application result of the model, and obtain a quantitative index of the aging degree.

[0041] An electronic device comprises a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of an online aging status monitoring method for silicon carbide MOSFET when executing the computer program.

[0042] Compared with the prior art, the present invention has the following beneficial effects:

[0043] 1. The present invention proposes an online aging status monitoring method for silicon carbide MOSFET, which realizes accurate evaluation of MOSFET aging status by real-time collection of working voltage, current and junction temperature data, and accurate extraction of aging status characteristic parameters, and then uses a dynamic evaluation model that integrates multi-source data and time series information. It can timely and accurately grasp the health status of the device, reduce the risk of system failure due to device aging, and improve the reliability and stability of the system.

[0044] 2. The present invention proposes an online aging status monitoring method for silicon carbide MOSFET, which intuitively displays the aging status information and sets a multi-level alarm strategy, so that the staff can quickly detect the abnormal aging of the device so as to take corresponding measures in time, such as maintenance and replacement of devices, to reduce potential economic losses; at the same time, the feedback data is used to optimize the evaluation model so that it can continuously adapt to the actual working conditions and aging characteristics changes of the MOSFET, further improving the accuracy and long-term effectiveness of the monitoring, and ensuring that reliable aging status monitoring services can be provided throughout the life cycle of the device. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] Figure 1 A schematic diagram of an online aging status monitoring method for silicon carbide MOSFET according to the present invention;

[0046] Figure 2 This is a principle flow chart of an online aging status monitoring method for silicon carbide MOSFET of the present invention;

[0047] Figure 3 This is an architecture diagram of an online aging status monitoring system for silicon carbide MOSFET according to the present invention. DETAILED DESCRIPTION

[0048] Example 1

[0049] See also Figure 1 and Figure 2 , an embodiment of the present invention provides: an online aging status monitoring method for silicon carbide MOSFET, comprising the following steps:

[0050] Step S1: When the silicon carbide MOSFET is working normally, the operating voltage, operating current and junction temperature data thereof are collected in real time through the sensor, and the collected data are transmitted to the data processing unit;

[0051] Furthermore, the specific steps of step S1 include:

[0052] (1) Perform power-on initialization operations on the sensor and its signal conditioning circuit, including setting the sensor's operating parameters, such as range and sensitivity, initializing the chip, configuring the amplifier gain and filter parameters in the signal conditioning circuit, etc., to ensure that the entire data acquisition system is in normal working condition and ready to collect data;

[0053] (2) Start the sensor to collect data according to the set sampling frequency. For example, if the data is collected every 1 millisecond, the voltage sensor, current sensor and temperature sensor will simultaneously measure the operating voltage, operating current and junction temperature of the MOSFET every 1 millisecond.

[0054] (3) The sensors transmit the collected analog signals to their respective signal conditioning circuits, which obtain digital data after amplification, filtering and ADC conversion.

[0055] (4) The data is transmitted to the data processing unit through the selected data transmission interface. After receiving the data, the data processing unit stores it in a pre-opened data buffer and records the data acquisition timestamp so that the subsequent data processing and analysis can correspond to the specific time point, thereby realizing real-time monitoring of the MOSFET working status;

[0056] (5) The data processing unit uses a parity check method to check the integrity and accuracy of the received data. If the check passes, it means that the data is received normally and enters the next step of the data processing process; if the check fails, a retransmission request is sent to the sensor to request the retransmission of the group of data. At the same time, the data processing unit can count the number of erroneous data. If multiple erroneous data appear consecutively, it means that there is a fault in the data acquisition system and it is necessary to diagnose and eliminate the fault, for example, check whether the sensor connection is loose, whether the signal conditioning circuit is working properly, whether the data transmission line is disturbed, etc., to ensure the reliability and stability of data acquisition. Among them, the parity check method is the existing technical content in this field and is not the inventive solution of this application, so it will not be repeated here.

[0057] Step S2: the data processing unit analyzes and processes the received data to extract the MOSFET aging state characteristic parameters, which include the threshold voltage change, the on-resistance change rate, the leakage current increment and the junction temperature change rate;

[0058] The current aging status information of the MOSFET displayed in step S2 includes real-time characteristic parameter values, aging degree levels, and historical aging trend curves.

[0059] Step S3: Based on the extracted characteristic parameters, the aging state of the silicon carbide MOSFET is evaluated using a pre-established dynamic aging state evaluation model that integrates multi-source data and time series information;

[0060] Step S4: according to the aging status evaluation result, the aging status information of the silicon carbide MOSFET is output to the display unit for display, and at the same time, a multi-level alarm strategy is set to automatically trigger different levels of alarm signals according to the severity of the aging status, the rate of change and the potential impact on the system;

[0061] Step S5: Collect feedback data from aging status monitoring, and optimize the aging status assessment model based on the feedback data.

[0062] Furthermore, the specific steps of step S5 include:

[0063] (1) Establish data collection channels to obtain feedback data related to SiC MOSFET aging status monitoring from multiple aspects, including: actual failure case data, recording detailed information such as the working conditions, characteristic parameter values, and failure type of the MOSFET when the failure occurred; regular offline detection data, such as using professional testing equipment to conduct comprehensive performance tests on the MOSFET to obtain the accurate values ​​of its threshold voltage, on-resistance, leakage current and other parameters at a specific time point; descriptions of abnormal phenomena and operation records fed back by users during actual use, such as abnormal heating and unstable power output during system operation, as well as the approximate working status of the MOSFET at that time;

[0064] (2) Organize and classify the collected feedback data, and archive them preliminarily according to the data type, such as fault data, detection data, user feedback data, time sequence, and correlation with MOSFET aging status, so as to facilitate subsequent analysis and processing;

[0065] (3) using a normalization method to pre-process the feedback data so that it matches the data format and feature distribution used in the training of the dynamic aging state assessment model, wherein the normalization method is a prior art in the art and is not an inventive solution of the present application, and is not described in detail here;

[0066] (4) Use the preprocessed feedback data to evaluate the existing dynamic aging state assessment model to determine the performance and deficiencies of the model in practical applications. The feedback data is divided into two parts: one part is used as test data to directly evaluate the prediction accuracy of the model; the other part is used as analysis data to further explore the source of the model's prediction error and the distribution of feature importance;

[0067] (5) The dynamic aging state assessment model is comprehensively evaluated using accuracy evaluation indicators, and based on the evaluation results, the aging state assessment model is updated and improved using regularization technology. Regularization technology is a prior art in this field and is not an inventive solution of this application, and is not described in detail here.

[0068] The process of extracting the characteristic parameters of the MOSFET aging state in step S2 includes: sending a specific test pulse signal to the silicon carbide MOSFET through the data processing unit, and measuring the gate-source voltage of the MOSFET using a voltage measurement circuit during the application of the test pulse. and drain-source voltage , at the same time, set the voltage fluctuation threshold ,like , then the corresponding gate-source voltage As the threshold voltage of MOSFET , then, the threshold voltage of the MOSFET The initial threshold voltage of the MOSFET pre-stored in the data processing unit Perform difference calculation to obtain the threshold voltage change ;

[0069] According to the real-time collected working current and operating voltage Data, calculate the real-time on-resistance of MOSFET and with the initial on-resistance Compared with the above, the on-resistance change rate is obtained. ; Real-time monitoring of MOSFET leakage current , and the leakage current increment is obtained At the same time, according to the collected junction temperature data , calculate the junction temperature change rate ,in, represents the initial leakage current value, Indicates the initial junction temperature pre-stored in the data processing unit.

[0070] The specific steps of step S3 include:

[0071] S3.1: Obtain MOSFET aging state characteristic parameter set ,and and use The obtained MOSFET aging state characteristic parameter set is interpolated and supplemented to obtain the MOSFET aging state characteristic parameter after interpolation and supplementation. ,in, represents the mth MOSFET aging state characteristic parameter, m represents the number of data in the MOSFET aging state characteristic parameter set, , , They represent the aging state characteristic parameters of the i-1th, i, and i+1th MOSFETs in X, respectively. , , Respectively represent the time points corresponding to the characteristic parameters of the aging state of the i-1th, i, and i+1th MOSFETs;

[0072] S3.2: Read characteristic parameter data within N sampling periods from the historical data storage area , and the interpolated MOSFET aging state characteristic parameters Add as a new line to , generate a feature parameter data set, and then arrange the data in the feature parameter data set in chronological order to form a feature parameter sequence matrix .

[0073] The specific steps of step S3 also include:

[0074] S3.3: Get feature parameter sequence matrix , and Perform vectorization processing to obtain the characteristic parameter vector ;

[0075] S3.4: Load a pre-built recurrent neural network model architecture from the model storage area and perform an initialization operation, wherein the recurrent neural network model architecture is a prior art content in the art and is not an inventive solution of the present application, and is not described in detail here;

[0076] S3.5: Input into the recurrent neural network model architecture to train the recurrent neural network model to obtain a dynamic aging state assessment model, wherein the training and prediction process of the recurrent neural network model is the prior art content in this field, is not the inventive solution of the present application, and is not described in detail here;

[0077] S3.6: Real-time The dynamic aging state assessment model is used to infer the aging state of the MOSFET and output an assessment result vector representing the aging state. .

[0078] Furthermore, the construction process of the dynamic aging state assessment model includes:

[0079] (1) First, collect historical data of silicon carbide MOSFETs with different aging degrees, including operating voltage, current, junction temperature data under various actual working conditions and corresponding aging state characteristic parameter data obtained through precise measurement and analysis, and divide these data into training set, validation set and test set according to a certain ratio. The training set is used for model training, the validation set is used to verify and adjust the performance of the model during the training process to prevent overfitting, and the test set is used to finally evaluate the generalization ability and accuracy of the model.

[0080] (2) Then, select the recurrent neural network algorithm as the basic architecture of the model, and design the specific structure of the model based on the selected basic architecture, determine the number of neurons in the input layer, hidden layer, and output layer, the connection method, and various hyperparameters such as learning rate, batch size, and number of training rounds;

[0081] (3) In the model training stage, the training set data is input into the model, and the output prediction value of the model is calculated by forward propagation. Then, according to the difference between the prediction value and the actual aging state label, the cross entropy loss function is used to calculate the loss value. At the same time, the back propagation algorithm is used to calculate the gradient of the model parameters relative to the loss function, and the model parameters are updated according to the stochastic gradient descent optimization algorithm. The weights and biases of the model are continuously adjusted so that the model gradually learns the complex mapping relationship between the feature parameters and the aging state during the training process, thereby improving the prediction accuracy and generalization ability of the model. During the training process, the model is regularly verified using the validation set. According to the performance indicators on the validation set, such as accuracy, recall rate, and F1 value, the hyperparameters of the model are adjusted and optimized. When the performance of the model on the validation set is no longer improved, the training is stopped to prevent the occurrence of overfitting, and a trained dynamic aging state assessment model is obtained. It should be understood that the forward propagation algorithm, the back propagation algorithm, the cross entropy loss function, the stochastic gradient descent optimization algorithm, and the performance indicator calculation formula are all prior art contents in this field, and are not the inventive scheme of this application, and are not elaborated here.

[0082] (4) Finally, the feature parameters extracted and preprocessed in real time are input into the trained aging status assessment model. The model makes inferences and predictions about the aging status of the MOSFET based on the patterns and rules it has learned, and outputs an assessment result vector representing the aging status. This vector usually indicates the probability distribution of the MOSFET belonging to various aging status categories, such as normal, mild aging, moderate aging, and severe aging.

[0083] The specific steps of S4 include:

[0084] S4.1: Obtain the output result of the dynamic aging status assessment model, organize the aging status information into a display data structure according to a predetermined format and layout, and transmit the aging status information to the display unit through the established communication connection for real-time update and display;

[0085] S4.2: After the display update is completed, judging whether the alarm condition is met according to the output result of the dynamic aging state evaluation model and the preset alarm thresholds of each level;

[0086] If the dynamic aging status assessment model determines that the MOSFET is in a non-aging state and the aging status information is less than the first-level alarm threshold, the MOSFET state is normal and continues to wait for the next assessment result update;

[0087] If the dynamic aging status assessment model determines that the MOSFET is in a mild aging state, and the aging status information is greater than or equal to the first-level alarm threshold and less than the second-level alarm threshold, the MOSFET status is normal, triggering the first-level alarm, and regular inspection and maintenance are performed;

[0088] If the dynamic aging status assessment model determines that the MOSFET is in a moderate aging state, and the aging status information is greater than or equal to the second-level alarm threshold and less than the third-level alarm threshold, the second-level alarm is triggered, and monitoring measures are taken to adjust the system operating parameters;

[0089] If the dynamic aging status assessment model determines that the MOSFET is in a serious aging state and the aging status information is greater than or equal to the third-level alarm threshold, a third-level alarm is triggered, the equipment is immediately stopped, and the MOSFET is replaced.

[0090] Example 2

[0091] See also Figure 3 Another embodiment provided by the present invention is an online aging status monitoring system for silicon carbide MOSFET, comprising:

[0092] Data acquisition module, data analysis module, aging status assessment module, display and alarm module, model optimization module;

[0093] The data acquisition module is used to collect the operating voltage, operating current and junction temperature data of the silicon carbide MOSFET in real time when the silicon carbide MOSFET is working normally, providing a basis for subsequent data processing and analysis;

[0094] A data analysis module is used to process and analyze the received data and extract characteristic parameters of the aging state of the MOSFET;

[0095] An aging status evaluation module is used to evaluate the aging status of the silicon carbide MOSFET based on the extracted characteristic parameters;

[0096] A display and alarm module is used to output the aging status evaluation results to the display unit for display, and trigger alarm signals of different levels according to the evaluation results;

[0097] The model optimization module is used to collect feedback data from aging status monitoring and optimize the aging status assessment model based on these data.

[0098] The data analysis module includes: a data receiving unit and a data analysis unit;

[0099] A data receiving unit, used for receiving data from the data acquisition module;

[0100] The data analysis unit is used to process and analyze the received data and extract the characteristic parameters of the aging status of the MOSFET, such as the threshold voltage change, on-resistance change rate, leakage current increment and junction temperature change rate. These parameters can reflect the aging degree and status of the MOSFET.

[0101] The aging state assessment module includes: a model selection unit and a state assessment unit;

[0102] A model selection unit is used to build and train a dynamic aging status assessment model that integrates multi-source data and time series information. The model can comprehensively consider multiple factors and accurately assess the aging status of MOSFET;

[0103] The state evaluation unit is used to specifically evaluate the aging state of the MOSFET according to the application results of the model and obtain a quantitative index of the aging degree.

[0104] The display and alarm module includes: a display unit and an alarm strategy unit;

[0105] A display unit, used to display aging status information of the silicon carbide MOSFET, including quantitative indicators and characteristic parameters of the aging degree;

[0106] The alarm strategy unit automatically triggers alarm signals of different levels by setting multi-level alarm strategies based on multiple dimensions such as the severity of the aging status, the rate of change, and the potential impact on the system. These alarm signals can remind operators to take timely measures to avoid failures.

[0107] Example 3

[0108] An electronic device includes a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, steps of an online aging status monitoring method for silicon carbide MOSFET are implemented. For details, please refer to the above method embodiment, which will not be repeated here.

[0109] It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0110] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0111] The embodiments of the present invention are described above in conjunction with the accompanying drawings, but the present invention is not limited to the above-mentioned specific implementation modes, which are merely illustrative rather than restrictive. Under the guidance of the present invention, ordinary technicians in the field may also change, modify, replace and modify the above-mentioned embodiments without departing from the purpose and scope of protection of the present invention, and all of these are within the protection of the present invention.

Claims

1. A method for online aging status monitoring of silicon carbide MOSFET, characterized in that: include: Step S1: When the silicon carbide MOSFET is working normally, the operating voltage, operating current and junction temperature data thereof are collected in real time through the sensor, and the collected data are transmitted to the data processing unit; Step S2: the data processing unit analyzes and processes the received data to extract the MOSFET aging state characteristic parameters, which include the threshold voltage change, the on-resistance change rate, the leakage current increment and the junction temperature change rate; Step S3: Based on the extracted characteristic parameters, the aging state of the silicon carbide MOSFET is evaluated using a pre-established dynamic aging state evaluation model that integrates multi-source data and time series information; Step S4: according to the aging status evaluation result, the aging status information of the silicon carbide MOSFET is output to the display unit for display, and at the same time, a multi-level alarm strategy is set to automatically trigger different levels of alarm signals according to the severity of the aging status, the rate of change and the potential impact on the system; Step S5: Collect feedback data from aging status monitoring, and optimize the aging status assessment model based on the feedback data.

2. An online aging status monitoring method for silicon carbide MOSFET according to claim 1, characterized in that: The process of extracting the characteristic parameters of the MOSFET aging state in step S2 includes: sending a specific test pulse signal to the silicon carbide MOSFET through the data processing unit, and measuring the gate-source voltage of the MOSFET using a voltage measurement circuit during the application of the test pulse. and drain-source voltage , at the same time, set the voltage fluctuation threshold ,like , then the corresponding gate-source voltage As the threshold voltage of MOSFET , then, the threshold voltage of the MOSFET The initial threshold voltage of the MOSFET pre-stored in the data processing unit Perform difference calculation to obtain the threshold voltage change .

3. The online aging status monitoring method for silicon carbide MOSFET according to claim 2, characterized in that: The extraction process of the MOSFET aging state characteristic parameters in step S2 also includes: and operating voltage Data, calculate the real-time on-resistance of MOSFET and with the initial on-resistance Compared with the above, the on-resistance change rate is obtained. ; Real-time monitoring of MOSFET leakage current , and the leakage current increment is obtained At the same time, according to the collected junction temperature data , calculate the junction temperature change rate ,in, represents the initial leakage current value, Indicates the initial junction temperature pre-stored in the data processing unit.

4. The online aging status monitoring method for silicon carbide MOSFET according to claim 3, characterized in that: The specific steps of step S3 include: S3.1: Obtain MOSFET aging state characteristic parameter set ,and and use The obtained MOSFET aging state characteristic parameter set is interpolated and supplemented to obtain the MOSFET aging state characteristic parameter after interpolation and supplementation. ,in, represents the mth MOSFET aging state characteristic parameter, m represents the number of data in the MOSFET aging state characteristic parameter set, , , They represent the aging state characteristic parameters of the i-1th, i, and i+1th MOSFETs in X, respectively. , , Respectively represent the time points corresponding to the characteristic parameters of the aging states of the i-1th, ith, and i+1th MOSFETs; S3.2: Read characteristic parameter data within N sampling periods from the historical data storage area , and the interpolated MOSFET aging state characteristic parameters Add as a new line to , generate a feature parameter data set, and then arrange the data in the feature parameter data set in chronological order to form a feature parameter sequence matrix .

5. The online aging status monitoring method for silicon carbide MOSFET according to claim 4, characterized in that: The specific steps of step S3 also include: S3.3: Get feature parameter sequence matrix , and Perform vectorization processing to obtain the characteristic parameter vector ; S3.4: Load the pre-built recurrent neural network model architecture from the model storage area and perform initialization operations; S3.5: Input into the recurrent neural network model architecture to train the recurrent neural network model to obtain a dynamic aging state assessment model; S3.6: Real-time The dynamic aging state assessment model is used to infer the aging state of the MOSFET and output an assessment result vector representing the aging state. .

6. The online aging status monitoring method for silicon carbide MOSFET according to claim 5, characterized in that: The specific steps of S4 include: S4.1: Obtain the output result of the dynamic aging status assessment model, organize the aging status information into a display data structure according to a predetermined format and layout, and transmit the aging status information to the display unit through the established communication connection for real-time update and display; S4.2: After the display update is completed, judging whether the alarm condition is met according to the output result of the dynamic aging state evaluation model and the preset alarm thresholds of each level; If the dynamic aging status assessment model determines that the MOSFET is in a non-aging state and the aging status information is less than the first-level alarm threshold, the MOSFET state is normal and continues to wait for the next assessment result update; If the dynamic aging status assessment model determines that the MOSFET is in a mild aging state, and the aging status information is greater than or equal to the first-level alarm threshold and less than the second-level alarm threshold, the MOSFET status is normal, triggering the first-level alarm, and regular inspection and maintenance are performed; If the dynamic aging status assessment model determines that the MOSFET is in a moderate aging state, and the aging status information is greater than or equal to the second-level alarm threshold and less than the third-level alarm threshold, the second-level alarm is triggered, and monitoring measures are taken to adjust the system operating parameters; If the dynamic aging status assessment model determines that the MOSFET is in a serious aging state and the aging status information is greater than or equal to the third-level alarm threshold, a third-level alarm is triggered, the equipment is immediately stopped, and the MOSFET is replaced.

7. An online aging status monitoring method for silicon carbide MOSFET according to claim 6, characterized in that: The current aging status information of the MOSFET displayed in step S2 includes real-time characteristic parameter values, aging degree levels and historical aging trend curves.

8. An online aging status monitoring system for silicon carbide MOSFET, which is used to implement an online aging status monitoring method for silicon carbide MOSFET according to any one of claims 1 to 7, characterized in that: include: Data acquisition module, data analysis module, aging status assessment module, display and alarm module, model optimization module; The data acquisition module is used to collect the operating voltage, operating current and junction temperature data of the silicon carbide MOSFET in real time when the silicon carbide MOSFET is working normally; The data analysis module is used to process and analyze the received data and extract the aging state characteristic parameters of the MOSFET; The aging state evaluation module is used to evaluate the aging state of the silicon carbide MOSFET according to the extracted characteristic parameters; The display and alarm module is used to output the aging status evaluation results to the display unit for display, and trigger alarm signals of different levels according to the evaluation results; The model optimization module is used to collect feedback data from aging status monitoring and optimize the aging status assessment model based on these data.

9. An online aging status monitoring system for silicon carbide MOSFET according to claim 8, characterized in that: The aging status assessment module includes: a model selection unit and a status assessment unit; The model selection unit is used to construct and train a dynamic aging state assessment model that integrates multi-source data and time series information; The state evaluation unit is used to specifically evaluate the aging state of the MOSFET according to the application result of the model, and obtain a quantitative index of the aging degree.

10. An electronic device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of an online aging status monitoring method for silicon carbide MOSFET according to any one of claims 1 to 7 are implemented.

Images