Power device storage temperature calculation method and device, electronic equipment and storage medium

Abstract

The invention provides a power device storage temperature calculation method, which comprises the following steps: simulating a preset power device model to obtain first simulation data; and obtaining a weak area of the power device according to the first simulation data. And establishing a sub-model for the weak area, and simulating the sub-model according to the first simulation data to obtain second simulation data. And comparing the second simulation data with pre-acquired device performance degradation information, and calculating the storage temperature of the power device according to the sub-model in response to successful comparison between the second simulation data and the device performance degradation information. Therefore, accurate calculation of the storage temperature of the power device is realized.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of component performance detection, and in particular to a power device storage temperature calculation method and device, electronic equipment and a storage medium. BACKGROUND

[0002] At present, semiconductor devices are applied to various electronic products and equipment. Different devices have different requirements for device reliability due to different use environments. Natural heat diffusion under high-temperature storage environment will cause the temperature of the internal material of the power device to continuously increase, the internal crystal of the metallization layer and the bonding point to continuously generate thermal stress, and the crystal to deform, such as dislocation, slip and twist, so that the internal stress of the metallization layer and the bonding point causes deformation, changes the surface morphology of the metallization layer, reduces the effective connection area of the corresponding metallization layer, and causes electrical performance degradation. At the same time, the effective connection area of the bonding wire will also be reduced, thereby accelerating the peeling or cracking of the bonding wire, resulting in failure of the power device.

[0003] In the actual storage test process, the commonly used method for power device performance detection is to directly give a conclusion through electrical performance test.

[0004] The current analysis method has some defects. For power devices under high-temperature storage environment, direct electrical performance test cannot accurately give the electrical performance degradation rule. The suitable storage temperature and electrical performance degradation curve need to be obtained through multiple tests for fitting, which consumes a lot of time. At the same time, the internal failure mechanism of the power device under high-temperature storage environment cannot be obtained through electrical test, and the internal weak link and thermal stress sensitive position cannot be accurately obtained. SUMMARY

[0005] Therefore, the present application aims to provide a power device storage temperature calculation method and device, electronic equipment and a storage medium.

[0006] In order to achieve the above purpose, the present application provides a power device storage temperature calculation method, which comprises: simulating a preset power device model to obtain first simulation data. Obtaining the weak area of the power device according to the first simulation data. Establishing a sub-model for the weak area, simulating the sub-model according to the first simulation data to obtain second simulation data. Comparing the second simulation data with the pre-acquired device performance degradation information, and in response to the successful comparison of the second simulation data and the device performance degradation information, calculating the storage temperature of the power device according to the sub-model.

[0007] In some embodiments, the sub-models include a first-level sub-model and a second-level sub-model. The sub-models are established for the weak area, and simulation is performed on the sub-models according to the first simulation data to obtain second simulation data, specifically including: simulation is performed on the first-level sub-model according to the first simulation data to obtain third simulation data; and simulation is performed on the second-level sub-model according to the third simulation data to obtain the second simulation data.

[0008] In some embodiments, the first simulation data includes first temperature field data and first stress data. The simulation is performed on the first-level sub-model according to the first simulation data, specifically including: heat transfer simulation is performed on the first-level sub-model according to the first temperature field data to obtain third temperature field data; and thermodynamic simulation is performed on the first-level sub-model according to the third temperature field data and the first stress data to obtain third stress data.

[0009] In some embodiments, the second-level sub-model is a polycrystal model. The sub-models are established for the weak area, further including: the first-level sub-model is constructed according to a preset mesh refinement algorithm; and the second-level sub-model is constructed according to the first-level sub-model and a Voronoi method.

[0010] In some embodiments, the sub-models further include a third-level sub-model. The sub-models are established for the weak area, and simulation is performed on the sub-models according to the first simulation data to obtain second simulation data, further including: simulation is performed on the third-level sub-model according to the first simulation data to obtain fourth simulation data; and simulation is performed on the first-level sub-model according to the fourth simulation data to obtain the third simulation data.

[0011] A power device storage temperature calculation apparatus includes: a first simulation module configured to simulate a preset power device model to obtain first simulation data; a simulation result analysis module configured to obtain a weak area of the power device according to the first simulation data; a second simulation module configured to establish sub-models for the weak area, and perform simulation on the sub-models according to the first simulation data to obtain second simulation data; and a storage temperature calculation module configured to compare the second simulation data with pre-acquired device performance degradation information, and in response to a successful comparison of the second simulation data with the device performance degradation information, calculate a storage temperature of the power device according to the sub-models.

[0012] In some embodiments, the sub-models include a first-level sub-model and a second-level sub-model. The second simulation module specifically includes: a third simulation module configured to simulate the first-level sub-model according to the first simulation data to obtain third simulation data; and simulate the second-level sub-model according to the third simulation data to obtain the second simulation data.

[0013] In some embodiments, the first simulation data comprises first temperature field data and first stress data. The third simulation module specifically comprises: performing heat transfer simulation on the first-level sub-model according to the first temperature field data to obtain third temperature field data. Performing thermodynamic simulation on the first-level sub-model according to the heat transfer simulation data and the first stress data to obtain third stress data.

[0014] An electronic device comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, the processor implementing the method of any of the above when executing the program.

[0015] A non-transitory computer-readable storage medium storing computer instructions for causing a computer to execute the method of any of the above.

[0016] As can be seen from the above, the power device storage temperature calculation method provided by the application can obtain the weak position of the power device by modeling and simulating the power device and analyzing the first simulation data, so as to perform focused analysis on the part of the power device that degrades the fastest in performance, and use the part as a standard for judging whether the power device is failed. A sub-model is established for the weak area of the power device, so that focused simulation analysis can be performed on the area. Finally, the simulation parameters are calibrated according to the pre-acquired experimental data, so that accurate simulation of the power device is realized, and the best storage temperature of the power device is obtained. BRIEF DESCRIPTION OF DRAWINGS

[0017] In order to more clearly illustrate the technical solutions in the application or related art, the following will briefly introduce the drawings needed to be used in the embodiments or related art descriptions. Obviously, the drawings in the following description are only embodiments of the application, and for those skilled in the art, other drawings can also be obtained without creative labor.

[0018] Figure 1 A flowchart of a power device storage temperature calculation method provided by an embodiment of the application; Figure 2 A structural diagram of a power device storage temperature calculation device provided by an embodiment of the application; Figure 3 A more specific hardware structure diagram of an electronic device provided by an embodiment of the application. DETAILED DESCRIPTION

[0019] In order to make the purpose, technical solutions and advantages of the application clearer, the following will further describe the application in combination with specific embodiments and with reference to the drawings.

[0020] It should be noted that, unless otherwise defined, technical terms or scientific terms used in the embodiments of the present application shall have the common meaning understood by one of ordinary skill in the art to which the present application belongs. The terms "first", "second", and similar terms used in the embodiments of the present application do not denote any order, quantity, or importance, but are merely used to distinguish different components. The terms "include", "contain", and similar terms mean that the elements or objects before the terms encompass the elements or objects listed after the terms and their equivalents, and do not exclude other elements or objects. The terms "connect" or "connected" and similar terms do not mean physical or mechanical connection, but can include electrical connection, whether direct or indirect. The terms "upper", "lower", "left", "right", and the like are merely used to represent relative positional relationships, and when the absolute positions of the described objects change, the relative positional relationships can also change accordingly.

[0021] As shown in Figure 1 The present application provides a power device storage temperature calculation method, comprising: Step S1, simulating a preset power device model to obtain first simulation data.

[0022] In the embodiment, the power device can be an LDO device. The power device can be three-dimensionally modeled by referring to a device manual, the model material parameters are given according to the power device specifications and experience parameters, the model is meshed and the mesh independence is verified. In the simulation process, the areas with faster performance degradation can be meshed more carefully, and the mesh size of the areas with slower performance degradation can be appropriately enlarged, so that the calculation amount required in the simulation process can be reduced as much as possible under the premise of ensuring the simulation accuracy.

[0023] As an optional implementation, the areas with more serious performance degradation due to power device aging are usually between the Al metallization layer and the connected Al bonding wire, so a more fine mesh needs to be set at this position. The mesh division mode of scanning is selected, which can select fine mesh division for the key structures Al bonding wire and Al metallization layer of interest, and can appropriately increase the mesh size of the auxiliary structures such as Cu substrate and reduce the number of units.

[0024] Step S2, obtaining a weak area of the power device according to the first simulation data.

[0025] Wherein, the weak position of the power device is obtained by modeling and simulating the power device and analyzing the first simulation data, so that the part with the fastest performance degradation of the power device can be analyzed as the standard for judging whether the power device fails.

[0026] Step S3: Establish a sub-model for the weak area, and simulate the sub-model based on the first simulation data to obtain the second simulation data.

[0027] By establishing sub-models in the weak areas of power devices, focused simulation analysis of these areas can be performed. However, focusing on a specific area requires refining the finite element simulation mesh. Global mesh refinement, however, significantly increases computational load, costs, and efficiency. Therefore, this application employs a sub-model method to extract key areas of interest on the Al metallization layer and refine their meshes, improving computational efficiency and accuracy. Furthermore, excessive mesh refinement in the global model can lead to a transition from coarse to fine meshes, reducing computational accuracy. Additionally, the finite element mesh generation process can easily produce numerous intermediate meshes with uneven shapes and sizes, affecting model accuracy. The sub-model method avoids this problem.

[0028] In the simulation of power devices, the surface roughness of the metallization layer in the power device is simulated, and the simulation parameters are calibrated based on the experimental data obtained in advance, so as to achieve accurate simulation of the power device and obtain the optimal storage temperature of the power device.

[0029] Step S4: Compare the second simulation data with the pre-acquired device performance degradation information. In response to the successful comparison between the second simulation data and the device performance degradation information, calculate the storage temperature of the power device based on the sub-model.

[0030] As an alternative implementation method, in order to quickly obtain the performance degradation data of power devices, the performance degradation information of power devices can be obtained by conducting aging tests on the power devices.

[0031] Specifically, a single high-temperature storage environment test was conducted on the power device, subjecting the entire device to a temperature of 175°C for 1000 hours. Electrical performance testing was performed to obtain the degradation parameters of the LDO device (ceramic package, internal structure from top to bottom: aluminum bonding wires, aluminum metallization layer, silicon chip, silver solder paste, copper substrate; the metallization layer is a sputtered layer on the chip surface, used to provide conductive paths between upper and lower layers). Resistance values ​​were measured before and after the test, and every 100 hours during the test; a trend curve of resistance R versus time t was plotted. Surface roughness was measured every 100 hours using a surface roughness tester to measure the overall surface height of the metallization layer, and the arithmetic mean surface roughness Sa was calculated using the following formula: The degradation relationship between the two (roughness and resistance change trend curve) was obtained.

[0032] During the aging test, EBSD samples were obtained by slicing the metallization layer on the device surface before and after the experiment. The internal crystal orientation transformation of the metallization layer was obtained through EBSD images, revealing the device degradation mechanism caused by internal crystal creep. (Statistical analysis of the crystal orientations within the metallization layer was performed using Euler angles from EBSD images before and after the experiment.) (Represented), the orientation difference of each adjacent crystal is calculated and the average value of all crystal orientation differences is taken. Finally, combined with the resistance test results, the crystal orientation difference-resistance trend curve is obtained. This curve is used as the power device performance degradation information to correct the simulation model.

[0033] In some embodiments, the sub-model includes a first-level sub-model and a second-level sub-model. Step S3 specifically includes: Step S31: Simulate the first-level sub-model based on the first simulation data to obtain the third simulation data.

[0034] Step S32: Simulate the secondary sub-model based on the third simulation data to obtain the second simulation data.

[0035] In this embodiment, detailed modeling and analysis of the roughness deformation of the surface Al metallization layer are required. Using a fine-grained mesh globally would result in extremely high computational costs and low efficiency. Therefore, a sub-model approach is adopted to extract key areas of interest on the Al metallization layer and perform fine-grained meshing, improving computational efficiency and accuracy. Furthermore, excessive mesh refinement in the global model can lead to a transition from coarse to fine mesh, reducing computational accuracy. Additionally, finite element meshing can easily generate numerous intermediate meshes with uneven shapes and sizes, affecting model accuracy. Therefore, the sub-model method is used to avoid this problem. By setting up multi-level sub-models, the first-level sub-model is simulated based on the first simulation data, and then the second-level sub-model is simulated based on the simulation results of the first-level sub-model. This allows for a smoother transition in the mesh generation of the simulation model. For example, the mesh size of the first-level sub-model can be set between that of the power device model and the second-level sub-model.

[0036] In some embodiments, the first simulation data includes first temperature field data and first stress data.

[0037] Step S31 specifically includes: Step S311: Perform heat transfer simulation on the first-level sub-model based on the first temperature field data to obtain the third temperature field data.

[0038] Step S312: Perform thermodynamic simulation on the first-level sub-model based on the third temperature field and the first stress data to obtain the third stress data.

[0039] In this embodiment, by performing heat transfer and thermodynamic simulations on the first-level sub-model based on the first temperature field data and the first stress data, the simulation results from the previous step can be used for simulation, thereby reducing the amount of simulation computation.

[0040] As an optional implementation, step S31 may further include: Step S313: Perform heat transfer simulation on the secondary sub-model based on the third temperature field data to obtain the second temperature field data.

[0041] Step S314: Perform thermodynamic simulation on the secondary sub-model based on the second temperature field and the third stress data to obtain the second stress data.

[0042] In some embodiments, the second-order sub-model is a polycrystalline model.

[0043] Step S3 also includes: Step S301: Construct a first-level sub-model according to a preset mesh refinement algorithm.

[0044] Step S303: Construct a second-level sub-model based on the first-level sub-model and the Voronoi method.

[0045] In this embodiment, based on Saint-Venant's principle, the boundary region of the established sub-model is at least twice the distance from the location of interest, and the number of meshes in the region of interest differs by 3-5 times. The Voronoi algorithm is used to establish the three-dimensional polycrystalline geometric topology. This method can accurately describe the geometric characteristics of polycrystalline structures, while also offering fast modeling speed and high efficiency. The Voronoi method is combined with Python scripts to establish the polycrystalline model in ABAQUS finite element software. According to the general definition of a Voronoi diagram, in a two-dimensional plane, let P be a set of n discrete points on the plane, i.e., p1, p2, ..., pn ∈ P, and define V(pi) satisfying... In the formula, d(x, pi) is the Euclidean distance between points x and pi. Then V(pi) is the Voronoi polygon of point pi. The randomly generated discrete point pi is called the generator of the Voronoi diagram, and each Voronoi polygon can represent a grain. The coordinate values ​​of the Voronoi diagram generated by MATLAB are saved as a data file in a certain order. Then, the vertex data file is read in using a Python scripting language, and the vertices are connected in order to generate a polyhedron. Finally, the geometric modeling of the polycrystalline structure is realized through the interface between ABAQUS / CAE and Python. A Fortran crystal subroutine is written based on the elastic-plastic-creep theory of crystals, making the total strain increment = elastic strain + plastic strain + creep strain, with stress updated only by elastic strain, reflecting the creep relaxation effect. The elastic-plastic-creep parameters of the aluminum crystal and the crystal orientation obtained in step two are input into the user-defined material parameters. Based on the sub-model method, the crystal model is used as a sub-model for calculation of the LDO device. The output of step four is used as the input condition for loading. The simulation simulates the deformation of polycrystalline dislocations, slip, torsion, etc. of the metallization layer on the device surface under high temperature storage environment. The roughness change of the metallization layer is obtained through sub-model simulation. The surface roughness change during the simulation is statistically analyzed to obtain the corresponding curve of storage time-surface roughness.

[0046] In some embodiments, the sub-model further includes a tertiary sub-model.

[0047] Step S3 also includes: Step S305: Simulate the third-level sub-model based on the first simulation data to obtain the fourth simulation data.

[0048] Step S307: Simulate the first-level sub-model based on the fourth simulation data to obtain the third simulation data.

[0049] As can be seen from the above embodiments of this application, this application models and simulates power devices, analyzes the first simulation data to obtain the weak points of the power devices, and thus focuses on analyzing the parts of the power devices whose performance degrades the fastest, using this as a standard to determine whether the power devices have failed. Then, a sub-model is established for the weak areas of the power devices, allowing for focused simulation analysis of these areas. Finally, the simulation parameters are calibrated based on pre-acquired experimental data, thereby achieving accurate simulation of the power devices and obtaining the optimal storage temperature for the power devices.

[0050] It should be noted that the method in this embodiment can be executed by a single device, such as a computer or server. The method can also be applied in a distributed scenario, where multiple devices cooperate to complete the task. In such a distributed scenario, one of these devices may execute only one or more steps of the method in this embodiment, and the multiple devices will interact with each other to complete the method described.

[0051] It should be noted that the above description describes some embodiments of this application. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recorded in the claims can be performed in a different order than that shown in the above embodiments and still achieve the desired result. Furthermore, the processes depicted in the drawings do not necessarily require a specific or sequential order to achieve the desired result. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0052] Based on the same inventive concept, corresponding to any of the above embodiments, this application also provides a power device storage temperature calculation device.

[0053] refer to Figure 2 A power device storage temperature calculation device, comprising: The first simulation module 100 is used to simulate a preset power device model to obtain the first simulation data.

[0054] The simulation result analysis module 200 is used to obtain the weak areas of the power device based on the first simulation data.

[0055] The second simulation module 300 is used to establish a sub-model for the weak area, and to simulate the sub-model based on the first simulation data to obtain the second simulation data.

[0056] The storage temperature calculation module 400 is used to compare the second simulation data with the pre-acquired device performance degradation information. In response to the successful comparison between the second simulation data and the device performance degradation information, the storage temperature of the power device is calculated based on the sub-model.

[0057] In some embodiments, the sub-model includes a first-level sub-model and a second-level sub-model. The second simulation module specifically includes: The third simulation module is used to simulate the first-level sub-model based on the first simulation data to obtain the third simulation data.

[0058] The second simulation data is obtained by simulating the second-level sub-model based on the third simulation data.

[0059] In some embodiments, the first simulation data includes first temperature field data and first stress data.

[0060] The third simulation module specifically includes: Heat transfer simulation was performed on the first-level sub-model based on the first temperature field data to obtain the third temperature field data.

[0061] Thermodynamic simulation was performed on the first-level sub-model based on the heat transfer simulation data and the first stress data to obtain the third stress data.

[0062] For ease of description, the above devices are described in terms of function, divided into various modules. Of course, in implementing this application, the functions of each module can be implemented in one or more software and / or hardware.

[0063] The apparatus described above is used to implement the corresponding power device storage temperature calculation method in any of the foregoing embodiments, and has the beneficial effects of the corresponding method embodiments, which will not be repeated here.

[0064] Based on the same inventive concept, corresponding to the methods of any of the above embodiments, this application also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the power device storage temperature calculation method described in any of the above embodiments.

[0065] Figure 3 This embodiment illustrates a more specific hardware structure of an electronic device, which may include a processor 1010, a memory 1020, an input / output interface 1030, a communication interface 1040, and a bus 1050. The processor 1010, memory 1020, input / output interface 1030, and communication interface 1040 are interconnected internally via the bus 1050.

[0066] The processor 1010 can be implemented using a general-purpose CPU (Central Processing Unit), microprocessor, application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of this specification.

[0067] The memory 1020 can be implemented in the form of ROM (Read Only Memory), RAM (Random Access Memory), static storage device, dynamic storage device, etc. The memory 1020 can store the operating system and other applications. When the technical solutions provided in the embodiments of this specification are implemented by software or firmware, the relevant program code is stored in the memory 1020 and is called and executed by the processor 1010.

[0068] The input / output interface 1030 is used to connect input / output modules to realize information input and output. Input / output modules can be configured as components within the device (not shown in the figure) or externally connected to the device to provide corresponding functions. Input devices may include keyboards, mice, touchscreens, microphones, various sensors, etc., while output devices may include displays, speakers, vibrators, indicator lights, etc.

[0069] The communication interface 1040 is used to connect a communication module (not shown in the figure) to enable communication between this device and other devices. The communication module can communicate via wired means (such as USB, Ethernet cable, etc.) or wireless means (such as mobile network, WIFI, Bluetooth, etc.).

[0070] Bus 1050 includes a pathway for transmitting information between various components of the device, such as processor 1010, memory 1020, input / output interface 1030, and communication interface 1040.

[0071] It should be noted that although the above-described device only shows the processor 1010, memory 1020, input / output interface 1030, communication interface 1040, and bus 1050, in specific implementations, the device may also include other components necessary for normal operation. Furthermore, those skilled in the art will understand that the above-described device may only include the components necessary for implementing the embodiments of this specification, and not necessarily all the components shown in the figures.

[0072] The electronic devices described above are used to implement the corresponding power device storage temperature calculation method in any of the foregoing embodiments, and have the beneficial effects of the corresponding method embodiments, which will not be repeated here.

[0073] Based on the same inventive concept, corresponding to the methods of any of the above embodiments, this application also provides a non-transitory computer-readable storage medium storing computer instructions for causing the computer to execute the power device storage temperature calculation method as described in any of the above embodiments.

[0074] The computer-readable medium of this embodiment includes permanent and non-permanent, removable and non-removable media, and information storage can be implemented by any method or technology. Information can be computer-readable instructions, data structures, program modules, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic magnetic disk storage or other magnetic storage devices, or any other non-transfer medium that can be used to store information accessible by a computing device.

[0075] The computer instructions stored in the storage medium of the above embodiments are used to cause the computer to execute the power device storage temperature calculation method as described in any of the above embodiments, and have the beneficial effects of the corresponding method embodiments, which will not be repeated here.

[0076] It should be noted that the embodiments of this application can also be further described in the following ways: A method for calculating the storage temperature of power devices, comprising: The preset power device model is simulated to obtain the first simulation data; The weak areas of the power device are obtained based on the first simulation data; A sub-model is established for the weak area, and the sub-model is simulated based on the first simulation data to obtain the second simulation data; The second simulation data is compared with the pre-acquired device performance degradation information. In response to the successful comparison between the second simulation data and the device performance degradation information, the storage temperature of the power device is calculated based on the sub-model.

[0077] Optionally, the sub-model includes a first-level sub-model and a second-level sub-model; the step of establishing a sub-model for the weak region, and simulating the sub-model based on the first simulation data to obtain second simulation data, specifically includes: The first-level sub-model is simulated based on the first simulation data to obtain the third simulation data; The second simulation data is obtained by simulating the second-level sub-model based on the third simulation data.

[0078] Optionally, the first simulation data includes first temperature field data and first stress data; The step of simulating the first-level sub-model based on the first simulation data specifically includes: Based on the first temperature field data, heat transfer simulation is performed on the first-level sub-model to obtain the third temperature field data; Thermodynamic simulation is performed on the first-level sub-model based on the third temperature field data and the first stress data to obtain the third stress data.

[0079] Optionally, the second-level sub-model is a polycrystalline model; The process of establishing a sub-model for the weak region also includes: The first-level sub-model is constructed according to a preset mesh refinement algorithm; The second-level sub-model is constructed based on the first-level sub-model and the Voronoi method.

[0080] Optionally, the sub-model may further include a three-level sub-model; The step of establishing a sub-model for the weak area, simulating the sub-model based on the first simulation data to obtain second simulation data, further includes: The third-level sub-model is simulated based on the first simulation data to obtain the fourth simulation data; The third simulation data is obtained by simulating the first-level sub-model based on the fourth simulation data.

[0081] A power device storage temperature calculation device, comprising: The first simulation module is used to simulate a preset power device model and obtain the first simulation data. The simulation result analysis module is used to determine the weak areas of the power device based on the first simulation data. The second simulation module is used to establish a sub-model for the weak area, and to simulate the sub-model based on the first simulation data to obtain the second simulation data. The storage temperature calculation module is used to compare the second simulation data with the pre-acquired device performance degradation information. In response to the successful comparison between the second simulation data and the device performance degradation information, the module calculates the storage temperature of the power device based on the sub-model.

[0082] Optionally, the sub-model includes a first-level sub-model and a second-level sub-model; the second simulation module specifically includes: The third simulation module is used to simulate the first-level sub-model based on the first simulation data to obtain the third simulation data. The second simulation data is obtained by simulating the second-level sub-model based on the third simulation data.

[0083] Optionally, the first simulation data includes first temperature field data and first stress data; The third simulation module specifically includes: Based on the first temperature field data, heat transfer simulation is performed on the first-level sub-model to obtain the third temperature field data; Thermodynamic simulation is performed on the first-level sub-model based on the heat transfer simulation data and the first stress data to obtain the third stress data.

[0084] An electronic device includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the program, implements the method as described in any of the preceding descriptions.

[0085] A non-transitory computer-readable storage medium storing computer instructions for causing a computer to perform any of the methods described above.

[0086] Those skilled in the art should understand that the discussion of any of the above embodiments is merely exemplary and is not intended to imply that the scope of this application (including the claims) is limited to these examples; within the framework of this application, the technical features of the above embodiments or different embodiments can also be combined, the steps can be implemented in any order, and there are many other variations of different aspects of the embodiments of this application as described above, which are not provided in the details for the sake of brevity.

[0087] Additionally, to simplify the description and discussion, and to avoid obscuring the embodiments of this application, the well-known power / ground connections to integrated circuit (IC) chips and other components may or may not be shown in the provided drawings. Furthermore, the apparatus may be shown in block diagram form to avoid obscuring the embodiments of this application, and this also takes into account the fact that the details of the implementation of these block diagram apparatuses are highly dependent on the platform on which the embodiments of this application will be implemented (i.e., these details should be fully understood by those skilled in the art). While specific details (e.g., circuits) have been set forth to describe exemplary embodiments of this application, it will be apparent to those skilled in the art that the embodiments of this application can be implemented without these specific details or with variations thereof. Therefore, these descriptions should be considered illustrative rather than restrictive.

[0088] Although this application has been described in conjunction with specific embodiments thereof, many substitutions, modifications, and variations of these embodiments will be apparent to those skilled in the art from the foregoing description. For example, other memory architectures (e.g., dynamic RAM (DRAM)) may be used with the embodiments discussed.

[0089] The embodiments of this application are intended to cover all such substitutions, modifications, and variations that fall within the broad scope of the appended claims. Therefore, any omissions, modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the embodiments of this application should be included within the protection scope of this application.

Claims

1. A power device storage temperature calculation method, comprising: simulating a preset power device model to obtain first simulation data; obtaining a weak area of the power device according to the first simulation data; establishing a sub-model for the weak area, simulating the sub-model according to the first simulation data to obtain second simulation data; comparing the second simulation data with pre-acquired device performance degradation information, and in response to a successful comparison between the second simulation data and the device performance degradation information, calculating a storage temperature of the power device according to the sub-model.

2. The power device storage temperature calculation method according to claim 1, wherein The sub-model comprises a first-level sub-model and a second-level sub-model. The step of establishing a sub-model for the weak area, simulating the sub-model according to the first simulation data to obtain second simulation data specifically comprises: simulating the first-level sub-model according to the first simulation data to obtain third simulation data; simulating the second-level sub-model according to the third simulation data to obtain the second simulation data.

3. The power device storage temperature calculation method according to claim 2, wherein, The first simulation data comprises first temperature field data and first stress data. The step of simulating the first-level sub-model according to the first simulation data specifically comprises: performing heat transfer simulation on the first-level sub-model according to the first temperature field data to obtain third temperature field data; performing thermodynamic simulation on the first-level sub-model according to the third temperature field data and the first stress data to obtain third stress data.

4. The power device storage temperature calculation method according to claim 2, wherein The second-level sub-model is a polycrystal model. The step of establishing a sub-model for the weak area further comprises: constructing the first-level sub-model according to a preset mesh refinement algorithm; constructing the second-level sub-model according to the first-level sub-model and a Voronoi method.

5. The power device storage temperature calculation method according to claim 2, wherein The sub-model further comprises a third-level sub-model. The step of establishing a sub-model for the weak area, simulating the sub-model according to the first simulation data to obtain second simulation data further comprises: simulating the third-level sub-model according to the first simulation data to obtain fourth simulation data; simulating the first-level sub-model according to the fourth simulation data to obtain the third simulation data. 6.A power device storage temperature calculation apparatus, comprising: a first simulation module configured to simulate a preset power device model to obtain first simulation data; a simulation result analysis module configured to obtain a weak area of the power device according to the first simulation data; a second simulation module configured to establish a sub-model for the weak area, simulate the sub-model according to the first simulation data to obtain second simulation data; a storage temperature calculation module configured to compare the second simulation data with pre-acquired device performance degradation information, and in response to a successful comparison between the second simulation data and the device performance degradation information, calculate a storage temperature of the power device according to the sub-model.

7. The power device storage temperature calculation apparatus according to claim 6, wherein The sub-model comprises a first-level sub-model and a second-level sub-model. The second simulation module specifically comprises: a third simulation module configured to simulate the first-level sub-model according to the first simulation data to obtain third simulation data; The second simulation data is obtained by simulating the second simulation data according to the third simulation data.

8. The power device storage temperature calculation apparatus according to claim 6, wherein The first simulation data comprises first temperature field data and first stress data; The third simulation module specifically comprises: The third temperature field data is obtained by simulating heat transfer of the first simulation data according to the first temperature field data; The third stress data is obtained by simulating thermodynamics of the first simulation data according to the first stress data. 9.An electronic device comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, wherein the processor implements the method of any one of claims 1 to 5 when executing the program. 10.A non-transitory computer readable storage medium storing computer instructions for causing a computer to execute the method of any one of claims 1 to 5.

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