Method for determining cohesion model parameters and related device

Abstract

The invention provides a method for determining cohesion model parameters. The method comprises the steps of obtaining a to-be-analyzed structure; performing a thrust shear test on the to-be-analyzed structure to obtain a load displacement test curve of the to-be-analyzed structure; inputting the load displacement test curve into a pre-trained cohesion model parameter prediction model to obtain cohesion model parameters of the to-be-analyzed structure; wherein the structure to be analyzed is a structure comprising a bonding interconnection layer. According to the method, the thrust shear test is performed on the to-be-analyzed structure to obtain the load displacement test curve of the to-be-analyzed structure, and the cohesion model parameters of the to-be-analyzed structure can be obtained by inputting the load displacement curve into the pre-trained cohesion model parameter prediction model, so that rapid determination of the cohesion model parameters is realized; the determination difficulty of cohesion model parameters is reduced, and the determination efficiency is improved.

Description

Technical Field

[0001] This application relates to the field of component analysis technology, and in particular to a method and related apparatus for determining cohesive force model parameters. Background Technology

[0002] The cohesive zone model (CZM) is a constitutive model of a material based on the deterministic law of traction separation, using fracture and failure criteria as the cohesive force. It allows for more convenient evaluation of the reliability of the adhesive interconnect layer between the chip and the metal substrate using energy methods. However, the establishment of the CZM model for the adhesive interconnect layer is affected by experimental factors, and the acquisition of CZM parameters requires iterative calculations and fitting of experimental test curves based on finite element simulations. This necessitates a large amount of numerical computation, increasing the difficulty of determining the CZM parameters. Summary of the Invention

[0003] In view of this, the purpose of this application is to propose a method and related apparatus for determining cohesive force model parameters, so as to solve the problem of increasing difficulty in determining the CZM parameters of adhesive interconnect layers.

[0004] To achieve the above objectives, this application provides a method for determining cohesive force model parameters, comprising: Obtain the structure to be analyzed; A thrust shear test was performed on the structure to be analyzed, and the load-displacement test curve of the structure to be analyzed was obtained. The load-displacement test curve is input into the pre-trained cohesive force model parameter prediction model to obtain the cohesive force model parameters of the structure to be analyzed. The structure to be analyzed is a structure containing an adhesive interconnect layer.

[0005] Furthermore, the pre-training process of the pre-trained cohesion model parameter prediction model includes: Obtain the analysis structure for pre-training; A thrust shear test was performed on the pre-training analysis structure to obtain the load-displacement training curve corresponding to the pre-training analysis structure. The cohesive model parameters corresponding to the pre-trained analysis structure are calculated based on the load-displacement training curve. Using the load-displacement training curve as input data and the cohesive force model parameters corresponding to the pre-trained analysis structure as input data, the neural network model is trained to obtain the cohesive force model parameter prediction model.

[0006] Furthermore, the calculation of the cohesive model parameters corresponding to the pre-trained analysis structure based on the load-displacement training curve includes: Based on the load-displacement training curve, determine multiple fracture energies of the pre-training analysis structure; The cohesive model parameters of the pre-trained analytical structure are obtained by simulation calculation based on the multiple fracture energies.

[0007] Furthermore, the simulation calculation based on the multiple fracture energies to obtain the cohesive model parameters of the pre-trained analysis structure includes: Iterative calculations were performed based on the multiple fracture energy and cohesion models to obtain the load-displacement simulation curves. The load-displacement training curve and the load-displacement simulation curve are fitted to obtain the cohesive force model parameters of the pre-trained analysis structure.

[0008] Furthermore, the step of obtaining the cohesive model parameters of the pre-trained analysis structure through simulation calculation based on the multiple fracture energies also includes: The stress variation curve and strain variation curve of the pre-training analysis structure are determined based on the cohesive model parameters of the pre-training analysis structure. The effectiveness of the cohesive force model parameters is verified based on the stress and strain variation curves.

[0009] Furthermore, the verification of the validity of the cohesive model parameters based on the stress change curve and strain change curve includes: The fracture time of the pre-training analysis structure is determined based on the strain change curve. The stress constant time of the pre-training analysis structure is determined based on the stress variation curve. In response to determining that the fracture time is the same as the stress constant time, the cohesive model parameters are determined to be valid.

[0010] Based on the same inventive concept, this application also provides an apparatus for determining cohesive force model parameters, comprising: The acquisition module is configured to acquire the structure to be analyzed. The testing module is configured to perform a thrust shear test on the structure to be analyzed and obtain the load-displacement test curve of the structure to be analyzed. The determination module is configured to input the load displacement test curve into a pre-trained cohesive force model parameter prediction model to obtain the cohesive force model parameters corresponding to the structure to be analyzed. The structure to be analyzed is a structure containing an adhesive interconnect layer.

[0011] Based on the same inventive concept, this application also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and running on the processor, wherein the processor executes the program to implement the method described above.

[0012] Based on the same inventive concept, this application also provides a non-transitory computer-readable storage medium that stores computer instructions for causing a computer to perform the method described above.

[0013] Based on the same inventive concept, this application also provides a computer program product, including computer program instructions, which, when run on a computer, cause the computer to perform the method described above.

[0014] As can be seen from the above, this application provides a method and related apparatus for determining cohesive force model parameters. The method includes acquiring the structure to be analyzed, conducting a thrust shear test on the structure to be analyzed to obtain a load-displacement test curve, and inputting the load-displacement test curve into a pre-trained cohesive force model parameter prediction model to obtain the cohesive force model parameters of the structure to be analyzed. The structure to be analyzed is a structure containing an adhesive interconnect layer. By conducting a thrust shear test on the structure to be analyzed to obtain the load-displacement test curve generated by the adhesive interconnect layer, and by inputting the load-displacement test curve into the pre-trained cohesive force model parameter prediction model to obtain the cohesive force model parameters of the structure to be analyzed, the cohesive force situation of the structure to be analyzed can be analyzed. This application obtains the load-displacement test curve of the structure to be analyzed by conducting a thrust shear test, and by inputting the load-displacement curve into the pre-trained cohesive force model parameter prediction model, the cohesive force model parameters of the structure to be analyzed can be obtained, realizing the rapid determination of cohesive force model parameters, which helps to reduce the difficulty of determining cohesive force model parameters and improve the determination efficiency. Attached Figure Description

[0015] To more clearly illustrate the technical solutions in this application or related technologies, the drawings used in the description of the embodiments or related technologies will be briefly introduced below. Obviously, the drawings described below are only embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0016] Figure 1 This is a flowchart illustrating a method for determining cohesive force model parameters according to an embodiment of this application; Figure 2 This is a schematic diagram of the load-displacement test curves according to an embodiment of this application; Figure 3 This is a schematic diagram of the stress variation curves in an embodiment of this application; Figure 4 This is a schematic diagram of the strain change curves of an embodiment of this application; Figure 5This is a schematic diagram of the structure of a device for determining cohesive force model parameters according to an embodiment of this application; Figure 6 This is a schematic diagram of an electronic device according to an embodiment of this application. Detailed Implementation

[0017] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with specific embodiments and the accompanying drawings.

[0018] It should be noted that, unless otherwise defined, the technical or scientific terms used in the embodiments of this application should have the ordinary meaning understood by one of ordinary skill in the art to which this application pertains. The terms "first," "second," and similar terms used in the embodiments of this application do not indicate any order, quantity, or importance, but are merely used to distinguish different components. Terms such as "comprising" or "including" mean that the element or object preceding the word encompasses the elements or objects listed after the word and their equivalents, without excluding other elements or objects. Terms such as "connected" or "linked" are not limited to physical or mechanical connections, but can include electrical connections, whether direct or indirect. Terms such as "upper," "lower," "left," and "right" are only used to indicate relative positional relationships; when the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0019] The chip and the metal substrate are connected by an adhesive interconnect layer, which is usually composed of solder paste or nano-sintered silver paste. The bonding quality of the adhesive interconnect layer is an important factor affecting the packaging quality of the component. Currently, shear strength, fracture energy and other indicators are usually used to evaluate the bonding reliability of the adhesive interconnect layer.

[0020] Chip thrust shear testing is widely used to test the shear strength of the bonded interconnect layers between chips and metal substrates, providing a quick and easy way to quantify the bond strength of these layers. However, this test cannot directly assess the shear fracture or failure behavior between the chip and the metal substrate. To study the shear fracture and fatigue mechanical behavior of bonded interconnect layers, it is often necessary to calibrate their fracture behavior and establish critical fracture conditions for the bonded interconnect layers, in order to more comprehensively clarify the causes of joint failure.

[0021] To study the shear fracture and fatigue mechanical behavior of bonded interconnects, it is often necessary to calibrate their fracture behavior and establish critical fracture conditions for the bonded interconnects in order to more comprehensively clarify the causes of failure. The cohesive model (CZM), based on the deterministic law of traction separation, uses fracture and failure criteria as the cohesive constitutive model of the material, making it easier to evaluate the reliability of bonded interconnects using energy methods. Based on the cohesive criterion, the CZM treats the fracture process in the cohesive region as a continuous and progressive damage process, analyzing the fracture and failure behavior of the material. Using the CZM, critical fracture conditions for bonded interconnects can be established, providing a more comprehensive explanation of the causes of fracture and failure in bonded interconnects, and evaluating and analyzing the interconnect reliability of bonded interconnects.

[0022] However, the establishment of the CZM model for the inversion analysis of the adhesive interconnect layer is affected by experimental factors, and the acquisition of CZM parameters requires repeated calculations and fitting of experimental test curves based on finite element simulation iterations, which requires a large amount of numerical calculation. These factors significantly affect the accuracy and efficiency of the inversion analysis of the cohesive model of the adhesive interconnect layer, increase the difficulty of acquiring CZM parameters during the analysis process, increase parameter dispersion, and thus affect the accurate assessment of the fracture and failure behavior of the adhesive layer.

[0023] Based on this, this application proposes a method and related apparatus for determining cohesive force model parameters, so as to simplify the determination of cohesive force model parameters of adhesive interconnect layers and thus improve the determination efficiency.

[0024] The embodiments of this application will be described in detail below with reference to the accompanying drawings.

[0025] In some embodiments, such as Figure 1 As shown, a method for determining cohesive force model parameters includes: Step S101: Obtain the structure to be analyzed, wherein the structure to be analyzed is a structure containing an adhesive interconnect layer.

[0026] Specifically, the structure to be analyzed is a structure comprising a chip, a metal substrate, and an adhesive interconnect layer, wherein the adhesive interconnect layer is located between the chip and the metal substrate to connect the chip and the metal substrate.

[0027] The structure to be analyzed is the structure containing the adhesive interconnect layer whose cohesive force model parameters are to be determined. The structure to be analyzed includes the adhesive interconnect layer and the chip and metal substrate connected by the adhesive interconnect layer.

[0028] Step S102: Perform a thrust shear test on the structure to be analyzed to obtain the load-displacement test curve of the structure to be analyzed; Specifically, a thrust shear test is used to test the shear strength of the structure to be analyzed, and the load and displacement comparison data during the test are recorded to obtain the load-displacement test curve of the structure to be analyzed.

[0029] It should be noted that the thrust shear test involves fixing the metal substrate of the structure to be analyzed and applying shear force to the chip of the structure to be analyzed. During the process, the displacement between the metal substrate and the chip is monitored. The shear force and displacement at the same moment correspond to a point of the load-displacement test curve. During the test, multiple points are included to form the load-displacement test curve.

[0030] For example, such as Figure 2 As shown, this is the load-displacement test curve, with the horizontal axis representing displacement and the vertical axis representing load (i.e., shear force).

[0031] Step S103: Input the load displacement test curve into the pre-trained cohesive force model parameter prediction model to obtain the cohesive force model parameters of the structure to be analyzed. Specifically, the load-displacement test curve corresponding to the structure to be analyzed is input into the cohesive force model parameter prediction model, which then outputs the parameters of the cohesive force model corresponding to the structure to be analyzed, so as to evaluate the adhesive performance of the adhesive interconnect layer of the structure to be analyzed based on the cohesive force model parameters of the structure to be analyzed.

[0032] For example, the cohesive force model parameters include fracture shear force, shear strength, and shear separation displacement. Fracture shear force is the maximum shear force that the adhesive interconnect layer of the structure to be analyzed can withstand, shear strength is the maximum shear strength that the adhesive interconnect layer of the structure to be analyzed can withstand, and shear separation displacement is the displacement when the metal substrate and the chip of the structure to be analyzed are disconnected.

[0033] In this embodiment, the method includes acquiring the structure to be analyzed, performing a thrust shear test on the structure to be analyzed to obtain a load-displacement test curve of the structure to be analyzed, and inputting the load-displacement test curve into a pre-trained cohesive force model parameter prediction model to obtain the cohesive force model parameters of the structure to be analyzed. The structure to be analyzed is a structure containing an adhesive interconnect layer. By performing a thrust shear test on the structure to be analyzed, the load-displacement test curve generated by the adhesive interconnect layer is obtained. By inputting the load-displacement test curve into the pre-trained cohesive force model parameter prediction model, the cohesive force model parameters of the structure to be analyzed are obtained, and the cohesive force of the structure to be analyzed is obtained. This application obtains the load-displacement test curve of the structure to be analyzed by performing a thrust shear test on the structure to be analyzed, and by inputting the load-displacement curve into the pre-trained cohesive force model parameter prediction model, the cohesive force model parameters of the structure to be analyzed can be obtained, realizing the rapid determination of the cohesive force model parameters, which helps to reduce the difficulty of determining the cohesive force model parameters and improve the determination efficiency.

[0034] In some embodiments, the pre-training process of the pre-trained cohesion model parameter prediction model includes: Step S201: Obtain multiple pre-training analysis structures; Specifically, multiple identical analysis structures are obtained as the pre-training analysis structures, in order to obtain the training data required for pre-training the cohesion model parameter prediction model based on the pre-training analysis structures.

[0035] It should be noted that the training data includes the load-displacement curves corresponding to each of the pre-training analysis structures and the cohesive force model parameters corresponding to each of the pre-training analysis structures. The load-displacement curves corresponding to the pre-training analysis structures are the input training data, and the cohesive force model parameters corresponding to the pre-training analysis structures are the output training data.

[0036] Step S202: Thrust shear tests are performed on multiple pre-training analysis structures under multiple working conditions to obtain load-displacement training curves corresponding to multiple pre-training analysis structures. Specifically, the pre-training analysis structures are identical in structure, and the specifications of the bonding interconnect layer, the chip, and the metal substrate, as well as the type of the bonding interconnect layer, are all the same. A thrust shear test is performed on one of the pre-training analysis structures under each working condition to obtain the load-displacement training curves (i.e., multiple load-displacement training curves) of the pre-training analysis structure under different working conditions, so as to analyze the cohesive model parameters of the pre-training analysis structure under different working conditions.

[0037] It should be noted that "operating conditions" can refer to the test environment, and the test environment can refer to the test temperature.

[0038] Step S203: Calculate the cohesive force model parameters corresponding to the pre-trained analysis structures based on the multiple load-displacement training curves. Specifically, the cohesive force model parameters corresponding to each load displacement training curve are calculated based on multiple load displacement reference points corresponding to each load displacement training curve.

[0039] Step S204: Using multiple load displacement training curves as input data and multiple cohesive force model parameters corresponding to the pre-trained analysis structure as input data, train the neural network model to obtain the cohesive force model parameter prediction model.

[0040] Specifically, each load-displacement training curve corresponds to a cohesive force model parameter. Multiple sets of load-displacement training curves and cohesive force model parameters are used as training data to train the neural network model. In each set of load-displacement training curves and cohesive force model parameters, the load-displacement training curves are used as input data and the cohesive force model parameters are used as output data to correct various parameters of the neural network model. The neural network model containing the corrected parameters is the cohesive force model parameter prediction model.

[0041] In this embodiment, during the pre-training of the cohesive force model parameter prediction model, thrust shear tests are performed on multiple pre-training analysis structures to obtain pre-training input data (load-displacement training curves). The cohesive force model parameters corresponding to each load-displacement training curve are then calculated based on these curves to obtain pre-training output data (cohesive force model parameters). Based on this, the neural network model is trained, and the model obtained after training is the cohesive force model parameter prediction model. The pre-training process of the cohesive force model parameter prediction model is based on thrust shear tests on the pre-training analysis structures. The use of the cohesive force model parameter prediction model is also based on thrust shear tests, which improves the prediction accuracy and efficiency of determining the cohesive force model parameters.

[0042] In some embodiments, step S203: calculating the cohesive model parameters corresponding to the multiple pre-trained analysis structures based on the multiple load-displacement training curves includes: Step S301: Determine multiple fracture energies of the pre-training analysis structure based on the load-displacement training curve; Specifically, based on the constitutive relation of the cohesive force model parameters and the load-displacement training curve obtained from the experiment, multiple fracture energies of the adhesive interconnect layer corresponding to the load-displacement training curve are calculated.

[0043] It should be noted that fracture energy refers to the energy required for the crack to propagate per unit area when the adhesive interconnect layer is subjected to tensile load.

[0044] Step S302: Based on the multiple fracture energies, perform simulation calculations to obtain the cohesive force model parameters of the pre-trained analysis structure.

[0045] Specifically, the multiple fracture energies are used as inputs to the finite element model for iterative calculation. The simulated load-displacement curve is fitted with the slope of the simulated load-displacement training curve and the maximum load value (curve shape) at which complete fracture is achieved, in order to obtain the final interface tangential strength and interface stiffness, as well as the relative shear separation displacement δ. f .

[0046] Specifically, step S302 includes step S401, performing iterative calculations based on the multiple fracture energy and cohesion models to obtain the load-displacement simulation curve; step S402, fitting the load-displacement training curve and the load-displacement simulation curve to obtain the cohesion model parameters of the pre-trained analysis structure.

[0047] It should be noted that, for each load-displacement training curve, the cohesive model parameters corresponding to that load-displacement training curve are determined according to the above steps, so as to realize the determination of the cohesive model parameters corresponding to multiple load-displacement training curves.

[0048] In this embodiment, multiple fracture energies of the adhesive interconnect layer corresponding to the load-displacement training curve are determined based on the constitutive relationship between the load-displacement training curve and the cohesive model parameters of the adhesive interconnect layer. These multiple fracture energies are then input into the finite element model for iterative simulation calculation to obtain a simulated load-displacement curve corresponding to the load-displacement training curve. The cohesive model parameters corresponding to the load-displacement training curve are then determined based on the simulated load-displacement curve. This process enables the determination of the cohesive model parameters required for training the cohesive model parameter prediction model, which helps to improve the accuracy of the determination of the cohesive model parameters.

[0049] In some embodiments, step S302: obtaining the cohesive model parameters of the pre-trained analysis structure based on the plurality of fracture energies through simulation calculation includes: Step S501: Determine the stress variation curve and strain variation curve of the pre-training analysis structure based on the cohesive model parameters of the pre-training analysis structure. Specifically, after determining the cohesive model parameters of the pre-training analysis structure, the cohesive model can be determined based on the cohesive model parameters, and the stress change curve and strain change curve of the pre-training analysis structure can be determined based on the cohesive model.

[0050] It should be noted that the stress variation curve represents the change of stress over time in the adhesive interconnect layer of the pre-training analysis structure during the thrust shear test, and the strain variation curve represents the change of strain over time in the adhesive interconnect layer of the pre-training analysis structure under stress during the thrust shear test.

[0051] For example, the stress variation curve is as follows: Figure 3 As shown, the strain change curve is as follows: Figure 4 As shown.

[0052] Step S502: Verify the validity of the cohesive force model parameters based on the stress change curve and strain change curve.

[0053] Specifically, the stress and strain curves are used to determine whether the stress and strain conditions experienced by the adhesive interconnect layer during the thrust shear test match, in order to verify the effectiveness of the cohesive model parameters.

[0054] It should be noted that after obtaining a new cohesive model parameter based on the cohesive model parameter prediction model, the effectiveness of the new cohesive model parameter can also be verified using this method. This can prove the prediction accuracy of the cohesive model parameter prediction model, thereby ensuring the accuracy of the cohesive model parameter while reducing the difficulty of determining the cohesive model parameter.

[0055] In this embodiment, when determining the cohesive model parameters required for training the cohesive model parameter prediction model, the cohesive model corresponding to the cohesive model parameters is verified to match the stress and strain changes of the adhesive interconnect layer during the thrust shear test. This verifies the effectiveness of the cohesive model parameters and, consequently, the accuracy of the cohesive model parameter prediction model, thereby improving its practicality.

[0056] In some embodiments, step S502: verifying the validity of the cohesive model parameters based on the stress change curve and strain change curve includes: Step S601: Determine the fracture time of the pre-training analysis structure based on the strain change curve; Specifically, the strain constant time of the pre-training analysis structure is determined based on the strain change curve, and this time is the fracture time of the pre-training analysis structure.

[0057] It should be noted that when the pre-training analysis structure breaks, it is determined that the adhesive interconnect layer of the pre-training analysis structure has failed under stress and can no longer connect the metal substrate and the chip. Therefore, the strain change curve is the strain change curve of the adhesive interconnect layer. When the strain of the adhesive interconnect layer does not change under stress, it means that the metal substrate and the chip do not have a restraining effect on the adhesive interconnect layer, the metal substrate and the chip are disconnected, and the pre-training analysis structure breaks. The strain of the adhesive interconnect layer is constant.

[0058] Step S602: Determine the stress constant time of the pre-training analysis structure based on the stress change curve; Specifically, the stress change curve represents the stress change experienced by the adhesive interconnect layer. The stress constant time is determined based on the stress change curve. At this time, the stress experienced by the adhesive interconnect layer remains constant, and the connection between the metal substrate and the chip is broken.

[0059] Step S603: In response to determining that the fracture time is the same as the stress constant time, the cohesive force model parameters are determined to be valid.

[0060] Specifically, by comparing the fracture time and the stress constant time, if the two times are the same, it is determined that the strain change curve and the stress change curve match the situation of the adhesive interconnect layer in the thrust shear test, thereby determining that the cohesive model parameters are valid.

[0061] In this embodiment, the fracture time and stress constant time are determined based on the strain change curve and stress change curve, respectively, so as to determine the effectiveness of the cohesive force model parameters by comparing the fracture time and stress constant time, thereby realizing the accuracy verification of the cohesive force model parameters.

[0062] 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.

[0063] 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.

[0064] Based on the same inventive concept, corresponding to any of the above embodiments, this application also provides an apparatus for determining cohesive force model parameters.

[0065] refer to Figure 5 The device includes: Module 100 is configured to acquire the structure to be analyzed. Test module 200 is configured to perform a thrust shear test on the structure to be analyzed and obtain the load-displacement test curve of the structure to be analyzed. The determination module 300 is configured to input the load displacement test curve into a pre-trained cohesive force model parameter prediction model to obtain the cohesive force model parameters corresponding to the structure to be analyzed. The structure to be analyzed is a structure containing an adhesive interconnect layer.

[0066] 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.

[0067] The apparatus described above is used to implement the corresponding method for determining cohesive force model parameters in any of the foregoing embodiments, and has the beneficial effects of the corresponding method embodiments, which will not be repeated here.

[0068] 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 method for determining cohesive model parameters as described in any of the above embodiments.

[0069] Figure 6 This embodiment illustrates a more specific hardware structure of an electronic device. The device 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.

[0070] 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.

[0071] 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.

[0072] 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.

[0073] 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.).

[0074] 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.

[0075] 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.

[0076] The electronic devices described above are used to implement the methods for determining cohesive force model parameters in any of the foregoing embodiments, and have the beneficial effects of the corresponding method embodiments, which will not be repeated here.

[0077] 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 method for determining cohesive model parameters as described in any of the above embodiments.

[0078] 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 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.

[0079] The computer instructions stored in the storage medium of the above embodiments are used to cause the computer to execute the method for determining cohesive force model parameters as described in any of the above embodiments, and have the beneficial effects of the corresponding method embodiments, which will not be repeated here.

[0080] Based on the same concept, corresponding to any of the above embodiments, this application also provides a computer program product, including computer program instructions, which, when run on a computer, cause the computer to perform the method described in any of the above embodiments, and have the beneficial effects of the corresponding method embodiments, which will not be repeated here.

[0081] It is understood that before using the technical solutions of the various embodiments in this disclosure, users will be informed of the type, scope of use, and usage scenarios of the personal information involved in an appropriate manner, and user authorization will be obtained.

[0082] For example, upon receiving a user's active request, a prompt message is sent to the user to explicitly inform them that the requested operation will require the acquisition and use of the user's personal information. This allows the user to independently choose, based on the prompt message, whether to provide personal information to the software or hardware such as electronic devices, applications, servers, or storage media performing the operations of this disclosed technical solution.

[0083] As an optional but not limited implementation, in response to a user's active request, sending a prompt message to the user can be done via a pop-up window, where the prompt message can be presented in text format. Furthermore, the pop-up window can also include a selection control allowing the user to choose "agree" or "disagree" to provide personal information to the electronic device.

[0084] It is understood that the above notification and user authorization process are merely illustrative and do not constitute a limitation on the implementation of this disclosure. Other methods that comply with relevant laws and regulations may also be applied to the implementation of this disclosure.

[0085] 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 is limited to these examples; under the concept 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 detail for the sake of brevity.

[0086] 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.

[0087] 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.

[0088] The embodiments of this application are intended to cover all such substitutions, modifications, and variations that fall within the broad scope of the claims of this application. 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 method for determining parameters of a cohesive force model, characterized in that, include: Obtain the structure to be analyzed; A thrust shear test was performed on the structure to be analyzed, and the load-displacement test curve of the structure to be analyzed was obtained. The load-displacement test curve is input into the pre-trained cohesive force model parameter prediction model to obtain the cohesive force model parameters of the structure to be analyzed. The structure to be analyzed is a structure containing an adhesive interconnect layer.

2. The method according to claim 1, characterized in that, The pre-training process of the pre-trained cohesion model parameter prediction model includes: Obtain multiple pre-trained analysis structures; Thrust shear tests were conducted on multiple pre-training analysis structures under various working conditions to obtain load-displacement training curves corresponding to the multiple pre-training analysis structures. The cohesive model parameters corresponding to the pre-trained analysis structures are calculated based on the multiple load-displacement training curves. Using multiple load displacement training curves as input data and multiple cohesive force model parameters corresponding to the pre-trained analysis structure as input data, the neural network model is trained to obtain the cohesive force model parameter prediction model.

3. The method according to claim 2, characterized in that, The calculation of cohesive model parameters corresponding to the pre-trained analysis structures based on the multiple load-displacement training curves includes: Based on the load-displacement training curve, determine multiple fracture energies of the pre-training analysis structure; The cohesive model parameters of the pre-trained analytical structure are obtained by simulation calculation based on multiple fracture energies.

4. The method according to claim 3, characterized in that, The cohesive model parameters of the pre-trained analytical structure are obtained by simulation calculation based on the multiple fracture energies, including: Iterative calculations were performed based on the multiple fracture energy and cohesion models to obtain the load-displacement simulation curves. The load-displacement training curve and the load-displacement simulation curve are fitted to obtain the cohesive force model parameters of the pre-trained analysis structure.

5. The method according to claim 4, characterized in that, The method of obtaining the cohesive model parameters of the pre-trained analytical structure based on the simulation calculation of the multiple fracture energies also includes: The stress variation curve and strain variation curve of the pre-training analysis structure are determined based on the cohesive model parameters of the pre-training analysis structure. The effectiveness of the cohesive force model parameters is verified based on the stress and strain variation curves.

6. The method according to claim 5, characterized in that, The verification of the validity of the cohesive model parameters based on the stress change curve and strain change curve includes: The fracture time of the pre-training analysis structure is determined based on the strain change curve. The stress constant time of the pre-training analysis structure is determined based on the stress variation curve. In response to determining that the fracture time is the same as the stress constant time, the cohesive model parameters are determined to be valid.

7. An apparatus for determining parameters of a cohesive force model, characterized in that, include: The acquisition module is configured to acquire the structure to be analyzed. The testing module is configured to perform a thrust shear test on the structure to be analyzed and obtain the load-displacement test curve of the structure to be analyzed. The determination module is configured to input the load displacement test curve into a pre-trained cohesive force model parameter prediction model to obtain the cohesive force model parameters corresponding to the structure to be analyzed. The structure to be analyzed is a structure containing an adhesive interconnect layer.

8. An electronic device comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that, When the processor executes the program, it implements the method as described in any one of claims 1 to 6.

9. A non-transitory computer-readable storage medium storing computer instructions, characterized in that, The computer instructions are used to cause the computer to perform the method as described in any one of claims 1 to 6.

10. A computer program product comprising computer program instructions, characterized in that, When the computer program instructions are executed on a computer, the computer causes the computer to perform the method as described in any one of claims 1 to 6.

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