Thermal interface material parameter prediction method, device, system, equipment and medium

Abstract

This application discloses a method, apparatus, system, device, and medium for predicting thermal interface material parameters. The method comprises inputting component thermal conductivity parameters and component configuration parameters of the thermal interface material into a thermal conductivity prediction model to obtain a target thermal conductivity. Based on the mechanical parameters of the thermal interface material at multiple sampling moments, a yield stress prediction model determined by a nonlinear elastic-viscoplastic model is iterated to obtain target rheological characteristic parameters. Based on the target thermal conductivity and target rheological characteristic parameters, the target parameters of the thermal interface material at a preset pressure are determined. This method can quickly and accurately predict the target parameters of the thermal interface material at a preset pressure, thereby improving the efficiency of heat dissipation system design for heat dissipation objects.

Description

Technical Field

[0001] The present application relates to the field of thermal energy and power technology, and in particular to a method, device, system, equipment and medium for predicting parameters of thermal interface materials. Background Art

[0002] Heat pipes are passive heat transfer devices based on gas-liquid phase change. They offer low energy consumption, high heat transfer coefficients, good isothermal properties, and high reliability, making them widely used in electronic device cooling. Because heat pipes have a rough surface that makes them difficult to fully adhere to the object being heated, a thermal interface material must be applied to the surface to fill the gaps between the surface and the object.

[0003] When a thermal interface material is under pressure, its rheological parameters affect its fluidity and thickness, while its target thermal conductivity affects its thermal resistance. Therefore, predicting the thermal resistance of thermal interface materials is crucial for heat dissipation structure design. However, the process of predicting the rheological parameters of thermal interface materials is complex, and thermal conductivity prediction methods struggle to accurately predict the thermal conductivity of composite materials with complex filler shapes, orientations, and interactions. Consequently, it's difficult to quickly and accurately predict the thermal resistance of thermal interface materials under pressure. Summary of the Invention

[0004] According to one aspect of the present disclosure, a method for predicting thermal interface material parameters is provided, the method comprising:

[0005] Inputting component thermal conductivity parameters and component configuration parameters of the thermal interface material into a thermal conductivity prediction model to obtain a target thermal conductivity of the thermal interface material;

[0006] Iterating a yield stress prediction model based on the mechanical parameters of the thermal interface material at multiple sampling moments to obtain target rheological characteristic parameters of the thermal interface material, wherein the yield stress prediction model is determined at least by a nonlinear elastic-viscoplastic model;

[0007] Based on the target thermal conductivity of the thermal interface material and the target rheological characteristic parameters of the thermal interface material, target parameters of the thermal interface material under a preset pressure are determined, where the target parameters at least include a target thermal resistance.

[0008] According to another aspect of the present disclosure, a device for predicting thermal interface material parameters is provided, the device comprising:

[0009] A prediction module, for inputting component thermal conductivity parameters and component configuration parameters of the thermal interface material into a thermal conductivity prediction model to obtain a target thermal conductivity of the thermal interface material;

[0010] an iterative module, configured to iterate a yield stress prediction model based on mechanical parameters of the thermal interface material at multiple sampling moments to obtain target rheological characteristic parameters of the thermal interface material, wherein the yield stress prediction model is determined at least by a nonlinear elastic-viscoplastic model;

[0011] A determination module is configured to determine target parameters of the thermal interface material at a preset pressure based on a target thermal conductivity of the thermal interface material and a target rheological characteristic parameter of the thermal interface material, wherein the target parameters include at least a target thermal resistance.

[0012] According to another aspect of the present disclosure, a heat dissipation system is provided, comprising a heat dissipation object, a heat sink, and a thermal interface material formed between the heat dissipation surface of the heat dissipation object and the heat absorption surface of the heat sink, wherein parameters of the thermal interface material are determined by the method described in the present application.

[0013] According to another aspect of the present disclosure, there is provided an electronic device, comprising:

[0014] processor; and,

[0015] Memory for storing programs;

[0016] The program includes instructions, which, when executed by the processor, enable the processor to perform the method described in this application.

[0017] According to another aspect of the present disclosure, a non-transitory computer-readable storage medium is provided, wherein the non-transitory computer-readable storage medium stores computer instructions, and the computer instructions are used to enable the computer to execute the method according to the present application.

[0018] According to another aspect of the present disclosure, a computer program product is provided, comprising a computer program, wherein the computer program implements the method described in the present application when executed by a processor.

[0019] In one or more technical solutions provided by this application, the component thermal conductivity parameters and component configuration parameters of the thermal interface material are used as inputs to the thermal conductivity prediction model, and the data mining and extrapolation capabilities of the thermal conductivity prediction model are used to perform data mining on the component thermal conductivity parameters and component configuration parameters of the thermal interface material, thereby quickly and accurately obtaining the thermal conductivity of the thermal interface material. In addition, this application can also utilize the fact that the thermal interface material follows the laws of non-Newtonian fluids to determine the yield stress prediction model through a nonlinear elastic-viscoplastic model, and then, using transient data of the thermal interface material, such as the mechanical parameters of the thermal interface material at multiple sampling moments, iterate the yield stress prediction model based on the mechanical parameters of the thermal interface material at multiple sampling moments, thereby quickly and accurately obtaining the target rheological characteristic parameters of the thermal interface material.

[0020] As can be seen, the method of this application can accurately and quickly predict the target thermal conductivity and rheological parameters of the thermal interface material, thereby ensuring the accuracy of the predicted thermal resistance of the thermal interface. Once the power and temperature tolerance of the heat exchange object are known, and then based on this combined with the ambient temperature, wind speed and other parameters of the heat dissipation area, the heat sink can be quickly selected and optimized, thereby improving the efficiency of the heat dissipation system design of the heat dissipation object. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Further details, features and advantages of the present disclosure are disclosed in the following description of exemplary embodiments in conjunction with the accompanying drawings, in which:

[0022] Figure 1 A schematic flow chart showing a method for predicting thermal interface material parameters according to an embodiment of the present application is shown;

[0023] Figure 2 A schematic flow chart showing a method for predicting target thermal conductivity according to an embodiment of the present application is shown;

[0024] Figure 3A A schematic diagram of the training process of the thermal conductivity prediction model according to an embodiment of the present application is shown;

[0025] Figure 3B A schematic diagram of a training framework of a thermal conductivity prediction model according to an embodiment of the present application is shown;

[0026] Figure 3C A schematic diagram comparing the prediction results of the thermal conductivity prediction model of the embodiment of the present application and the Bruggeman model is shown;

[0027] Figure 4A A schematic diagram of a process for determining target rheological characteristic parameters according to an embodiment of the present application is shown;

[0028] Figure 4B A schematic diagram of a framework for determining target rheological characteristic parameters according to an embodiment of the present application is shown;

[0029] Figure 5A A schematic diagram of a process for determining another target rheological characteristic parameter according to an embodiment of the present application is shown;

[0030] Figure 5B A schematic diagram of a framework for determining another target rheological characteristic parameter according to an embodiment of the present application is shown;

[0031] Figure 6 A schematic flow chart of a method for determining target parameters of a thermal interface material under a preset pressure according to an embodiment of the present application is shown;

[0032] Figure 7 A schematic diagram of the heat dissipation system structure is shown in an embodiment of the present application, taking a chip heat dissipation system as an example;

[0033] Figure 8 A schematic block diagram showing functional modules of a device for predicting thermal interface material parameters according to an exemplary embodiment of the present disclosure is shown;

[0034] Figure 9 shows a schematic block diagram of a chip according to an exemplary embodiment of the present disclosure;

[0035] Figure 10 A structural block diagram of an exemplary electronic device that can be used to implement the embodiments of the present disclosure is shown. DETAILED DESCRIPTION

[0036] The following describes embodiments of the present disclosure in more detail with reference to the accompanying drawings. Although certain embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be construed as limited to the embodiments described herein. Rather, these embodiments are provided to provide a more thorough and complete understanding of the present disclosure. It should be understood that the drawings and embodiments of the present disclosure are for illustrative purposes only and are not intended to limit the scope of protection of the present disclosure.

[0037] It should be understood that the various steps described in the method embodiments of the present disclosure may be performed in different orders and / or in parallel. In addition, the method embodiments may include additional steps and / or omit the steps shown. The scope of the present disclosure is not limited in this respect.

[0038] The term "including" and its variations used in this document are open inclusions, that is, "including but not limited to". The term "based on" means "based at least in part on". The term "one embodiment" means "at least one embodiment"; the term "another embodiment" means "at least one other embodiment"; the term "some embodiments" means "at least some embodiments". The relevant definitions of other terms will be given in the description below. It should be noted that the concepts of "first", "second", etc. mentioned in this disclosure are only used to distinguish different devices, modules or units, and are not used to limit the order or interdependence of the functions performed by these devices, modules or units.

[0039] It should be noted that the modifications of "one" and "multiple" mentioned in the present disclosure are illustrative rather than restrictive, and those skilled in the art should understand that unless otherwise clearly indicated in the context, they should be understood as "one or more".

[0040] In data centers, air cooling or liquid cooling is often used to transfer heat from chips to other areas to control chip temperature. For low-density data centers, air cooling can achieve effective temperature control. However, for high-density data centers, air cooling poses challenges in temperature control and results in low power usage effectiveness (PUE), necessitating the use of liquid cooling to dissipate heat from the chips.

[0041] Heat pipes are a type of passive heat transfer device based on gas-liquid phase change. They offer low energy consumption, high heat transfer coefficients, good isothermal properties, and high reliability, making them widely used in electronic device cooling. However, due to limitations in machining processes, numerous micro-asperities are distributed on the solid surface, preventing the two heat transfer surfaces from fully fitting together. Even under high pressure, the actual contact area between the two heat transfer surfaces is still far less than the theoretical contact area.

[0042] Currently, thermal interface materials, such as thermal grease, can be applied between two heat transfer surfaces to fill the gap between them. However, rheological parameters of thermal interface materials, such as viscosity, affect the fluidity and thickness of the thermal surface material. The target thermal conductivity of the thermal interface material also affects its thermal resistance. Therefore, predicting the thermal resistance of thermal interface materials is crucial for heat dissipation structure design.

[0043] Common pyrolytic interface materials include thermal greases and phase-change thermal materials, all of which are typical polymer composites. These materials are formed by incorporating highly thermally conductive fillers into a polymer matrix. However, due to the wide variety of filler properties, shapes, and dispersion states, as well as the complex interactions between fillers and the polymer matrix, it is impossible to accurately determine all properties of the filler and matrix. Therefore, experimental measurement is the primary method for accurately predicting the thermal conductivity and rheological parameters of thermal interface materials.

[0044] For example, the effective medium theory model can be used to predict the thermal conductivity of thermal interface materials. However, the effective medium theory model is suitable for scenarios with simple filler shapes and few influencing factors, and is not suitable for scenarios with complex filler shapes, orientations, and interactions. When using experimental equipment to measure the target thermal conductivity of thermal interface materials, it usually takes a long time to reach a steady state before the target thermal conductivity of the thermal interface material can be accurately obtained. Therefore, using experimental equipment to measure the target thermal conductivity of thermal interface materials requires a long waiting time. At the same time, the viscosity prediction process of thermal surface materials is complex, time-consuming, and has the problem of limited prediction accuracy. It can be seen that when the thermal interface material is under pressure, the thermal resistance of the thermal interface cannot be predicted quickly and accurately.

[0045] To address the above issues, embodiments of the present application provide a method for predicting the thermal resistance of a thermal interface material (TIM). This method can be quickly and accurately predicted, improving the efficiency of heat dissipation system design for heat dissipation targets. This method can be executed by an electronic device or a chip used in an electronic device.

[0046] In practical applications, the methods of the embodiments of the present application can be applied to various heat dissipation scenarios to improve the efficiency of heat conduction between thermal interfaces. For example, heat dissipation structures in data center servers, thermal management of electronic devices, thermal power, aerospace, electric vehicles, photovoltaics, etc.

[0047] Figure 1 FIG. 1 is a flow chart showing a method for predicting thermal interface material parameters according to an embodiment of the present application. Figure 1 As shown, the method for predicting interface material parameters of the embodiment of the present application includes:

[0048] Step 101: Inputting component thermal conductivity parameters and component configuration parameters of the thermal interface material into a thermal conductivity prediction model to obtain a target thermal conductivity of the thermal interface material.

[0049] The components of the thermal interface material of the embodiment of the present application may include a matrix and a filler. The matrix may be a polymer and the filler may be a highly thermally conductive filler. For example, the thermal interface material may include various phase change thermal conductive materials such as thermal grease.

[0050] The component thermal conductivity parameters of the thermal interface material in embodiments of the present application may include the thermal conductivity of the matrix and the thermal conductivity of the filler, and the component configuration parameters may include the filler content. When the component thermal conductivity parameters and component configuration parameters of the thermal interface material are input into the thermal conductivity deviation prediction model, component classification information of the thermal interface material, such as the filler type and the polymer composite material type, may also be input.

[0051] The component thermal conductivity parameters, component configuration parameters and component category information of the above-mentioned thermal interface materials directly or indirectly reflect the shape and orientation of the complex filler, as well as the interaction between the filler and the polymer composite material. Therefore, the data mining capability of the thermal conductivity prediction model can be used to perform data mining on the component thermal conductivity parameters, component configuration parameters and component category information of the thermal interface material, so as to quickly and accurately obtain the thermal conductivity of the thermal interface material.

[0052] Step 102: iterating the yield stress prediction model based on the mechanical parameters of the thermal interface material at multiple sampling moments to obtain target rheological characteristic parameters of the thermal interface material, wherein the yield stress prediction model is determined by at least a nonlinear elastic-viscoplastic model.

[0053] In practical applications, embodiments of the present application can use at least the yield stress prediction model determined by the nonlinear elastic-viscoplastic model as a constraint, and based on the initial rheological characteristic parameters of the thermal interface material and the mechanical parameters of the thermal interface material at multiple sampling moments, use an iterative update method to update the initial rheological characteristic parameters of the thermal interface material until the difference between the initial rheological characteristic parameters before and after the update is relatively small. At this point, the initial rheological characteristic parameters are the target rheological characteristic parameters of the thermal interface material of the embodiments of the present application.

[0054] When the yield stress prediction model of the embodiment of the present application constrains the initial rheological characteristic parameters of a given thermal interface material and the mechanical parameters of the thermal interface material at multiple sampling moments, the transient mechanical parameters of the thermal interface material at multiple sampling moments can be used to update the initial rheological characteristic parameters of the thermal interface material without obtaining the steady-state mechanical parameters of the thermal interface material at multiple sampling moments. Therefore, the method of the embodiment of the present application can quickly and accurately determine the target rheological characteristic parameters of the thermal interface material.

[0055] Step 103: Determine target parameters of the thermal interface material at a preset pressure based on the target thermal conductivity and target rheological characteristic parameters of the thermal interface material, where the target parameters include at least a target thermal resistance.

[0056] In practical applications, the embodiments of the present application can quickly and accurately predict the target thermal conductivity and target rheological parameters of the thermal interface material, resulting in an accurate target thermal resistance of the thermal interface material at a preset pressure. In this case, once the power and temperature tolerance of the heat exchange object are known, the target thermal resistance of the thermal interface material at a preset pressure can be combined with parameters such as the ambient temperature and wind speed in the area where the heat dissipation object is located to quickly select and optimize the heat sink design, thereby improving the efficiency of the heat dissipation object's heat dissipation system design.

[0057] In a possible implementation, the thermal conductivity prediction model of the embodiment of the present application may include a first prediction model and an effective medium theory model. In this case, Figure 2 FIG. 1 is a flow chart showing a method for predicting target thermal conductivity according to an embodiment of the present application. Figure 2 As shown, the embodiment of the present application inputs the component thermal conductivity parameters and component configuration parameters of the thermal interface material into the thermal conductivity prediction module to obtain the target thermal conductivity of the thermal interface material, including:

[0058] Step 201: Inputting the component thermal conductivity parameters and component configuration parameters of the thermal interface material into the first prediction model to obtain the thermal conductivity deviation of the thermal interface material.

[0059] In practical applications, the thermal conductivity deviation of the thermal interface material in the embodiment of the present application is not necessarily the difference between the target thermal conductivity of the thermal interface material and the theoretical thermal conductivity of the thermal interface material in a strict sense, but is related to the thermal conductivity deviation of the thermal interface material. Therefore, the thermal conductivity deviation of the thermal interface material can be used as a parameter for solving the target thermal conductivity of the thermal interface material through the theoretical thermal conductivity of the thermal interface material.

[0060] Step 202: Inputting the component thermal conductivity parameters and component configuration parameters of the thermal interface material into the effective medium theory model to obtain the theoretical thermal conductivity of the thermal interface material.

[0061] In practical applications, the theoretical thermal conductivity of the thermal interface material of the embodiment of the present application can be determined using an effective medium theory model. Here, the component thermal conductivity parameters and component configuration parameters of the thermal interface material can be input into the effective medium theory model to determine the theoretical thermal conductivity of the thermal interface material. For example, the effective medium theory model of the embodiment of the present application can be the Bruggeman model, and the descriptive equation of the Bruggeman model can be expressed as:

[0062]

[0063] Among them, κ c represents the theoretical thermal conductivity of the thermal interface material, κ f represents the thermal conductivity of the matrix, κ m Indicates the thermal conductivity of the filler material, V f Represents the filler volume fraction of the thermal interface material.

[0064] Step 203: fusing the thermal conductivity deviation of the thermal interface material and the theoretical thermal conductivity of the thermal interface material based on the target fusion parameter to obtain the target thermal conductivity of the thermal interface material.

[0065] The thermal conductivity deviation of the thermal interface material in the embodiment of the present application is related to the thermal conductivity deviation of the thermal interface material, but is not necessarily the difference between the target thermal conductivity of the thermal interface material and the theoretical thermal conductivity of the thermal interface material. Instead, it is related to the thermal conductivity deviation of the thermal interface material. Therefore, the embodiment of the present application can use a linear or nonlinear method to fuse the thermal conductivity deviation of the thermal interface material and the theoretical thermal conductivity of the thermal interface material sample to obtain the target thermal conductivity of the thermal interface material.

[0066] When fusing the thermal conductivity deviation of the thermal interface material and the theoretical thermal conductivity of the thermal interface material sample, the target fusion parameter used can be a fixed value set in advance, or a fusion parameter determined during the training of the thermal conductivity prediction model.

[0067] For example, when the thermal conductivity deviation of the thermal interface material of the embodiment of the present application is expressed as Δκ, a linear weighted fusion method is adopted, and the target fusion parameter may include the weight k1 of the theoretical thermal conductivity and the weight k2 of the thermal conductivity deviation. At this time, the thermal conductivity κ of the thermal interface material is TIM =k1*κ c +k2*Δκ. Thus, the embodiments of the present application combine machine learning with the effective medium theory model to fully exploit the information contained in the component thermal conductivity parameters, component configuration parameters, and component type information of the thermal interface material, thereby achieving the purpose of quickly and accurately predicting the target thermal conductivity of the thermal interface material.

[0068] When the thermal conductivity prediction model of the present embodiment includes a first prediction model and an effective medium theory model, the method of the present embodiment also includes a method for training the thermal conductivity prediction model. Considering that the parameters of the effective medium theory model are fixed, during the training of the thermal conductivity prediction model, the effective medium theory model can be used as an auxiliary to train the first prediction model and determine target fusion parameters to ensure that the resulting thermal conductivity deviation prediction model can predict the thermal conductivity deviation of the thermal interface material.

[0069] Figure 3A FIG. 1 shows a schematic diagram of the training process of the thermal conductivity prediction model of the embodiment of the present application. Figure 3A As shown, the flow chart of the training method of the thermal conductivity prediction model of the embodiment of the present application is shown. Figure 3A As shown, the training method of the thermal conductivity prediction model of the embodiment of the present application includes:

[0070] Step 301: Inputting the component thermal conductivity parameters and historical configuration parameters of the thermal interface material sample into a first prediction model to obtain a thermal conductivity deviation prediction result of the thermal interface material sample.

[0071] In practical applications, the component classification information of thermal interface material samples can also be input into the first prediction model, allowing the trained thermal conductivity deviation prediction model to predict the thermal conductivity deviation parameters of various thermal interface materials. It should be understood that the component classification information, component thermal conductivity parameters, and historical configuration parameters can be found in the previous section and will not be further elaborated here.

[0072] Step 302: Input the thermal conductivity parameters and historical configuration parameters of the thermal interface material sample into the effective medium theory model to determine the theoretical thermal conductivity of the thermal interface material sample. Reference can be made to step 202 above to determine the theoretical thermal conductivity of the thermal interface material sample, and no further details will be given.

[0073] Step 303: Based on the initial fusion parameters, the predicted thermal conductivity deviation results of the thermal interface material sample are fused with the theoretical thermal conductivity of the thermal interface material sample to determine the predicted thermal conductivity result of the thermal interface material sample. The fusion method for the predicted thermal conductivity deviation results of the thermal interface material sample and the theoretical thermal conductivity of the thermal interface material sample can be found in step 203 above and will not be further described.

[0074] Step 304: Determine the predicted loss of the thermal conductivity prediction model based on the thermal conductivity prediction result of the thermal interface material sample and the reference thermal conductivity of the thermal interface material sample.

[0075] In practical applications, the embodiment of the present application can determine the prediction loss of the thermal conductivity prediction model by selecting an applicable loss function. For example, the mean-square error (MSE) loss function shown in Formula 2 can be used to determine the mean square error L of the thermal conductivity prediction model. MSE1 .

[0076]

[0077] Where N represents the total number of thermal interface material samples, i represents the serial number of the thermal interface material sample, κ pi represents the thermal conductivity prediction result of the i-th thermal interface material sample, κ ri represents the reference thermal conductivity of the i-th thermal interface material sample.

[0078] Step 305: In response to the predicted loss of the thermal conductivity prediction model satisfying the convergence condition of the first prediction model, the first prediction model is determined to have completed training, and target fusion parameters are determined based on the initial fusion parameters. The initial fusion parameters can be set as the target fusion parameters. Of course, the target fusion parameters can also be adjusted using relevant techniques.

[0079] Step 306: In response to the predicted loss of the thermal conductivity prediction model not satisfying the convergence condition of the first prediction model, the model parameters of the first prediction model are updated. For example, an Adaptive Moment Estimation (Adam) optimizer or a Broyden-Fletcher-Goldfarb-Shanno (BFGS) optimizer may be used to update the model parameters of the first prediction model.

[0080] In practical applications, the convergence condition of the first prediction model in the embodiment of the present application may include: the prediction loss L of the first prediction model MSE Less than a preset loss, or the number of updates of the first prediction model reaches a preset number, etc.

[0081] Figure 3B FIG. 1 shows a schematic diagram of a training framework of a thermal conductivity prediction model according to an embodiment of the present application. Figure 3B As shown, the embodiment of the present application can collect experimental data of a large number of thermal interface material samples, and these experimental data may include reference thermal conductivity κ of the thermal interface material samples. r , the matrix thermal conductivity κ of the thermal interface material sample m , filler thermal conductivity κ of thermal interface material samples f and the filler volume fraction V of the thermal interface material sample f .

[0082] In this embodiment of the present application, the matrix thermal conductivity of the thermal interface material sample, the filler thermal conductivity of the thermal interface material sample, and the filler volume fraction of the thermal interface material sample can be input into the first prediction model 301 to obtain the thermal conductivity deviation prediction result of the first prediction model. Here, the first prediction model 301 may include a multi-layer perceptron, which uses the data mining and extrapolation capabilities of the multi-layer perceptron to analyze the matrix thermal conductivity of the thermal interface material sample, the filler thermal conductivity of the thermal interface material sample, and the filler volume fraction of the thermal interface material sample to obtain the thermal conductivity deviation prediction result of the first prediction model.

[0083] At the same time, if Figure 3B As shown, the matrix thermal conductivity of the thermal interface material sample, the filler thermal conductivity of the thermal interface material sample, and the filler volume fraction of the thermal interface material sample are input into the Bruggeman model 302 to obtain the theoretical thermal conductivity of the thermal interface material sample. Then, based on the initial fusion parameters, the theoretical thermal conductivity κ0 of the thermal interface material sample and the thermal conductivity deviation prediction result Δκ of the first prediction model are fused to determine the thermal conductivity prediction result κ of the thermal interface material sample. p Here, the embodiment of the present application may use linear or nonlinear operations to combine the thermal conductivity deviation of the thermal interface material sample with the theoretical thermal conductivity of the thermal interface material.

[0084] For example, when the thermal interface material sample in an embodiment of the present application includes multiple thermal interface material samples, the thermal conductivity prediction results of the thermal interface material samples include the thermal conductivity prediction results of the multiple thermal interface material samples, and the reference thermal conductivity of the thermal interface material samples includes the reference thermal conductivity of the multiple thermal interface material samples. In this case, the thermal conductivity prediction results of the multiple thermal interface material samples and the reference thermal conductivity of the thermal interface material samples can be input into the MSE loss function to determine the mean square error of the thermal conductivity prediction model.

[0085] The embodiment of the present application can determine whether the mean square error loss of the thermal conductivity prediction model is less than the first preset mean square error. If the mean square error L of the thermal conductivity prediction model is less than the first preset mean square error, MSE1When the root mean square error is greater than or equal to the first preset mean square error L1, it indicates that the gradient of the root mean square error is relatively large, which is manifested in that the root mean square error of the first prediction model is relatively large before and after the model parameters of the first prediction model are updated. Therefore, the prediction accuracy of the first prediction model is insufficient, and the Adam optimizer 303 can be used to optimize the model parameters and initial fusion parameters of the first prediction model. When the root mean square error L MSE1 When it is less than the first preset mean square error L1, it means that the gradient of the root mean square error is relatively small, which is manifested in that the difference in the mean square error of the first prediction model is small before and after the model parameters of the first prediction model are updated. Therefore, the prediction accuracy of the first prediction model is good, and the training of the first prediction model can be ended. The first prediction model can be determined as the thermal conductivity deviation prediction model, and the initial fusion parameters are set as the target fusion parameters.

[0086] It can be seen that the embodiment of the present application integrates the first prediction model with the effective medium theory model, combines the machine learning process of the first prediction model with the prediction process of the effective medium theory model, and trains the first prediction model. Since the mean square error loss of the thermal conductivity prediction model is determined by the thermal conductivity prediction result of the thermal interface material sample and the reference thermal conductivity of the thermal interface material sample, when the mean square error loss of the thermal conductivity prediction model is less than the preset mean square error loss, it can be considered that the prediction result of the first prediction model is close to or equal to the prediction error of the thermal interface material sample predicted by the Bruggeman model. Therefore, the first prediction model of the embodiment of the present application can accurately predict the thermal conductivity deviation of the thermal interface material after training.

[0087] Moreover, considering that the thermal conductivity of the thermal interface material sample actually reflects the comprehensive influence of the various component materials contained in the thermal interface material sample under their proportions, the training process of the first prediction model of the embodiment of the present application can use various experimental data of the thermal interface material under different components and proportions as training data to train the first prediction model, which does not require data assumptions about the thermal interface material. Therefore, compared with directly using the Bruggeman model to predict the thermal conductivity of the thermal interface material, the embodiment of the present application is based on the first prediction model after training, and can obtain the thermal conductivity of the thermal interface material more accurately and quickly.

[0088] Figure 3C Schematic diagram showing the comparison of the prediction results of the thermal conductivity prediction model of the embodiment of the present application and the Bruggeman model. Figure 3C As shown, the embodiment of the present application utilizes a thermal conductivity prediction model to learn the prediction error of the effective medium theory model from various experimental data of thermal interface materials. Therefore, the thermal conductivity deviation prediction model and the effective medium model are integrated to ensure that the effective medium model can be applied to non-uniform medium modeling, and has good generalization ability and higher accuracy.

[0089] It should be noted that, before training the first prediction model, the embodiment of the present application can be used to calculate the reference thermal conductivity of the thermal interface material sample, the matrix thermal conductivity of the thermal interface material sample, the filler thermal conductivity of the thermal interface material sample, and the filler volume fraction and theoretical prediction value κ of the thermal interface material sample. c Normalization is performed, and 5-fold cross validation can be used to enhance the generalization ability of the obtained thermal conductivity prediction model.

[0090] In one possible implementation, when the embodiment of the present application iterates the yield stress prediction model based on the mechanical parameters of the thermal interface material at multiple sampling moments, the mechanical parameters of the thermal interface material at multiple sampling moments used may include the shear rate and reference yield stress of the thermal interface material at multiple sampling moments.

[0091] When the yield stress prediction model is determined solely by the nonlinear elastic-viscoplastic model, Figure 4A FIG1 shows a schematic diagram of a process for determining target rheological characteristic parameters according to an embodiment of the present application. Figure 4B FIG. 1 shows a schematic diagram of a framework for determining target rheological characteristic parameters according to an embodiment of the present application. Figure 4A and Figure 4B As shown, the embodiment of the present application iterates the yield stress prediction model based on the mechanical parameters of the thermal interface material at multiple sampling moments to obtain the target rheological characteristic parameters of the thermal interface material, which may include:

[0092] Step 401: Inputting the mechanical parameters of the thermal interface material at multiple sampling moments and the initial rheological characteristic parameters of the thermal interface material into a yield stress prediction model to obtain a first yield stress prediction result of the thermal interface material at each sampling moment.

[0093] In practical applications, the nonlinear elastoplastic model shown in Equation 3 can be discretized by finite difference to obtain the finite difference discretization result of the nonlinear elastoplastic model, and the finite difference discretization result of the nonlinear elastoplastic model is set as the model function of the yield stress prediction model.

[0094]

[0095] Among them, σ y represents the yield stress of the thermal interface material, η y represents the viscosity of the thermal interface material, γ represents the shear rate of the thermal interface material, G represents the shear modulus, k represents the coefficient, n represents the parameter from the Bulkley model, σ represents the current yield stress of the thermal interface material, and n and G are known quantities.

[0096] In practical applications, the nonlinear elastic-viscoplastic model is discretized by finite difference, and the model function σ(t+Δt) of the yield stress prediction model can be obtained as shown in Equation 4. Given the yield stress σ of the thermal interface material in Equation 4, y The initial value of (i.e. the initial yield stress of the thermal interface material), the viscosity η of the thermal interface material of the four setting formulas y The initial value of the thermal interface material (i.e. the initial viscosity of the thermal interface material) and the yield stress σ of the thermal interface material at the tth sampling moment are obtained. t and the shear rate γ of the thermal interface material at the tth sampling moment t , then σ y ,η s , σ t and γ t Substituting into formula 4, we can obtain the yield stress prediction result of the thermal interface material at the sampling time t+Δt

[0097]

[0098] Step 402: Determine a prediction loss of a yield stress prediction model based on the first yield stress prediction results of the thermal interface material at multiple sampling moments and the reference yield stress of the thermal interface material at multiple sampling moments.

[0099] In practical applications, the embodiment of the present application can determine whether the predicted loss of the yield stress prediction model meets the convergence condition of the yield stress prediction model. If the predicted loss of the yield stress prediction model meets the convergence condition of the yield stress prediction model, it means that the yield stress σ of the given thermal interface material is y The initial value of the viscosity η of the thermal interface material y The possibility of the initial value of σ changing at the subsequent sampling time is relatively low, so step 403 can be executed. Otherwise, it indicates that the yield stress σ of the given thermal interface material is y The initial value of and the initial viscosity η of the thermal interface material y The possibility that the initial value of changes at subsequent sampling moments is relatively high, so step 404 can be executed.

[0100] The convergence conditions of the yield stress prediction model in the embodiment of the present application may include: the prediction loss of the yield stress prediction model is less than a second preset mean square error, the number of iterations of the yield stress prediction model is greater than or equal to a preset number of iterations, etc.

[0101] For example, the embodiment of the present application can refer to the MSE loss function shown in Formula 5 to determine the mean square error L of the yield stress prediction model. MSE2 , when the mean square error L of the yield stress prediction model MSE2When it is less than the second preset mean square error, it means that the yield stress prediction result of the thermal interface material at each sampling moment is very close to the reference yield stress of the thermal interface material at the corresponding sampling moment. Therefore, the mean square error L of the yield stress prediction model is MSE2 If the preset loss of the yield stress prediction model is satisfied, step 403 can be executed; otherwise, it means that the yield stress prediction result of the thermal interface material at each sampling moment is significantly different from the reference yield stress of the thermal interface material at the corresponding sampling moment. Therefore, the mean square error L of the yield stress prediction model is MSE2 If the preset loss of the yield stress prediction model is not satisfied, step 404 may be executed.

[0102]

[0103] Wherein, M represents the first sampling number, represents the yield stress prediction result of the thermal interface material at the sampling time t+Δt, σ t+Δt Reference yield stress of the thermal interface material at sampling time t+Δt.

[0104] Step 403 : In response to the predicted loss of the yield stress prediction model satisfying the convergence condition of the yield stress prediction model, determining the initial rheological characteristic parameters of the thermal interface material as the target rheological characteristic parameters of the thermal interface material.

[0105] For example, when the mean square error L of the yield stress prediction model MSE2 is less than the second preset mean square error, the initial value of the yield stress of the thermal interface material can be set as the target yield stress σ of the thermal interface material target , set the initial value of the viscosity of the thermal interface material to the target viscosity η of the thermal interface material target .

[0106] Step 404: in response to the predicted loss of the yield stress prediction model not satisfying the convergence condition of the yield stress prediction model, updating the initial rheological characteristic parameters of the thermal interface material.

[0107] In another possible implementation, the yield stress prediction model of the embodiment of the present application includes a second prediction model and a nonlinear elasto-viscoplastic residual model. In this case, the nonlinear elasto-viscoplastic model can be integrated into the training process of the second prediction model, so that when the second prediction model training is completed, the target rheological characteristic parameters of the thermal interface material can be simultaneously obtained. Here, the mechanical parameters of the thermal interface material at multiple sampling times include the shear rate and reference yield stress of the thermal interface material at multiple sampling times.

[0108] When the nonlinear elastic-viscoplastic model is incorporated into the second prediction model, Figure 5A FIG2 shows another flow chart of determining target rheological characteristic parameters according to an embodiment of the present application. Figure 5B FIG. 2 shows another schematic diagram of a framework for determining target rheological characteristic parameters according to an embodiment of the present application. Figure 5A and Figure 5B As shown, the target rheological characteristic parameters of the thermal interface material of the embodiment of the present application may include:

[0109] Step 501: Input the shear rate of the thermal interface material at each sampling time into a second prediction model to obtain a second yield stress prediction result of the thermal interface material at each sampling time. The second prediction model may include a multilayer perceptron. Leveraging the data mining and extrapolation capabilities of the multilayer perceptron, a second yield stress prediction result of the thermal interface material at each sampling time may be obtained.

[0110] Step 502: Determine the prediction residual of the second yield stress at each sampling moment based on the second yield stress prediction result of the thermal interface material at each sampling moment and the reference yield stress of the thermal interface material at each sampling moment.

[0111] In practical applications, the difference between the predicted second yield stress and the reference yield stress of the thermal interface material at the same sampling moment can be solved and defined as the predicted residual of the second yield stress at the sampling moment.

[0112] For example, the shear rate γ of the thermal interface material at the tth sampling moment is t By inputting the second prediction model, the yield stress prediction result of the thermal interface material at the sampling time t+Δt can be obtained. In this case, the reference yield stress σ of the thermal interface material at the sampling time t+Δt can be used as the basis. t+Δt And the yield stress prediction results of the thermal interface material at the t+Δt sampling time Determine the prediction residual of the second yield stress at sampling time t+Δt

[0113] Step 503: Input the reference yield stress of the thermal interface material at each sampling moment, the shear rate of the thermal interface material at each sampling moment, and the initial rheological characteristic parameters of the thermal interface material into the nonlinear elasto-viscoplastic residual model to determine the prediction residual of the nonlinear elasto-viscoplastic model at each sampling moment.

[0114] In practical applications, the embodiment of the present application can determine the change information of the second prediction model over time based on the second prediction model, and determine the nonlinear elastic-viscoplastic residual model based on the change information of the second prediction model over time and the nonlinear elastic-viscoplastic model.

[0115] For example, Figure 5BAs shown, assuming that the model function of the second prediction model 501 can be expressed as σ, the differential result of the second prediction model can be obtained by solving the differential of the model function of the second prediction model with respect to time. Then the differential result of the second prediction model is By taking the difference with the nonlinear elastic-viscoplastic model, the functional expression of the nonlinear elastic-viscoplastic residual model 502 can be obtained, that is, the control equation Δδ can be expressed as Equation 6:

[0116]

[0117] Step 504: Determine the prediction loss of the yield stress prediction model based on the prediction residuals of the second yield stress at multiple sampling moments and the prediction residuals of the nonlinear elastic-viscoplastic model at multiple sampling moments.

[0118] In practical applications, it can be determined whether the predicted loss of the yield stress prediction model meets the convergence condition of the second prediction model. When the predicted loss of the yield stress prediction model meets the convergence condition of the yield stress prediction model, it indicates that the possibility of the initial rheological characteristic parameters of the thermal interface material changing at the subsequent sampling time is relatively low. Therefore, step 505 can be executed. Otherwise, it indicates that the possibility of the initial rheological characteristic parameters of the thermal interface material changing at the subsequent sampling time is relatively high. Therefore, step 506 can be executed.

[0119] Illustratively, the convergence conditions of the yield stress prediction model of the embodiment of the present application may include: the loss of the prediction residual is less than a second preset mean square error, the number of iterations of the yield stress prediction model is greater than or equal to a preset number of iterations, etc.

[0120] For example, the MSE loss function shown in Formula 7 can be used to determine the prediction loss of the yield stress prediction model, that is, the mean square error L of the yield stress prediction model. MSE3 .

[0121]

[0122] Wherein, K represents the second sampling number.

[0123] When the mean square error L of the yield stress prediction model MSE3 When the loss of the yield stress prediction model satisfies the convergence condition of the yield stress prediction model, step 505 can be executed. Otherwise, the predicted loss of the yield stress prediction model does not satisfy the convergence condition of the yield stress prediction model, and step 506 can be executed.

[0124] Step 505 : In response to the predicted loss of the yield stress prediction model satisfying the convergence condition of the yield stress prediction model, determining the initial rheological characteristic parameters of the thermal interface material as the target rheological characteristic parameters of the thermal interface material.

[0125] When the initial rheological characteristic parameters of the thermal interface material include the yield stress σ y The initial value and bracket determine the viscosity η of the thermal interface material y The target rheological characteristic parameters of the thermal interface material may include the target yield stress σ target and the target viscosity η of the thermal interface material target .

[0126] Step 506: In response to the predicted loss of the yield stress prediction model not satisfying the convergence condition of the yield stress prediction model, the initial rheological characteristic parameters of the thermal interface material and the model parameters of the second prediction model are updated. Here, the Adam optimizer 503 can be used to update the initial rheological characteristic parameters of the thermal interface material, such as the yield stress σ of the thermal interface material. y The initial value and bracket determine the viscosity η of the thermal interface material y and the model parameters of the second prediction model.

[0127] It can be seen that the thermal interface materials in the embodiments of the present application, such as thermal silicone grease, are complex non-Newtonian fluids. The nonlinear elastic-viscoplastic model is integrated with the training process of the second prediction model, and the experimental data of the thermal interface material is used as input to predict the rheological characteristic parameters of the thermal interface material.

[0128] Moreover, the experimental data used in the process of predicting the rheological characteristic parameters of the thermal interface material in the embodiment of the present application can be transient experimental data, and it is not necessary to reach a steady state. Therefore, the prediction time of the rheological characteristic parameters of the thermal interface material in the embodiment of the present application is shorter and more efficient.

[0129] In one possible implementation, when the target rheological characteristic parameters of the thermal interface material include the target yield stress of the thermal interface material, the target parameters of the embodiment of the present application may also include the target thickness. In this case, Figure 6 The following is a flow chart showing a method for determining target parameters of a thermal interface material under a preset pressure according to an embodiment of the present application. Figure 6 As shown, the embodiment of the present application determines the target parameters of the thermal interface material at a preset pressure based on the target thermal conductivity of the thermal interface material and the target rheological characteristic parameters of the thermal interface material, which may include:

[0130] Step 601: Determine a target thickness of the thermal interface material at a preset pressure based on a target yield stress of the thermal interface material and a radial dimension of the heat dissipation object. The target thickness BLT of the thermal interface material at a preset pressure can be determined using Equation 8.

[0131]

[0132] Where r represents the radius of the heat dissipation object. If the heat dissipation object is a chip, then r represents the radius of the chip, p represents the preset pressure, and σ target Represents the target yield stress of the thermal interface material.

[0133] Step 602: Determine the thermal contact resistance of the thermal interface material based on the target thermal conductivity of the thermal interface material and the surface contact parameters of the thermal interface material.

[0134] In practical applications, the thermal interface material of the embodiment of the present application has a heat dissipation surface in contact with the heat sink, and a heat absorption surface in contact with the heat dissipation object. The surface contact parameters of the thermal interface material may include the heat dissipation surface contact parameters of the thermal interface material and the heat absorption surface contact parameters of the thermal interface material. In this case, the contact thermal resistance of the thermal interface material includes: the heat dissipation surface contact thermal resistance of the thermal interface material and the heat absorption surface contact thermal resistance of the thermal interface material. Based on this, based on the target thermal conductivity of the thermal interface material and the surface contact parameters of the thermal interface material, the contact thermal resistance of the thermal interface material is determined, including:

[0135] Based on the target thermal conductivity of the thermal interface material and the heat dissipation surface contact parameters of the thermal interface material, the heat dissipation surface contact thermal resistance of the thermal interface material is determined. Based on the target thermal conductivity of the thermal interface material and the heat absorption surface contact parameters of the thermal interface material, the heat absorption surface contact thermal resistance of the thermal interface material is determined.

[0136] An embodiment of the present application provides a heat dissipation system, which may include a heat dissipation object, a heat sink, and a thermal interface material formed between the heat dissipation surface of the heat dissipation object and the heat absorption surface of the heat sink. The parameters of the thermal interface material can be determined by the method of the embodiment of the present application.

[0137] In practical applications, the parameters of the thermal interface material in the embodiments of this application include the target thermal resistance of the thermal interface material, but may also include the target thickness of the thermal interface material at a preset pressure. Furthermore, the heat dissipation target may be a chip or other heat-generating device, which will not be detailed here.

[0138] Figure 7 FIG1 shows a schematic diagram of the heat dissipation system structure of an embodiment of the present application, taking a chip heat dissipation system as an example. Figure 7 As shown, the chip heat dissipation system 700 of the embodiment of the present application may include a packaged chip 701 and a heat sink 702. A thermal interface material 703 is filled between the heat dissipation surface of the packaged chip 701 and the heat absorption surface of the heat sink 702. Here, the packaged chip 701 may include a chip 7011 and a cover 7012. The inner surface of the cover 7012 may be in contact with the chip 7011 through the thermal interface material.

[0139] like Figure 7As shown, the packaged chip 701 of the embodiment of the present application is a heat dissipation object, and the surface of the thermal interface material 703 in contact with the heat dissipation surface of the packaged chip 701 can be defined as a heat absorption surface in contact with the heat dissipation object, and the surface of the thermal interface material 703 in contact with the heat absorption surface of the radiator 702 can be defined as a heat absorption surface in contact with the radiator.

[0140] When the heat dissipation surface contact parameters of the thermal interface material include: the roughness Ra1 of the heat sink, and the effective contact area ratio A between the heat dissipation surface and the heat sink n1 / A r1 The heat dissipation contact resistance of the thermal interface material can be determined according to formula 9 and expressed as R c1 .

[0141]

[0142] Among them, A n1 Indicates the theoretical contact area between the heat dissipation surface and the radiator, A r1 The actual contact area between the heat dissipation surface and the radiator.

[0143] When the heat absorbing surface contact parameters of the thermal interface material include: the roughness Ra2 of the heat exchange object, and the effective contact area ratio A between the heat absorbing surface and the radiator n2 / A r2 The heat dissipation contact resistance of the thermal interface material can be determined according to formula 10 and expressed as R c2 .

[0144]

[0145] Among them, A n2 Indicates the theoretical contact area between the heat absorbing surface and the radiator, A r2 Indicates the actual contact area between the heat absorbing surface and the radiator.

[0146] It can be seen that the embodiment of the present application combines the target rheological characteristic parameters of the thermal interface material with the preset pressure, the surface roughness of the contact object of the thermal interface material (such as a heat sink or a heat dissipation object) and other information, which can accurately determine the contact thermal resistance of the thermal interface material, and then substitute the contact thermal resistance of the thermal interface material into the thermal resistance network diagram of the chip heat dissipation.

[0147] Step 603 : Determine a target thermal resistance of the thermal interface material at the preset pressure based on the target thickness of the thermal interface material at the preset pressure, the contact thermal resistance of the thermal interface material, and the target thermal conductivity of the thermal interface material.

[0148] When the contact thermal resistance of the thermal interface material includes: the heat dissipation surface contact thermal resistance R c1 Thermal resistance R of the heat absorbing surface of the thermal interface material c2In the embodiment of the present application, the target thermal resistance R of the thermal interface material under the preset pressure can be determined by referring to Formula 11. TIM , and based on the target thermal resistance R of the thermal interface material at a preset pressure TIM and the heat sink's thermal resistance R h Determine the total thermal resistance R of the heat dissipation system of the heat dissipation object, referring to Formula 12 for details.

[0149]

[0150] R=R TIM +R h Formula 12

[0151] In Formula 12, the thermal resistance of the heat sink is related to the material used, the shape, number, material, wind speed, etc. of the fins. Therefore, after knowing the power and temperature tolerance of the heat exchange object such as a chip, the thermal resistance of the thermal interface material can be determined based on the method of the embodiment of the present application. Then, combined with parameters such as ambient temperature and wind speed, the heat sink can be quickly selected and optimized to improve the design efficiency of the heat exchange system.

[0152] In one or more technical solutions provided by the embodiments of the present application, the component thermal conductivity parameters and component configuration parameters of the thermal interface material are used as inputs of the thermal conductivity prediction model, and the data mining and extrapolation capabilities of the thermal conductivity prediction model are used to perform data mining on the component thermal conductivity parameters and component configuration parameters of the thermal interface material, thereby quickly and accurately obtaining the thermal conductivity of the thermal interface material. Therefore, the embodiments of the present application can quickly and accurately determine the target thermal conductivity of the thermal interface material based on the thermal conductivity deviation of the thermal interface material and the theoretical thermal conductivity of the thermal interface material. Moreover, the embodiments of the present application can also utilize the fact that the thermal interface material follows the laws of non-Newtonian fluids to determine the yield stress prediction model through a nonlinear elastic-viscoplastic model, and then, using transient data of the thermal interface material, such as the mechanical parameters of the thermal interface material at multiple sampling moments, iterate the yield stress prediction model based on the mechanical parameters of the thermal interface material at multiple sampling moments, thereby quickly and accurately obtaining the target rheological characteristic parameters of the thermal interface material.

[0153] As can be seen, the method of the present embodiment can accurately and rapidly predict the target thermal conductivity and rheological parameters of the thermal interface material, thereby ensuring the accuracy of the predicted thermal resistance of the thermal interface. Once the power and temperature tolerance of the heat exchange target are known, this can be combined with other parameters such as the ambient temperature and wind speed in the area where the heat dissipation target is located to quickly select and optimize the heat sink design, thereby improving the efficiency of the heat dissipation system design.

[0154] In summary, the method of the embodiment of the present application can quickly and accurately predict the target thermal conductivity and target rheological characteristic parameters of the thermal interface material, which is conducive to shortening the determination cycle of the thermal interface material; at the same time, it can quickly match the thermal interface material used to optimize the heat dissipation performance according to the specific needs of different electronic devices, improve the overall system efficiency and stability of the heat dissipation system, and help servers and electronic devices to improve customized efficiency.

[0155] The above mainly introduces the solution provided by the embodiment of the present disclosure from the perspective of an electronic device. It is understandable that, in order to realize the above functions, the electronic device includes a hardware structure and / or software module corresponding to the execution of each function. Those skilled in the art should easily realize that, in combination with the units and algorithm steps of each example described in the embodiments disclosed herein, the present disclosure can be implemented in the form of hardware or a combination of hardware and computer software. Whether a function is executed in a hardware or computer software driven hardware manner depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of the present disclosure.

[0156] The embodiments of the present disclosure can divide the functional units of the electronic device according to the above method examples. For example, each functional module can be divided according to each function, or two or more functions can be integrated into one processing module. The above integrated modules can be implemented in the form of hardware or in the form of software functional modules. It should be noted that the division of modules in the embodiments of the present disclosure is schematic and is only a logical function division. In actual implementation, there may be other division methods.

[0157] In the case of dividing each functional module according to each function, an exemplary embodiment of the present disclosure provides a device for predicting thermal interface material parameters. The device for predicting thermal interface material parameters may be an electronic device or a chip applied to the electronic device. Figure 8 FIG. 1 shows a schematic block diagram of the functional modules of a device for predicting thermal interface material parameters according to an exemplary embodiment of the present disclosure. Figure 8 As shown, the thermal interface material parameter prediction device 800 includes:

[0158] Prediction module 801, for inputting component thermal conductivity parameters and component configuration parameters of the thermal interface material into a thermal conductivity prediction model to obtain a target thermal conductivity of the thermal interface material;

[0159] an iterative module 802 for iterating a yield stress prediction model based on mechanical parameters of the thermal interface material at multiple sampling moments to obtain target rheological characteristic parameters of the thermal interface material, wherein the yield stress prediction model is determined at least by a nonlinear elastic-viscoplastic model;

[0160] The determination module 803 is configured to determine target parameters of the thermal interface material at a preset pressure based on the target thermal conductivity and target rheological characteristic parameters of the thermal interface material, wherein the target parameters include at least a target thermal resistance.

[0161] In one possible implementation, the prediction module 801 is used to input the component thermal conductivity parameters and component configuration parameters of the thermal interface material into the first prediction model to obtain the thermal conductivity deviation of the thermal interface material; input the component thermal conductivity parameters and component configuration parameters of the thermal interface material into the effective medium theoretical model to obtain the theoretical thermal conductivity of the thermal interface material; and fuse the thermal conductivity deviation of the thermal interface material and the theoretical thermal conductivity of the thermal interface material based on the target fusion parameter to obtain the target thermal conductivity of the thermal interface material.

[0162] In one possible implementation, the prediction module 801 is further used to input the component thermal conductivity parameters and historical configuration parameters of the thermal interface material sample into the first prediction model to obtain a thermal conductivity deviation prediction result of the thermal interface material sample; input the component thermal conductivity parameters and historical configuration parameters of the thermal interface material sample into the effective medium theoretical model to determine the theoretical thermal conductivity of the thermal interface material sample; fuse the thermal conductivity deviation prediction result of the thermal interface material sample and the theoretical thermal conductivity of the thermal interface material sample based on the initial fusion parameters to obtain a thermal conductivity prediction result of the thermal interface material sample; determine the prediction loss of the thermal conductivity prediction model based on the thermal conductivity prediction result of the thermal interface material sample and the reference thermal conductivity of the thermal interface material sample; in response to the prediction loss of the thermal conductivity prediction model satisfying the convergence condition of the first prediction model, determine that the first prediction model has completed training, and set the target fusion parameter based on the initial fusion parameter.

[0163] In a possible implementation, the mechanical parameters of the thermal interface material at multiple sampling moments include a shear rate and a reference yield stress of the thermal interface material at multiple sampling moments.

[0164] In one possible implementation, the iterative module 802 is used to input the mechanical parameters of the thermal interface material at multiple sampling moments and the initial rheological characteristic parameters of the thermal interface material into the yield stress prediction model to obtain the first yield stress prediction result of the thermal interface material at each sampling moment, and the yield stress prediction model is determined by the finite difference discretization result of the nonlinear elastic-viscoplastic model; based on the first yield stress prediction result of the thermal interface material at multiple sampling moments and the reference yield stress of the thermal interface material at multiple sampling moments, the prediction loss of the yield stress prediction model is determined; when the prediction loss of the yield stress prediction model meets the convergence condition of the yield stress prediction model, the initial rheological characteristic parameters of the thermal interface material are determined as the target rheological characteristic parameters of the thermal interface material; when the prediction loss of the yield stress prediction model does not meet the convergence condition of the yield stress prediction model, the initial rheological characteristic parameters of the thermal interface material are updated.

[0165] In one possible implementation, the iterative module 802 is used to input the shear rate of the thermal interface material at each sampling moment into a second prediction model to obtain a second yield stress prediction result of the thermal interface material at each sampling moment; based on the second yield stress prediction result of the thermal interface material at each sampling moment and the reference yield stress of the thermal interface material at each sampling moment, determine the prediction residual of the second yield stress at each sampling moment; input the reference yield stress of the thermal interface material at each sampling moment, the shear rate of the thermal interface material at each sampling moment and the initial rheological characteristic parameters of the thermal interface material into a nonlinear elastic-viscoplastic residual model to determine the prediction residual of the nonlinear elasto-viscoplastic model at each sampling moment; determining the prediction loss of the yield stress prediction model based on the prediction residual of the second yield stress prediction result and the prediction residual of the nonlinear elasto-viscoplastic model at multiple sampling moments; in response to the prediction loss of the yield stress prediction model satisfying the convergence condition of the yield stress prediction model, determining the initial rheological characteristic parameters of the thermal interface material as the target rheological characteristic parameters of the thermal interface material; in response to the prediction loss of the yield stress prediction model not satisfying the convergence condition of the yield stress prediction model, updating the model parameters of the second prediction model and the initial rheological characteristic parameters of the thermal interface material.

[0166] In a possible implementation, the iteration module 802 is further configured to determine, based on the second prediction model, information about changes in the second prediction model over time; and determine the nonlinear elasto-visco-plastic residual model based on the information about changes in the second prediction model over time and the nonlinear elasto-visco-plastic model.

[0167] In one possible implementation, the target rheological characteristic parameters of the thermal interface material include a target yield stress of the thermal interface material, and the target parameters also include a target thickness. The determination module 803 is used to determine the target thickness of the thermal interface material at the preset pressure based on the target yield stress of the thermal interface material and the radial size of the heat dissipation object; determine the contact thermal resistance of the thermal interface material based on the target thermal conductivity of the thermal interface material and the surface contact parameters of the thermal interface material; and determine the target thermal resistance of the thermal interface material at the preset pressure based on the target thickness of the thermal interface material at the preset pressure, the contact thermal resistance of the thermal interface material, and the target thermal conductivity of the thermal interface material.

[0168] In one possible implementation, the thermal interface material has a heat dissipation surface in contact with the heat sink, and a heat absorption surface in contact with the heat dissipation object. The surface contact parameters of the thermal interface material include the heat dissipation surface contact parameters of the thermal interface material and the heat absorption surface contact parameters of the thermal interface material. The contact thermal resistance of the thermal interface material includes: the heat dissipation surface contact thermal resistance of the thermal interface material and the heat absorption surface contact thermal resistance of the thermal interface material.

[0169] In one possible implementation, the determination module 803 is used to determine the heat dissipation surface contact thermal resistance of the thermal interface material based on the target thermal conductivity of the thermal interface material and the heat dissipation surface contact parameters of the thermal interface material; and to determine the heat absorption surface contact thermal resistance of the thermal interface material based on the target thermal conductivity of the thermal interface material and the heat absorption surface contact parameters of the thermal interface material.

[0170] In a possible implementation, the heat dissipation surface contact parameters of the thermal interface material include: roughness of the heat sink, and an effective contact area ratio between the heat dissipation surface and the heat sink;

[0171] The heat absorbing surface contact parameters of the thermal interface material include: the roughness of the heat exchange object, and the effective contact area ratio between the heat absorbing surface and the heat sink.

[0172] Figure 9 FIG. 1 shows a schematic block diagram of a chip according to an exemplary embodiment of the present disclosure. Figure 9 As shown, the chip 900 includes one or more (including two) processors 901 and a communication interface 902. The communication interface 902 can support the electronic device to perform the data sending and receiving steps in the above method, and the processor 901 can support the electronic device to perform the data processing steps in the above method.

[0173] Optional, such as Figure 9As shown, the chip 900 also includes a memory 903, which may include a read-only memory and a random access memory, and provides operation instructions and data to the processor. A portion of the memory may also include a non-volatile random access memory (NVRAM).

[0174] In some embodiments, as Figure 9 As shown, the processor 901 performs corresponding operations by calling the operation instructions stored in the memory (the operation instructions may be stored in the operating system). The processor 901 controls the processing operations of any one of the terminal devices, and the processor may also be called a central processing unit (CPU). The memory 903 may include a read-only memory and a random access memory, and provides instructions and data to the processor 901. A portion of the memory 903 may also include NVRAM. For example, in an application, the memory, the communication interface, and the memory are coupled together through a bus system, wherein the bus system may include a power bus, a control bus, and a status signal bus in addition to a data bus. However, for the sake of clarity, in Figure 9 Various buses are labeled as bus system 904.

[0175] The methods disclosed in the above embodiments of the present disclosure can be applied to or implemented by a processor. The processor may be an integrated circuit chip with signal processing capabilities. During implementation, each step of the above method can be completed by hardware integrated logic circuits in the processor or by software instructions. The above processor may be a general-purpose processor, a digital signal processor (DSP), an ASIC, a field-programmable gate array (FPGA), or other programmable logic device, a discrete gate or transistor logic device, or a discrete hardware component. The methods, steps, and logic block diagrams disclosed in the embodiments of the present disclosure can be implemented or executed. The general-purpose processor may be a microprocessor or any conventional processor. The steps of the methods disclosed in conjunction with the embodiments of the present disclosure can be directly implemented and executed by a hardware decoding processor, or by a combination of hardware and software modules in the decoding processor. The software module can be located in a storage medium well-known in the art, such as random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, registers, etc. The storage medium is located in the memory, and the processor reads the information in the memory and, in conjunction with its hardware, completes the steps of the above method.

[0176] The exemplary embodiments of the present disclosure further provide an electronic device, comprising: at least one processor; and a memory communicatively connected to the at least one processor. The memory stores a computer program executable by the at least one processor, the computer program being configured to cause the electronic device to perform a method according to an exemplary embodiment of the present disclosure when executed by the at least one processor.

[0177] Exemplary embodiments of the present disclosure further provide a non-transitory computer-readable storage medium storing a computer program, wherein the computer program, when executed by a processor of a computer, is used to cause the computer to perform a method according to an embodiment of the present disclosure.

[0178] Exemplary embodiments of the present disclosure further provide a computer program product, including a computer program, wherein when the computer program is executed by a processor of a computer, it is used to cause the computer to perform the method according to the embodiment of the present disclosure.

[0179] refer to Figure 10 , a block diagram of an electronic device 1000 that can serve as a server or client of the present disclosure will now be described, which is an example of a hardware device that can be applied to various aspects of the present disclosure. The electronic device is intended to represent various forms of digital electronic computer devices, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present disclosure described and / or required herein.

[0180] like Figure 10 As shown, electronic device 1000 includes a computing unit 1001, which can perform various appropriate actions and processes according to a computer program stored in a read-only memory (ROM) 1002 or a computer program loaded from a storage unit 1008 into a random access memory (RAM) 1003. Various programs and data required for the operation of device 1000 can also be stored in RAM 1003. Computing unit 1001, ROM 1002, and RAM 1003 are connected to each other via a bus 1004. An input / output (I / O) interface 1005 is also connected to bus 1004.

[0181] like Figure 10As shown, multiple components within electronic device 1000 are connected to I / O interface 1005, including an input unit 1006, an output unit 1007, a storage unit 1008, and a communication unit 1009. Input unit 1006 can be any type of device capable of inputting information into electronic device 1000. Input unit 1006 can receive input numeric or character information and generate key input signals related to user settings and / or function control of the electronic device. Output unit 1007 can be any type of device capable of presenting information and may include, but is not limited to, a display, a speaker, a video / audio output terminal, a vibrator, and / or a printer. Storage unit 1008 may include, but is not limited to, a magnetic disk or an optical disk. Communication unit 1009 allows electronic device 1000 to exchange information / data with other devices via computer networks such as the Internet and / or various telecommunication networks, and may include, but is not limited to, a modem, a network card, an infrared communication device, a wireless communication transceiver and / or a chipset, such as a Bluetooth™ device, a WiFi device, a WiMax device, a cellular communication device, and / or the like.

[0182] like Figure 10 As shown, the computing unit 1001 can be various general and / or special processing components with processing and computing capabilities. Some examples of the computing unit 1001 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various computing units that run machine learning model algorithms, digital signal processors (DSPs), and any appropriate processors, controllers, microcontrollers, etc. The computing unit 1001 performs the various methods and processes described above. For example, in some embodiments, the method of the embodiment of the present application can be implemented as a computer software program, which is tangibly included in a machine-readable medium, such as a storage unit 1008. In some embodiments, part or all of the computer program can be loaded and / or installed on the electronic device 1000 via the ROM 1002 and / or the communication unit 1009. In some embodiments, the computing unit 1001 can be configured to perform the method of the embodiment of the present application by any other appropriate means (e.g., by means of firmware).

[0183] The program code for implementing the method of the present disclosure can be written in any combination of one or more programming languages. These program codes can be provided to a processor or controller of a general-purpose computer, a special-purpose computer, or other programmable data processing device so that when the program code is executed by the processor or controller, the functions / operations specified in the flow chart and / or block diagram are implemented. The program code can be executed entirely on the machine, partially on the machine, as a stand-alone software package, partially on the machine and partially on a remote machine, or entirely on a remote machine or server.

[0184] In the context of the present disclosure, a machine-readable medium can be a tangible medium that can contain or store a program for use by or in conjunction with an instruction execution system, device or equipment. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium can include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or equipment, or any suitable combination of the foregoing. A more specific example of a machine-readable storage medium can include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0185] As used in this disclosure, the terms "machine-readable medium" and "computer-readable medium" refer to any computer program product, apparatus, and / or device (e.g., a magnetic disk, an optical disk, a memory, a programmable logic device (PLD)) for providing machine instructions and / or data to a programmable processor, including machine-readable media that receive machine instructions as machine-readable signals. The term "machine-readable signal" refers to any signal used to provide machine instructions and / or data to a programmable processor.

[0186] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user can provide input to the computer. Other types of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0187] The systems and techniques described herein can be implemented in a computing system that includes back-end components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes front-end components (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such back-end components, middleware components, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include a local area network (LAN), a wide area network (WAN), and the Internet.

[0188] Computer systems may include clients and servers. A client and server are generally remote from each other and typically interact through a communication network. The client and server relationship arises through computer programs running on the respective computers and having a client-server relationship to each other.

[0189] In the above embodiments, they can be implemented in whole or in part by software, hardware, firmware, or any combination thereof. When implemented using software, they can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer programs or instructions. When the computer program or instructions are loaded and executed on a computer, the processes or functions described in the embodiments of the present disclosure are performed in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, a terminal, a user device, or other programmable device. The computer program or instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer program or instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired or wireless means. The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or data center that integrates one or more available media. The available medium can be a magnetic medium, such as a floppy disk, a hard disk, or a tape; it can also be an optical medium, such as a digital video disc (DVD); it can also be a semiconductor medium, such as a solid state drive (SSD).

[0190] Although the present disclosure has been described with reference to specific features and embodiments thereof, it will be apparent that various modifications and combinations may be made thereto without departing from the spirit and scope of the present disclosure. Accordingly, this specification and the drawings are merely illustrative of the present disclosure as defined by the appended claims and are deemed to cover any and all modifications, variations, combinations or equivalents within the scope of the present disclosure. Obviously, those skilled in the art may make various modifications and variations to the present disclosure without departing from the spirit and scope of the present disclosure. Thus, the present disclosure is intended to include such modifications and variations if they fall within the scope of the claims of the present disclosure and their equivalents.

Claims

1. A method for predicting thermal interface material parameters, characterized in that: The method comprises: Inputting component thermal conductivity parameters and component configuration parameters of the thermal interface material into a thermal conductivity prediction model to obtain a target thermal conductivity of the thermal interface material; Iterating a yield stress prediction model based on the mechanical parameters of the thermal interface material at multiple sampling moments to obtain target rheological characteristic parameters of the thermal interface material, wherein the yield stress prediction model is determined at least by a nonlinear elastic-viscoplastic model; Based on the target thermal conductivity of the thermal interface material and the target rheological characteristic parameters of the thermal interface material, target parameters of the thermal interface material under a preset pressure are determined, where the target parameters at least include a target thermal resistance.

2. The method according to claim 1, characterized in that The thermal conductivity prediction model includes a first prediction model and an effective medium theory model. Inputting component thermal conductivity parameters and component configuration parameters of the thermal interface material into the thermal conductivity prediction module to obtain a target thermal conductivity of the thermal interface material includes: Inputting the component thermal conductivity parameters and component configuration parameters of the thermal interface material into the first prediction model to obtain the thermal conductivity deviation of the thermal interface material; Inputting the component thermal conductivity parameters and component configuration parameters of the thermal interface material into the effective medium theoretical model to obtain the theoretical thermal conductivity of the thermal interface material; The thermal conductivity deviation of the thermal interface material and the theoretical thermal conductivity of the thermal interface material are fused based on the target fusion parameter to obtain the target thermal conductivity of the thermal interface material.

3. The method according to claim 2, characterized in that The method further comprises: Inputting the component thermal conductivity parameters and historical configuration parameters of the thermal interface material sample into the first prediction model to obtain a thermal conductivity deviation prediction result of the thermal interface material sample; Inputting the component thermal conductivity parameters and historical configuration parameters of the thermal interface material sample into the effective medium theory model to determine the theoretical thermal conductivity of the thermal interface material sample; fusing the thermal conductivity deviation prediction result of the thermal interface material sample and the theoretical thermal conductivity of the thermal interface material sample based on the initial fusion parameters to obtain a thermal conductivity prediction result of the thermal interface material sample; determining a predicted loss of a thermal conductivity prediction model based on a thermal conductivity prediction result of the thermal interface material sample and a reference thermal conductivity of the thermal interface material sample; In response to the prediction loss of the thermal conductivity prediction model satisfying the convergence condition of the first prediction model, it is determined that the first prediction model has completed training, and the initial fusion parameters are set as target fusion parameters.

4. The method according to claim 1, wherein The mechanical parameters of the thermal interface material at multiple sampling moments include a shear rate and a reference yield stress of the thermal interface material at multiple sampling moments.

5. The method according to claim 4, characterized in that The iterating of the yield stress prediction model based on the mechanical parameters of the thermal interface material at multiple sampling moments to obtain target rheological characteristic parameters of the thermal interface material includes: Inputting the mechanical parameters of the thermal interface material at multiple sampling moments and the initial rheological characteristic parameters of the thermal interface material into the yield stress prediction model to obtain a first yield stress prediction result of the thermal interface material at each sampling moment, wherein the yield stress prediction model is determined by a finite difference discretization result of the nonlinear elastic-viscoplastic model; determining a prediction loss of the yield stress prediction model based on first yield stress prediction results of the thermal interface material at multiple sampling moments and reference yield stresses of the thermal interface material at multiple sampling moments; In response to the predicted loss of the yield stress prediction model satisfying a convergence condition of the yield stress prediction model, determining the initial rheological characteristic parameters of the thermal interface material as target rheological characteristic parameters of the thermal interface material; In response to the predicted loss of the yield stress prediction model not satisfying a convergence condition of the yield stress prediction model, the initial rheological characteristic parameters of the thermal interface material are updated.

6. The method according to claim 4, characterized in that The yield stress prediction model includes a second prediction model and a nonlinear elastic-viscoplastic residual model. The yield stress prediction model is iterated based on the mechanical parameters of the thermal interface material at multiple sampling moments to obtain target rheological characteristic parameters of the thermal interface material, including: Inputting the shear rate of the thermal interface material at each sampling moment into a second prediction model to obtain a second yield stress prediction result of the thermal interface material at each sampling moment; Determining a prediction residual of the second yield stress at each sampling moment based on a prediction result of the second yield stress of the thermal interface material at each sampling moment and a reference yield stress of the thermal interface material at each sampling moment; Inputting the reference yield stress of the thermal interface material at each sampling moment, the shear rate of the thermal interface material at each sampling moment, and the initial rheological characteristic parameters of the thermal interface material into a nonlinear elasto-viscoplastic residual model to determine the prediction residual of the nonlinear elasto-viscoplastic model at each sampling moment; determining a prediction loss of a yield stress prediction model based on prediction residuals of the second yield stress at multiple sampling moments and prediction residuals of the nonlinear elastic-viscoplastic model at multiple sampling moments; In response to the predicted loss of the yield stress prediction model satisfying a convergence condition of the yield stress prediction model, determining the initial rheological characteristic parameters of the thermal interface material as target rheological characteristic parameters of the thermal interface material; In response to the predicted loss of the yield stress prediction model not satisfying a convergence condition of the yield stress prediction model, model parameters of the second prediction model and initial rheological characteristic parameters of the thermal interface material are updated.

7. The method according to claim 6, characterized in that The method further comprises: Determining, based on the second prediction model, change information of the second prediction model over time; The nonlinear elasto-visco-plastic residual model is determined based on the change information of the second prediction model over time and the nonlinear elasto-visco-plastic model.

8. The method according to any one of claims 1 to 7, characterized in that The target rheological characteristic parameters of the thermal interface material include a target yield stress of the thermal interface material, and the target parameters also include a target thickness. Determining the target parameters of the thermal interface material under a preset pressure based on the target thermal conductivity of the thermal interface material and the target rheological characteristic parameters of the thermal interface material includes: determining a target thickness of the thermal interface material under the preset pressure based on a target yield stress of the thermal interface material and a radial dimension of the heat dissipation object; determining a thermal contact resistance of the thermal interface material based on a target thermal conductivity of the thermal interface material and a surface contact parameter of the thermal interface material; A target thermal resistance of the thermal interface material at the preset pressure is determined based on the target thickness of the thermal interface material at the preset pressure, the contact thermal resistance of the thermal interface material, and the target thermal conductivity of the thermal interface material.

9. The method according to claim 8, characterized in that The thermal interface material has a heat dissipation surface in contact with the heat sink and a heat absorption surface in contact with the heat dissipation object. The surface contact parameters of the thermal interface material include the heat dissipation surface contact parameters of the thermal interface material and the heat absorption surface contact parameters of the thermal interface material. The contact thermal resistance of the thermal interface material includes: the heat dissipation surface contact thermal resistance of the thermal interface material and the heat absorption surface contact thermal resistance of the thermal interface material.

10. The method according to claim 8, characterized in that Determining the contact thermal resistance of the thermal interface material based on the target thermal conductivity of the thermal interface material and the surface contact parameter of the thermal interface material includes: determining a heat dissipation surface contact thermal resistance of the thermal interface material based on a target thermal conductivity of the thermal interface material and a heat dissipation surface contact parameter of the thermal interface material; The heat absorbing surface contact thermal resistance of the thermal interface material is determined based on the target thermal conductivity of the thermal interface material and the heat absorbing surface contact parameter of the thermal interface material.

11. The method according to claim 8, characterized in that The heat dissipation surface contact parameters of the thermal interface material include: the roughness of the heat sink, and the effective contact area ratio between the heat dissipation surface and the heat sink; The heat absorbing surface contact parameters of the thermal interface material include: the roughness of the heat exchange object, and the effective contact area ratio between the heat absorbing surface and the heat sink.

12. A device for predicting thermal interface material parameters, characterized in that: The device comprises: A prediction module, for inputting component thermal conductivity parameters and component configuration parameters of the thermal interface material into a thermal conductivity prediction model to obtain a target thermal conductivity of the thermal interface material; an iterative module, configured to iterate a yield stress prediction model based on mechanical parameters of the thermal interface material at multiple sampling moments to obtain target rheological characteristic parameters of the thermal interface material, wherein the yield stress prediction model is determined at least by a nonlinear elastic-viscoplastic model; A determination module is configured to determine target parameters of the thermal interface material at a preset pressure based on a target thermal conductivity of the thermal interface material and a target rheological characteristic parameter of the thermal interface material, wherein the target parameters include at least a target thermal resistance.

13. A heat dissipation system, characterized in that: The invention comprises a heat dissipation object, a heat sink and a thermal interface material formed between the heat dissipation surface of the heat dissipation object and the heat absorption surface of the heat sink, wherein the parameters of the thermal interface material are determined by the method according to any one of claims 1 to 11.

14. An electronic device, characterized in that: include: processor; as well as, Memory for storing programs; The program includes instructions, which, when executed by the processor, cause the processor to perform the method according to any one of claims 1 to 11.

15. A non-transitory computer-readable storage medium, characterized in that The non-transitory computer-readable storage medium stores computer instructions, and the computer instructions are used to cause the computer to execute the method according to any one of claims 1 to 11.

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