A method and system for detecting the current carrying capacity of a COOLMOS

Abstract

The application provides a current carrying capacity detection method and system of COOLMOS, and relates to the technical field of current carrying capacity detection. The method comprises the following steps: obtaining an association relationship by associating a thermal image set and an electrical characteristic data set collected from a to-be-tested device; performing current critical main and auxiliary judgment point analysis through the association relationship, and obtaining a current critical point through weighted fusion; and extracting a time-proximal thermal runaway non-occurrence point from the association relationship according to the current critical point, and taking the point as the current carrying capacity. The application can solve the technical problem that the early signs of the evolution of a device from a thermal stable state to a thermal runaway state cannot be identified in time in the prior art, realize the technical goal of accurately extracting a current critical point and a current carrying capacity by fusing thermal images and electrical characteristic data for multi-dimensional dynamic monitoring and critical analysis, and achieve the technical effect of improving the evaluation precision of the current carrying capacity, strengthening the failure early warning capability, and enhancing the reliability verification level of the device.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of current carrying capacity detection, and in particular to a COOLMOS current carrying capacity detection method and system. BACKGROUND

[0002] With the continuous development of power electronic systems in high performance and high density applications, COOLMOS devices, as a kind of high-voltage and low-on-resistance super-junction power metal oxide semiconductor devices, are widely used in power management, motor drive, inverter conversion and other high-frequency and high-speed switching scenarios.

[0003] At present, the existing method usually relies on the critical change of current, voltage and other electrical signals as the basis for judgment when detecting the current carrying capacity of COOLMOS devices, but this kind of method ignores the temperature distribution evolution process inside the device, which is one of the fundamental mechanisms leading to failure. Since COOLMOS devices will produce significant self-heating effect in the junction area when they bear high current, the internal temperature does not rise uniformly, but local hot spot areas will appear. If the detection means cannot identify the formation and expansion trajectory of these hot spots, it is impossible to accurately determine whether the device is in the edge state of thermal runaway. More seriously, this thermal runaway often has hysteresis, that is, when the electrical parameters are still in the normal range, irreversible structural damage may have occurred inside the device. Therefore, only by voltage mutation or current interruption to determine the limit point, there is obvious hysteresis and misjudgment risk.

[0004] In summary, in the prior art, due to the neglect of the dynamic evolution process of the internal temperature distribution of the COOLMOS device, only relying on external electrical signals to determine the critical point, it is impossible to identify the early signs of the evolution of the device from a thermal stable state to a thermal runaway state in time, which further affects the accuracy of current carrying capacity evaluation, the reliability of failure determination and the engineering stability in the process of device screening and application. SUMMARY

[0005] The purpose of the present application is to provide a COOLMOS current carrying capacity detection method and system to solve the technical problem in the prior art that due to the neglect of the dynamic evolution process of the internal temperature distribution of the COOLMOS device, only relying on external electrical signals to determine the critical point, it is impossible to identify the early signs of the evolution of the device from a thermal stable state to a thermal runaway state in time, which further affects the accuracy of current carrying capacity evaluation, the reliability of failure determination and the engineering stability in the process of device screening and application.

[0006] In view of the above problems, the present application provides a COOLMOS current carrying capacity detection method and system.

[0007] In a first aspect, the application provides a COOLMOS current carrying capacity detection method, which is realized by a COOLMOS current carrying capacity detection system, comprising: obtaining an association relationship by associating a thermal image set and an electrical characteristic data set obtained by collecting a to-be-tested device; performing current criticality main and auxiliary judgment point analysis through the association relationship, and obtaining a current critical point by weighted fusion; and extracting a time-proximate thermal runaway non-occurrence point from the association relationship as a current carrying capacity according to the current critical point.

[0008] Preferably, the COOLMOS current carrying capacity detection method further comprises: obtaining a to-be-tested device fixed on a circuit board for conduction, wherein the circuit board has a thermal window; performing current loading on the to-be-tested device after conduction; performing thermal imaging of surface temperature of the to-be-tested device after current loading by placing an infrared thermal imager above the to-be-tested device to obtain a thermal image set; synchronously obtaining an electrical characteristic data set corresponding to the thermal image set, associating the electrical characteristic data set with the thermal image set, and obtaining the association relationship.

[0009] Preferably, the COOLMOS current carrying capacity detection method further comprises: performing temperature curve evolution trend analysis under current variation based on the thermal image set, performing linear fitting and mutation inflection point analysis on the temperature trend, and extracting a trend turning point as a current criticality main judgment point; and judging the current carrying capacity according to the current criticality main judgment point and a current criticality auxiliary judgment point.

[0010] Preferably, the COOLMOS current carrying capacity detection method further comprises: extracting thermal spot area, temperature gradient and average temperature from a temperature characteristic parameter of the thermal image set; performing mutation analysis of the temperature characteristic parameter with a current data set in the electrical characteristic data set based on the association relationship to obtain a temperature rise mutation point of the current data set; performing sudden increase analysis of the thermal spot area with the current data set in the electrical characteristic data set based on the association relationship to obtain a thermal spot sudden increase point of the current data set; performing mutation analysis of the temperature gradient with the current data set in the electrical characteristic data set based on the association relationship to obtain a gradient mutation point of the current data set; and combining the temperature rise mutation point, the thermal spot sudden increase point and the gradient mutation point to form the current criticality main judgment point.

[0011] Preferably, the current carrying capacity detection method of the COOLMOS further comprises: performing mutation analysis of the voltage data set with the current data set in the electrical characteristic data set to obtain a voltage mutation point of the current data set; performing nonlinear growth analysis of the current data set in the electrical characteristic data set to obtain a nonlinear growth point of the current data set; performing waveform burr analysis of the current data set to obtain a current jitter point of the current data set; performing waveform drop analysis of the current data set to obtain a current interruption point of the current data set; and combining the voltage mutation point, the nonlinear growth point, the current jitter point and the current interruption point to form the current critical auxiliary judgment point.

[0012] Preferably, the current carrying capacity detection method of the COOLMOS further comprises: performing repeated testing on the to-be-tested device and sample devices of the same type under the same test conditions to obtain sample current carrying capacities; and judging whether the current carrying limit is stable based on the sample current carrying capacities and the current carrying capacity, and if the judgment is unstable, performing classification analysis on the sample devices, and excluding failure of the to-be-tested device according to the stability judgment result of the classified sample devices to complete verification of the current carrying capacity.

[0013] Preferably, the current carrying capacity detection method of the COOLMOS further comprises: based on the sample device set, obtaining a corresponding relative safety margin set; extracting a corresponding relative safety margin from the relative safety margin set according to the sample device; and judging the margin carrying of the current carrying limit through the relative safety margin to obtain a stability judgment result.

[0014] Preferably, the current carrying capacity detection method of the COOLMOS further comprises: extracting an extreme abnormal sample device from the sample device set for unpacking and microscopic observation to obtain a sample defect result; and performing comparison analysis on the sample defect result and the failed to-be-tested device according to a preset comparison similarity to judge failure of the to-be-tested device.

[0015] Preferably, the current carrying capacity detection method of the COOLMOS further comprises: based on the correlation, normalizing and mapping related feature dimensions of the current carrying capacity in the correlation to generate a three-dimensional thermal-electric critical analysis model; and visualizing the three-dimensional thermal-electric critical analysis model as a three-dimensional temperature rise-voltage surface graph to label the current carrying capacity.

[0016] In a second aspect, the application further provides a current carrying capacity detection system of COOLMOS, used for executing the current carrying capacity detection method of COOLMOS as described in the first aspect, comprising: a correlation obtaining module, configured to obtain a correlation by correlating a thermal image set collected from a to-be-tested device and an electrical characteristic data set; a current critical point obtaining module, configured to obtain a current critical point by performing current critical main and auxiliary judgment point analysis on the correlation; and a current carrying capacity obtaining module, configured to extract a time-proximate thermal runaway non-occurrence point from the correlation as the current carrying capacity according to the current critical point.

[0017] The technical solutions provided in the application have at least the following technical effects or advantages: by implementing multi-dimensional dynamic monitoring and critical analysis of the fusion of thermal images and electrical characteristic data, the technical target of accurately extracting the current critical point and the current carrying capacity is achieved, and the technical effects of improving the current carrying capacity evaluation precision, strengthening the failure early warning capability, and enhancing the device reliability verification level are achieved.

[0018] The above description is only a summary of the technical solutions of the application. In order to enable the technical means of the application to be more clearly understood, the following detailed description can be implemented according to the content of the description, and in order to enable the above and other purposes, features and advantages of the application to be more apparent and easy to understand, the following detailed description of the specific embodiments of the application is provided. It should be understood that the content described in this section is not intended to identify the key or important features of the embodiments of the application, nor is it intended to limit the scope of the application. Other features of the application will become apparent from the following description. BRIEF DESCRIPTION OF DRAWINGS

[0019] In order to more clearly illustrate the technical solutions in the application or the prior art, the following will briefly introduce the drawings needed to be used in the embodiments or the prior art description. Obviously, the drawings in the following description are only exemplary, and for those skilled in the art, other drawings can also be obtained without creative labor on the basis of the provided drawings.

[0020] Figure 1 A flowchart of the current carrying capacity detection method of COOLMOS according to the application.

[0021] Figure 2 A structure diagram of the current carrying capacity detection system of COOLMOS according to the application.

[0022] Legend of reference signs: correlation obtaining module 11, current critical point obtaining module 12, and current carrying capacity obtaining module 13. DETAILED DESCRIPTION

[0023] This application provides a current carrying capacity detection method and system for COOLMOS devices. It addresses the technical problem in existing technologies where the dynamic evolution of the internal temperature distribution of COOLMOS devices is ignored, and critical point judgment relies solely on external electrical signals. This leads to the inability to promptly identify early signs of the device evolving from a thermally stable state to a thermal runaway state, further affecting the accuracy of current carrying capacity assessment, the reliability of failure determination, and the engineering stability during device selection and application. The method achieves the technical goal of integrating thermal images and electrical characteristic data for multi-dimensional dynamic monitoring and critical analysis, accurately extracting current critical points and current carrying capacity. This results in improved accuracy of current carrying capacity assessment, enhanced failure early warning capabilities, and improved device reliability verification.

[0024] The technical solutions of this application will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. It should be understood that this application is not limited to the exemplary embodiments described herein. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of this application. It should also be noted that, for ease of description, only the parts related to this application are shown in the accompanying drawings, not all of them.

[0025] Example 1, please refer to the appendix. Figure 1 This application provides a method for detecting the current carrying capacity of a COOLMOS transistor, applied to a COOLMOS current carrying capacity detection system, specifically including the following steps:

[0026] S1: The correlation relationship is obtained by associating the thermal image set acquired from the device under test with the electrical characteristic dataset.

[0027] Specifically, during the testing of COOLMOS devices, their surface temperature distribution and electrical behavior are simultaneously acquired, and a correlation is established between the two. The thermal image dataset refers to a series of temperature distribution images acquired using an infrared thermal imager at different stages of current loading, reflecting temperature changes at different locations on the device surface on a frame-by-frame basis. The electrical characteristic dataset includes current, voltage, and power parameter curves recorded synchronously with the thermal image acquisition time, used to describe the device's response process under electrical signal loading. To establish an effective correlation, each frame of the thermal image needs to be matched one-to-one with its corresponding electrical data on the time axis, revealing the dynamic coupling relationship between thermal response and electrical load.

[0028] S2: Analyze the primary and secondary current critical decision points through the aforementioned correlation, and obtain the current critical point by weighted fusion.

[0029] Specifically, by analyzing the current critical main and auxiliary judgment points through the correlation relationship, based on the time synchronization and parameter correspondence relationship established between the thermal image set and the electrical characteristic data set, two types of critical feature points that may occur in the device during the gradual increase of current are identified respectively. Among them, the main judgment point is mainly derived from the position of thermal feature mutation such as temperature rise curve, thermal spot area or temperature gradient, which is manifested as a sudden rapid rise of the surface temperature of the device; the auxiliary judgment point is more from the abnormal points of electrical characteristics such as voltage mutation, current jitter or waveform nonlinearity. In order to obtain more accurate critical judgment results, the main judgment point and the auxiliary judgment point need to be weighted and fused, that is, the importance weight of different features to the overall judgment is set, and a unified current critical point is formed by mathematical superposition.

[0030] S3: According to the current critical point, a time-proximate thermal point runaway non-occurrence point is extracted from the correlation relationship as the current carrying capacity.

[0031] Specifically, according to the current critical point, a time-proximate thermal point runaway non-occurrence point is extracted from the correlation relationship as the current carrying capacity, on the basis of the current critical point that the device is about to enter the thermal runaway or electrical abnormal state, the stable state data slightly earlier than the point in the thermal image and electrical data is traced back, and the last time when the thermal point does not appear the temperature rise or voltage abnormal change is found, and the corresponding current value is taken as the maximum working current that the device can safely carry. The current critical point is the position where the device performance is about to deteriorate, and the thermal point runaway non-occurrence point is the last "safe frame" before the critical point. Through this reverse positioning method based on thermal-electric correlation data, the misjudgment caused by the edge unstable state can be effectively eliminated, so that a more stable and real current carrying capacity index is obtained, which provides a basis for device design margin evaluation and engineering use.

[0032] Further, the present application also includes: acquiring a to-be-tested device fixed on a circuit board for conduction, wherein the circuit board has a thermal window; loading current on the to-be-tested device after conduction; imaging the surface temperature of the to-be-tested device after current loading through an infrared thermal imager placed above the to-be-tested device to obtain a thermal image set; synchronously acquiring an electrical characteristic data set corresponding to the thermal image set, correlating the electrical characteristic data set with the thermal image set, and obtaining the correlation relationship.

[0033] Specifically, the to-be-tested device is fixed on the circuit board for conduction, which means that a to-be-tested COOLMOS device is welded or mounted on a special test circuit board, and a forward voltage is applied to the gate of the device to make it conduct. The circuit board is pre-designed with a thermal window, which leaves an open area directly below or above the device, so that the infrared thermal imager can directly observe the real surface temperature distribution of the heating area of the device, avoiding the shielding or interference of the PCB material on the thermal radiation.

[0034] Subsequently, the device under test is subjected to current loading, meaning that a constant voltage source or a pulsed current source is applied to the device in the on state from the drain to the source, with a gradually increasing current, and the current level is gradually increased to evaluate the bearing performance of the device under different current loads.

[0035] Subsequently, the device under test is subjected to current loading, meaning that a constant voltage source or a pulsed current source is applied to the device in the on state from the drain to the source, with a gradually increasing current, and the current level is gradually increased to evaluate the bearing performance of the device under different current loads.

[0036] At the same time, a set of electrical characteristic data corresponding to each thermal image frame is synchronously collected, including time series parameters such as current, voltage, and power. For example, in the case of a thermal frame rate of thirty frames per second, each thermal image frame matches the VDS voltage and ID current data at a time point. After aligning the electrical characteristic data with the thermal image set, a set of thermal-electric response mappings with time sequence correlation, i.e., correlation, can be established.

[0037] Further, the present application also includes: based on the thermal image set, performing temperature curve evolution trend analysis under current change, linear fitting and mutation inflection point analysis of temperature trend, extracting the trend turning point as the current critical main decision point; judging the current bearing capacity according to the current critical main decision point and the current critical auxiliary decision point.

[0038] Specifically, based on the thermal image set, the temperature curve evolution trend analysis under current change is performed, the collected infrared thermal images are sorted according to the time sequence of current loading, and the device temperature characteristic parameters corresponding to each level of current are extracted, and the highest temperature, average temperature or temperature center value of a specific area on the surface of the device is analyzed, so that the change curve between current and temperature can be observed as the current gradually increases, the temperature is slowly rising, suddenly accelerating or sharply deteriorating at a certain point, reflecting the change of thermal stability of the device. Linear fitting and mutation inflection point analysis of temperature trend, the overall trend of temperature change with current is described by a linear function, and then the inflection point of sudden increase of slope in the curve is found by algorithm to obtain the turning point of temperature rising speed from slow to fast.

[0039] The trend turning point is extracted as the current critical main decision point, the current value corresponding to the mutation inflection point in the curve is defined as the thermal response main limit current of the device, and the last stable interval of the device before reaching the electro-thermal critical state is obtained.

[0040] According to the current carrying capacity judgment of the current critical main decision point and the current critical auxiliary decision point, the main decision point obtained through temperature change is compared and fused with the auxiliary decision point extracted from the electrical characteristics such as voltage waveform, current nonlinearity and waveform burr, to judge whether they are close and establish a weighted rule.

[0041] Further, the application also includes: extracting hot spot area, temperature gradient and average temperature from the temperature characteristic parameters of the thermal image set; performing sudden change analysis of the temperature characteristic parameters with the current data set in the electrical characteristic data set based on the correlation, to obtain the temperature rise sudden change point of the current data set; performing sudden increase analysis of the hot spot area with the current data set in the electrical characteristic data set based on the correlation, to obtain the hot spot sudden increase point of the current data set; performing sudden change analysis of the temperature gradient with the current data set in the electrical characteristic data set based on the correlation, to obtain the gradient sudden change point of the current data set; combining the temperature rise sudden change point, the hot spot sudden increase point and the gradient sudden change point to form the current critical main decision point.

[0042] Specifically, hot spot area, temperature gradient and average temperature are extracted from the temperature characteristic parameters of the thermal image set, and the thermal distribution data in each frame of infrared thermal image is processed to extract multiple indexes representing the thermal state of the device surface. Among them, the hot spot area refers to the area of the region in the image whose temperature is higher than the set threshold, reflecting the extension degree of the high temperature region; the temperature gradient represents the temperature difference change rate between different regions of the device, revealing the unevenness of heat diffusion; the average temperature is the temperature mean value of the whole target region, which is used to measure the overall thermal load level.

[0043] Based on the correlation, the sudden change analysis of the temperature characteristic parameters with the current data set in the electrical characteristic data set is performed, the law of temperature change in the process of gradually increasing current is tracked, the inflection point of sudden rise of average temperature at a certain current value is identified, the temperature rise sudden change point of the current data set is obtained, indicating that the device heat dissipation ability reaches the limit or the internal thermal resistance suddenly deteriorates.

[0044] Based on the correlation, the sudden increase analysis of the hot spot area with the current data set in the electrical characteristic data set is performed, the area growth behavior of the high temperature region in the current change process is observed, the hot spot sudden increase point of the current data set is obtained, and when the current reaches a certain level, if the hot spot area appears a jump far greater than the normal linear growth trend, it means that the local hot spot is uncontrollable.

[0045] The mutation analysis of the current data set in the temperature gradient and the electrical characteristic data set based on the correlation relationship is performed, the sharp change of the temperature difference between different positions in the current change process is analyzed, and the gradient mutation point of the current data set is obtained. When the gradient corresponding to a current point changes from a small linear rise to a sharp jump, it can be considered as a key node where the thermal distribution balance of the device is destroyed. The first appearing or the most stable mutation point among the three is selected as the main critical current point of the device, and the main critical current point is combined to form the current critical main judgment point.

[0046] Further, the application also includes: performing mutation analysis of the voltage data set and the current data set in the electrical characteristic data set to obtain the voltage mutation point of the current data set; performing nonlinear growth analysis of the current data set in the electrical characteristic data set to obtain the nonlinear growth point of the current data set; performing waveform burr analysis of the current data set to obtain the current jitter point of the current data set; performing waveform drop analysis of the current data set to obtain the current interruption point of the current data set; and combining the voltage mutation point, the nonlinear growth point, the current jitter point and the current interruption point to form the current critical auxiliary judgment point.

[0047] Specifically, the mutation analysis of the voltage data set and the current data set in the electrical characteristic data set is performed, the voltage data recorded under each level of current loading condition is continuously analyzed, the voltage mutation point of the current data set is obtained, and whether the voltage mutation point has abnormal conditions of sudden change when the current gradually rises is observed. The voltage mutation usually shows that the drain voltage of the device suddenly changes from slow rise to rapid rise at a certain current level.

[0048] The nonlinear growth analysis of the current data set in the electrical characteristic data set is performed, the nonlinear growth point of the current data set is obtained, and whether the change curve of the current with time or driving voltage deviates from the linear relationship under the given control voltage or gate driving condition is observed. When the device enters the nonlinear region, the increase of the current no longer changes uniformly, but may have an abnormal stage of sudden slowing down or speeding up of the rising speed.

[0049] The waveform burr analysis of the current data set is performed, the current jitter point of the current data set is obtained, the sharp transient fluctuation in the current curve is identified and analyzed by using the waveform record of high-frequency sampling. The burr appears when the parasitic oscillation in the device, EMI interference or the unstable package pin appears, and the jitter amplitude exceeding a certain threshold can be considered as a precursor of instability.

[0050] The waveform drop analysis of the current data set is performed to obtain a current interruption point of the current data set, and whether a momentary drop or zero flow phenomenon occurs in the waveform of the current during the current loading process is monitored, and the drop represents a device internal failure, a disconnection of an electrical connection, or a breakdown protection action trigger. In combination with the voltage mutation point, the nonlinear growth point, the current jitter point and the current interruption point, the first or most typical point in the electrical characteristics is extracted and combined into the current critical auxiliary judgment point as the auxiliary judgment basis for the current critical change.

[0051] Further, the application also includes: performing repeated tests on the to-be-tested device and sample devices of the same type under the same test conditions to obtain sample current carrying capacity; judging whether the current carrying limit is stable based on the sample current carrying capacity and the current carrying capacity, and if it is judged to be unstable, performing classification analysis on the sample devices, and excluding the failure of the to-be-tested device according to the stability judgment result of the classified sample devices to complete the verification of the current carrying capacity.

[0052] Specifically, a plurality of device samples of the same type as the to-be-tested COOLMOS device are selected, and repeated current carrying capacity tests are performed in a completely consistent experimental environment. The test conditions include constant environmental temperature, heat dissipation mode, current loading rate and thermal imager sampling frequency, etc., to ensure that all samples are measured under a unified reference, to obtain the carrying behavior of the samples under the same stress, and to avoid individual differences affecting the judgment.

[0053] Based on the sample current carrying capacity and the current carrying capacity, it is judged whether the current carrying limit is stable, the critical current carrying value of the to-be-tested device is compared with the carrying results of the plurality of sample devices, and it is analyzed whether the difference is within an acceptable fluctuation range. If the carrying capacity of the sample is concentrated around a certain average value, and the result of the to-be-tested device deviates significantly, it is necessary to judge whether the result is stable or abnormal.

[0054] If it is judged to be unstable, the sample devices are classified and analyzed, the sample devices are divided into a stable group and an abnormal group according to the difference in the current carrying performance of the sample devices, it is judged whether there are manufacturing defects, packaging differences or abnormal heat distribution factors, and the result is attributed.

[0055] According to the stability judgment result of the classified sample devices, the failure of the to-be-tested device is excluded, and the verification of the current carrying capacity is completed. If most of the sample results are stable and the deviation is small, and the test value of the to-be-tested device deviates significantly from the sample interval, it means that there may be a device defect, and at this time the result should be treated as a whole representative of the type, and be excluded; otherwise, it can be confirmed as a stable carrying capacity representative, thereby completing the effectiveness verification of the test result.

[0056] Further, the application also includes: based on the sample device set, obtaining a corresponding relative safety margin set; extracting a corresponding relative safety margin from the relative safety margin set according to the sample device; and performing margin bearing judgment on the current carrying limit through the relative safety margin to obtain a stability judgment result.

[0057] Specifically, based on the sample device set, a corresponding relative safety margin set is obtained. After current carrying tests are performed on multiple sample devices of the same type, the margin difference between the actual current critical point and the rated specification of each sample device is calculated, and the difference is aggregated into a relative safety margin set. The relative safety margin is an index margin that measures the additional current that a device can withstand without failure during actual use. It is represented by the result of subtracting the rated current from the actual critical current.

[0058] In the sample set, a specific sample individual is identified according to the number or sequence, and the individual margin value of the sample device is extracted from the overall relative safety margin data. Margin bearing judgment is performed on the current carrying limit based on the relative safety margin of the sample device to determine whether it can maintain stable operation and have sufficient redundancy space in actual application, and a stability judgment result is obtained. If the relative margin of the sample device is higher than the value, it is considered that the current carrying capacity has stability; otherwise, if the margin is lower than the threshold, there may be risks.

[0059] Further, the application also includes: extracting an extreme abnormal sample device from the sample device set for unpacking and microscopic observation to obtain a sample defect result; and performing comparison analysis based on the sample defect result and the failure to-be-tested device to determine the failure of the to-be-tested device according to a preset comparison similarity.

[0060] Specifically, an extreme abnormal sample device is extracted from the sample device set for unpacking and microscopic observation. Among all the tested sample devices, those individuals with significantly low current carrying capacity or abnormal behaviors such as abnormal waveforms and severe temperature rise during the test are selected as extreme abnormal samples. The abnormal devices are unpacked by removing the external packaging materials of the devices through chemical corrosion or mechanical cover opening, exposing the internal chips and wire structures; then a microscope is used for observation to find possible structural defects such as micro-cracks, ablation, cavities, metal migration and contact failure, which can reveal the physical root cause of device failure.

[0061] Based on the sample defect result and the failure to-be-tested device, the defect types found in the extreme abnormal sample devices are used as a reference for one-to-one comparison of image, structure or electrical characteristics with the failure performance of the to-be-tested device. The comparison can be performed in terms of morphology similarity, defect distribution position and material degradation degree to ensure the accuracy of the analysis.

[0062] After the sample defect mode is compared with the phenomenon of the device to be tested, quantitative judgment is performed according to a pre-set similarity threshold. For example, if the image structural similarity is higher than the pre-set similarity threshold, it can be determined that the device to be tested belongs to the same type of failure mechanism; if it is lower than the pre-set similarity threshold, the failure consistency cannot be directly inferred and needs to be further verified.

[0063] Further, the application also includes: based on the association relationship, performing normalized mapping on the related feature dimensions of the current carrying capacity in the association relationship, generating a three-dimensional thermal-electric critical analysis model; and visualizing output of the three-dimensional thermal-electric critical analysis model as a three-dimensional temperature rise-voltage surface graph, and performing marking of the current carrying capacity.

[0064] Specifically, after the correspondence between the thermal image and the electrical characteristic data is established in the test process, multiple variable dimensions closely related to the current carrying capacity are extracted therefrom, including temperature rise value, voltage change, hot spot area, temperature gradient, etc., then the multi-dimensional data is uniformly subjected to scale conversion, i.e., normalized processing, so that the numerical range is limited between zero and one, thereby eliminating the dimension influence of different physical quantities, the original data can be analyzed and compared with a relatively unified standard, which is helpful for subsequent construction of a space model. After the normalization is completed, the temperature feature, voltage change and corresponding current value are taken as three-axis coordinates, a data fitting or discrete point set in a three-dimensional space is established, a model reflecting the joint change trend of the thermal and electrical characteristics of the device is formed, and the relationship mode of the temperature rise speed, voltage offset and critical behavior of the device under different current levels can be revealed.

[0065] The three-dimensional thermal-electric critical analysis model is visualized and output as a three-dimensional temperature rise-voltage surface graph, converted into a visual image, and a three-dimensional surface graph with the temperature rise value as the horizontal axis, the voltage as the vertical axis and the current as the height is drawn, so as to display the working state evolution process of the device under the thermal and electrical coupling conditions. The critical region is represented as the position of sudden steep rise or depression of the surface, which is convenient for observation and judgment.

[0066] In the three-dimensional surface graph, the critical current point or the failure corner point that has been identified is marked with color, symbol or label, so that a certain critical region in the graph is significantly highlighted, thereby helping the user to quickly identify the limit position of the current carrying capacity of the device. Table 1 is part of the record of three-dimensional thermal-electric critical analysis modeling of a COOLMOS device.

[0067] Table 1: Part of the record of three-dimensional thermal-electric critical analysis modeling of a COOLMOS device

[0068]

[0069] In summary, the current carrying capacity detection method of the COOLMOS provided in the application has the following technical effects: by realizing multi-dimensional dynamic monitoring and critical analysis by fusing thermal images and electrical characteristic data, the technical goal of accurately extracting the current critical point and the current carrying capacity is achieved, and the technical effects of improving the current carrying capacity evaluation precision, strengthening the failure warning capability, and enhancing the device reliability verification level are achieved.

[0070] In the second embodiment, based on the same inventive concept as the current carrying capacity detection method of the COOLMOS in the foregoing embodiments, the application further provides a current carrying capacity detection system of the COOLMOS, please refer to the accompanying drawings Figure 2 , comprising: an association relationship obtaining module 11, configured to obtain an association relationship by associating a thermal image set and an electrical characteristic data set obtained by collecting a to-be-tested device; a current critical point obtaining module 12, configured to obtain a current critical point by performing current critical main and auxiliary judgment point analysis based on the association relationship and weighted fusion; and a current carrying capacity obtaining module 13, configured to extract a time-proximate thermal runaway non-occurrence point from the association relationship as the current carrying capacity according to the current critical point.

[0071] Further, the current carrying capacity detection system of the COOLMOS is further used for: obtaining a to-be-tested device fixed on a circuit board for conduction, wherein the circuit board has a thermal window; performing current loading on the to-be-tested device after conduction; performing thermal image imaging of the surface temperature of the to-be-tested device after current loading by placing an infrared thermal imager above the to-be-tested device to obtain a thermal image set; synchronously obtaining an electrical characteristic data set corresponding to the thermal image set, associating the electrical characteristic data set with the thermal image set, and obtaining the association relationship.

[0072] Further, the current carrying capacity detection system of the COOLMOS is further used for: based on the thermal image set, performing temperature curve evolution trend analysis under current change, performing linear fitting and mutation inflection point analysis on the temperature trend, and extracting a trend turning point as a current critical main judgment point; and judging the current carrying capacity according to the current critical main judgment point and a current critical auxiliary judgment point.

[0073] Further, the current carrying capacity detection system of the COOLMOS is also used for: extracting a hot spot area, a temperature gradient and an average temperature from a temperature characteristic parameter of the thermal image set; performing mutation analysis of the temperature characteristic parameter based on the correlation with the current data set in the electrical characteristic data set to obtain a temperature rise mutation point of the current data set; performing sudden increase analysis of the hot spot area based on the correlation with the current data set in the electrical characteristic data set to obtain a hot spot sudden increase point of the current data set; performing mutation analysis of the temperature gradient based on the correlation with the current data set in the electrical characteristic data set to obtain a gradient mutation point of the current data set; and combining the temperature rise mutation point, the hot spot sudden increase point and the gradient mutation point to form the current critical main judgment point.

[0074] Further, the current carrying capacity detection system of the COOLMOS is also used for: performing mutation analysis of the voltage data set with the current data set in the electrical characteristic data set to obtain a voltage mutation point of the current data set; performing nonlinear growth analysis of the current data set in the electrical characteristic data set to obtain a nonlinear growth point of the current data set; performing waveform burr analysis of the current data set to obtain a current jitter point of the current data set; performing waveform drop analysis of the current data set to obtain a current interruption point of the current data set; and combining the voltage mutation point, the nonlinear growth point, the current jitter point and the current interruption point to form the current critical auxiliary judgment point.

[0075] Further, the current carrying capacity detection system of the COOLMOS is also used for: performing repeated testing on the sample device of the same type under the same test condition to obtain a sample current carrying capacity; judging whether the current carrying limit is stable based on the sample current carrying capacity and the current carrying capacity; if it is judged that the current carrying limit is not stable, performing classification analysis on the sample device, and excluding the failure of the device under test according to the stability judgment result of the classified sample device to complete verification of the current carrying capacity.

[0076] Further, the current carrying capacity detection system of the COOLMOS is also used for: based on the sample device set, obtaining a corresponding relative safety margin set; extracting a corresponding relative safety margin from the relative safety margin set according to the sample device; and judging the margin carrying of the current carrying limit through the relative safety margin to obtain a stability judgment result.

[0077] Further, the current carrying capacity detection system of the COOLMOS is also used for: extracting an extreme abnormal sample device from the sample device set for unpacking and microscopic observation to obtain a sample defect result; and performing comparison analysis based on the sample defect result and the failure device under test to judge the failure of the device under test according to a preset comparison similarity.

[0078] Further, the current carrying capacity detection system of the COOLMOS is also used for: based on the association relationship, normalizing mapping the related characteristic dimensions of the current carrying capacity in the association relationship, generating a three-dimensional thermal-electric critical analysis model; visualizing the three-dimensional thermal-electric critical analysis model as a three-dimensional temperature rise-voltage surface graph, and marking the current carrying capacity.

[0079] The various embodiments in the specification are described in a progressive manner, and each embodiment focuses on the difference from other embodiments. The COOLMOS current carrying capacity detection method and specific examples in the first embodiment are also applicable to the COOLMOS current carrying capacity detection system of the present embodiment. Through the foregoing detailed description of the COOLMOS current carrying capacity detection method, those skilled in the art can clearly understand the COOLMOS current carrying capacity detection system of the present embodiment. Therefore, in order to make the specification concise, the COOLMOS current carrying capacity detection system will not be described in detail.

[0080] The above description of the disclosed embodiments enables a person skilled in the art to implement or use the present application. Various modifications to the embodiments will be apparent to those skilled in the art, and the general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to the embodiments shown herein, but will conform to the widest scope consistent with the principles and novel features disclosed herein.

[0081] Obviously, those skilled in the art can make various modifications and variations to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the present application and its equivalents, the present application also intends to include these modifications and variations.

Claims

1. A method for detecting the current carrying capacity of a COOLMOS transistor, characterized in that, include: The association relationship is obtained by associating the thermal image set acquired by the device under test with the electrical characteristic dataset. This includes synchronously acquiring the electrical characteristic dataset corresponding to the thermal image set, associating the electrical characteristic dataset with the thermal image set, and obtaining the association relationship. The current critical point is obtained by analyzing the primary and secondary decision points of the current based on the correlation relationship and by weighted fusion. Specifically, based on the thermal image set, the evolution trend analysis of the temperature curve under the change of current is performed, and the temperature trend is linearly fitted and the abrupt change inflection point analysis is performed to extract the trend inflection point as the primary decision point of the current critical point. Perform abrupt change analysis on the voltage dataset versus the current dataset in the electrical characteristic dataset to obtain the voltage abrupt change points in the current dataset; Perform a nonlinear growth analysis on the current dataset in the electrical characteristic dataset to obtain the nonlinear growth point of the current dataset; Perform waveform glitches analysis on the current dataset to obtain the current jitter points in the current dataset; Perform waveform drop analysis on the current dataset to obtain the current interruption points in the current dataset; The voltage abrupt change point, nonlinear growth point, current jitter point, and current interruption point are combined to form the current critical auxiliary judgment point; Based on the current critical point, points where hotspots that have not yet run out of control are extracted from the correlation relationship and used as current carrying capacity.

2. The current carrying capacity detection method for COOLMOS as described in claim 1, characterized in that, include: The device under test is fixed to a circuit board to enable electrical conduction, wherein the circuit board has a thermal window; Apply current to the device under test after it is turned on; An infrared thermal imager is placed above the device under test to perform thermal imaging of the surface temperature of the device under test after applying current, and a thermal image set is obtained. Simultaneously acquire the electrical characteristic dataset corresponding to the thermal image set, associate the electrical characteristic dataset with the thermal image set, and obtain the association relationship.

3. The current carrying capacity detection method for COOLMOS as described in claim 1, characterized in that, include: Hot spot area, temperature gradient, and average temperature are extracted from the temperature feature parameters of the thermal image set. Based on the aforementioned correlation, abrupt changes in temperature characteristic parameters with respect to the current dataset in the electrical characteristic dataset are analyzed to obtain the temperature rise abrupt change points of the current dataset. Based on the aforementioned correlation, a sudden increase analysis of the hot spot area with the current dataset in the electrical characteristic data set is performed to obtain the hot spot sudden increase point in the current dataset. Based on the aforementioned correlation, abrupt changes in the temperature gradient with respect to the current dataset within the electrical characteristic dataset are analyzed to obtain the gradient abrupt change points of the current dataset. The temperature rise abrupt change point, hot spot abrupt increase point, and gradient abrupt change point are combined to form the current critical main determination point.

4. The current carrying capacity detection method for COOLMOS as described in claim 1, characterized in that, include: Repeat the test on the same model of the device under test under the same test conditions to obtain the current carrying capacity of the sample. Based on the current carrying capacity of the sample and the current carrying capacity, it is determined whether the current carrying limit is stable. If it is determined to be unstable, the sample device is classified and analyzed. Based on the stability judgment results of the classified sample devices, the failure of the device under test is excluded, and the current carrying capacity is verified.

5. The current carrying capacity detection method for COOLMOS as described in claim 4, characterized in that, include: Based on the sample device set, obtain the corresponding set of relative safety margins; Based on the sample device, extract the corresponding relative safety margin from the relative safety margin set; The current carrying capacity is judged by the relative safety margin, and the stability judgment result is obtained.

6. The current carrying capacity detection method for COOLMOS as described in claim 5, characterized in that, include: Extremely anomalous sample devices are extracted from the sample device set, depackaged, and observed under a microscope to obtain sample defect results; Based on the sample defect results, a comparative analysis is performed with the failed device under test, and the device under test is judged to be failed according to the preset comparison similarity.

7. The current carrying capacity detection method for COOLMOS as described in claim 1, characterized in that, include: Based on the aforementioned correlation, the relevant feature dimensions of current carrying capacity in the correlation are normalized and mapped to generate a three-dimensional thermo-electric critical analysis model. The three-dimensional thermo-electric critical analysis model is visualized and output as a three-dimensional temperature rise-voltage surface plot to indicate the current carrying capacity.

8. A current carrying capacity detection system for COOLMOS, characterized in that, The steps for implementing the current carrying capacity detection method for a COOLMOS according to any one of claims 1 to 7 include: The correlation module is used to obtain correlation relationships by associating the thermal image set acquired from the device under test with the electrical characteristic dataset; The current critical point acquisition module is used to perform current critical main and auxiliary judgment point analysis through the correlation relationship, and obtain the current critical point by weighted fusion. The current carrying capacity module is used to extract, based on the current critical point, points where hotspots that have not yet run out of control in the near future from the correlation relationship, as the current carrying capacity.

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