Interface thermal conductivity coefficient distribution optimization design method and system for improving thermal contact performance of assembly interface

Abstract

Disclosed in the present invention are an interface thermal conductivity coefficient distribution optimization design method and system for improving the thermal contact performance of an assembly interface, the method comprising: acquiring microscopic morphological features of a contact heat dissipation surface of a component of an electronic chip packaging system; constructing a thermal contact performance numerical analysis model; using the thermal contact performance numerical analysis model to perform multiple iterations on the thermal conductivity coefficient distribution of a design area to obtain a thermal conductivity coefficient distribution and thermal contact resistance; on the basis of the thermal conductivity coefficient distribution and the thermal contact resistance, determining whether each iteration meets an iteration convergence criterion; and if the iteration convergence criterion is met, outputting the thermal conductivity coefficient distribution and the thermal contact resistance. In the present invention, the temperature transfer path of an assembly interface is controlled by designing the thermal conductivity coefficient distribution of the surface of the assembly interface, and the innovative design thinking of a "heterogeneous" thermal conductivity coefficient distribution is used to truly achieve a uniform temperature gradient distribution.

Description

A method and system for optimizing the distribution of interfacial thermal conductivity to improve the contact thermal performance of assembly interfaces. Technical Field

[0001] This invention belongs to the field of mechanical design technology, specifically a method and system for optimizing the distribution of interface thermal conductivity to improve the contact thermal performance of assembly interfaces. Background Technology

[0002] The trend towards miniaturization, integration, and high assembly density in electronic devices has led to a significant accumulation of heat, resulting in insufficient system heat dissipation and severely impacting the operational performance and stability of electronic devices throughout their entire service life. The journal *Nature* has pointed out that due to the inability to solve the heat dissipation problem, "Moore's Law" is about to become obsolete. How to effectively ensure the heat dissipation performance of electronic devices has become a pressing issue. Electronic devices are assemblies with specific functions, assembled from components using certain connection methods. Internal loads and physical characteristics are transferred between the various structures through the assembly interfaces. The assembly interfaces disrupt the continuity of temperature and stress transmission between electronic device components, and their contact thermal resistance (TCR) accounts for a very high proportion of the overall thermal resistance, far exceeding the impact of bulk thermal resistance. TCR caused by non-ideal contact conditions at the assembly interfaces is the main reason for the insufficient contact thermal performance and overall performance of electronic devices. As a multidisciplinary problem involving mechanics, heat transfer, materials, and other related fields, TCR has significant research and practical application value. Reducing TCR can effectively improve heat transfer efficiency, prevent overheating, damage, performance degradation, and problems caused by localized thermal stress and hot spots, thereby improving the reliability and service life of the equipment. Therefore, finding new and efficient technologies to improve contact heat transfer performance has become a prominent issue in addressing the thermal management challenges of heat-sensitive equipment in various fields.

[0003] Existing macroscopic control and surface treatment technologies for enhancing contact thermal performance are insufficient. Thermal interface materials also face challenges in preparation and their thickness cannot adapt to the trend of integrated development, resulting in a technological ceiling for enhancing contact thermal performance. Furthermore, considering the essence of contact thermal resistance formation, its physical nature stems from insufficient and uneven contact due to rough and uneven interfaces, leading to inadequate contact thermal performance. From an energy dissipation perspective, this is due to the failure to minimize heat transfer energy dissipation at the assembly interface. Technical issues

[0004] This invention addresses the bottleneck in improving contact thermal performance caused by the existing "homogenization" design mindset. The aim is to provide a method and system for optimizing the distribution of interface thermal conductivity to improve the contact thermal performance of the assembly interface. By actively designing the distribution of thermal conductivity at the assembly interface, the invention aims to reduce the contact thermal resistance at the assembly interface, thereby effectively ensuring the service life and stability of heat-sensitive mechanical equipment such as electronic chip packaging systems. Technical solutions

[0005] To achieve the objective of this invention, the technical solution adopted is as follows:

[0006] A method for optimizing the distribution of interfacial thermal conductivity to improve the contact thermal performance of assembly interfaces includes the following steps:

[0007] To obtain the microscopic morphological characteristics of the contact heat dissipation surface of components in an electronic chip packaging system;

[0008] Based on the microstructure characteristics of the contact heat dissipation surface of the component, a numerical analysis model of contact thermal performance is constructed.

[0009] The thermal conductivity distribution of the design area was iterated multiple times using a numerical analysis model of contact thermal performance to obtain the thermal conductivity distribution and contact thermal resistance.

[0010] The thermal conductivity distribution and contact thermal resistance are used to determine whether each iteration meets the iteration convergence criterion. If the iteration convergence criterion is met, the thermal conductivity distribution and contact thermal resistance are output. If the iteration convergence criterion is not met, the thermal conductivity distribution of the next iteration step is calculated, and the thermal conductivity distribution of the design area in the contact thermal performance numerical analysis model is updated. The process of iterating the thermal conductivity distribution of the design area multiple times using the contact thermal performance numerical analysis model is then returned.

[0011] Furthermore, a laser confocal roughness testing system was used to obtain the microscopic morphological characteristics of the contact heat dissipation surface of key components.

[0012] Furthermore, by mapping the rough surface to the contact surface grid cell nodes with the same sampling rate, a numerical analysis model of the contact thermal performance considering the real micro-rough surface is constructed.

[0013] Furthermore, the iterative design criterion for thermal conductivity distribution is a principle for uniform temperature gradient distribution, extended from the principle of minimizing energy dissipation in the heat transfer process, and is stated as follows:

[0014] (1)

[0015] (2)

[0016] in, This represents the grid cell representing the design region at the (n+1)th iteration step. thermal conductivity, This represents the design region grid cell for the nth iteration step. thermal conductivity, Design regional grid units temperature gradient, Indicates regulatory factor, This indicates the minimum thermal conductivity limit for the design area. This indicates the maximum limit of thermal conductivity in the design area.

[0017] Furthermore, the goal of the iterative design criterion for thermal conductivity distribution is to achieve uniform temperature gradient and minimize contact thermal resistance by optimizing the thermal conductivity distribution.

[0018] Furthermore, the design area is the assembly area with a thickness set in the direction perpendicular to the contact surface, and the design sub-area is each grid cell within the design area.

[0019] Furthermore, the contact thermal resistance (TCR) is calculated using the following formula:

[0020] (3)

[0021] in, This indicates the average temperature of the contact surfaces on the assembly. This indicates the average temperature of the contact surfaces under the assembly. This represents the average heat flux across the contact surfaces of the assembly. This represents the average heat flux across the lower contact surface of the assembly.

[0022] Furthermore, the iterative convergence criteria include convergence of the maximum number of iterations, convergence of the optimization rate of the design objective, and convergence of the rate of change of the design objective between two adjacent iterations.

[0023] Furthermore, the iterative convergence criterion is:

[0024] (4)

[0025] in, Indicates the maximum number of iterations. , , This represents the TCR settlement results for the (n-1), n, and n+1th iterations. This represents the initial TCR result before optimization. This represents the convergence threshold for the reduction rate of the optimization objective. This represents the convergence threshold of the rate of change of adjacent optimization objectives.

[0026] A system for optimizing the distribution of interfacial thermal conductivity to improve the contact thermal performance of assembly interfaces, comprising:

[0027] The micro-morphology feature acquisition module is used to acquire the micro-morphology features of the contact heat dissipation surface of the component.

[0028] The contact thermal performance numerical analysis model building module is used to build a contact thermal performance numerical analysis model based on the micro-morphological characteristics of the contact heat dissipation surface of the component.

[0029] The module for calculating thermal conductivity distribution and contact thermal resistance is used to perform multiple iterative calculations on the thermal conductivity distribution of the design area using a numerical analysis model of contact thermal performance, so as to obtain the thermal conductivity distribution and contact thermal resistance.

[0030] The judgment module is used to determine whether each iteration meets the iteration convergence criterion based on the thermal conductivity distribution and contact thermal resistance. If the iteration convergence criterion is met, the thermal conductivity distribution and contact thermal resistance are output. If the iteration convergence criterion is not met, the thermal conductivity distribution of the next iteration step is calculated, and the thermal conductivity distribution of the design area in the contact thermal performance numerical analysis model is updated. The module then returns the steps of iterating the thermal conductivity distribution of the design area multiple times using the contact thermal performance numerical analysis model. Beneficial effects

[0031] Compared with the prior art, the present invention has the following beneficial effects:

[0032] This invention, for the first time, analyzes the physical essence of contact thermal resistance formation from the perspective of energy dissipation mechanisms during heat transfer. Addressing the limitations of existing macroscopic control and surface treatment technologies for enhancing contact thermal performance, as well as the difficulties in preparing thermal interface materials and their inability to adapt their thickness to the trend of integrated development, this invention creatively proposes controlling the temperature transfer path at the assembly interface by designing the surface thermal conductivity distribution. It applies an innovative design concept of "heterogeneous" thermal conductivity distribution to truly achieve uniform temperature gradient distribution, overcoming the uneven temperature gradient distribution caused by the traditional "uniform" thermal conductivity distribution at the assembly interface. Furthermore, by minimizing energy dissipation, it effectively reduces contact thermal resistance. This invention breaks through the bottleneck of contact thermal performance improvement caused by traditional "uniform" design thinking, fundamentally solving the problem of insufficient contact thermal performance caused by contact thermal resistance during heat transfer, based on the physical essence of heat transfer. It significantly improves the contact thermal performance of heat-sensitive mechanical equipment such as electronic chip packaging systems, thereby effectively ensuring their service life and stability. Attached Figure Description

[0033] Figure 1 is a flowchart of the interface thermal conductivity distribution optimization design method for improving the contact thermal performance of the assembly interface according to the present invention;

[0034] Figure 2 is a schematic diagram of the numerical analysis model;

[0035] Figure 3 shows the temperature distribution cloud map of the master and slave assembly interfaces before the optimized design; where (a) is the master assembly interface and (b) is the slave assembly interface.

[0036] Figure 4 shows the temperature distribution cloud map of the master and slave assembly interfaces after the optimized design; where (a) is the master assembly interface and (b) is the slave assembly interface.

[0037] Figure 5 is a schematic diagram of the interface thermal conductivity distribution optimization design system for improving the contact thermal performance of the assembly interface according to the present invention. Embodiments of the present invention

[0038] To facilitate understanding of the present invention, a more complete description will be given below with reference to the accompanying drawings. Preferred embodiments of the invention are shown in the drawings. However, the invention can be implemented in many different forms and is not limited to the embodiments described herein. Rather, these embodiments are provided to provide a thorough and complete understanding of the disclosure of the invention.

[0039] By proactively designing the thermal conductivity distribution of the assembly interface to minimize energy dissipation, this invention achieves uniform temperature gradient distribution and thus minimizes energy dissipation. This enables rapid and precise layout of the thermal conductivity of the assembly interface to match optimal contact thermal performance. This invention overcomes the bottleneck in improving contact thermal performance caused by traditional "uniformity" design thinking, significantly improving the contact thermal performance of heat-sensitive mechanical equipment such as electronic chip packaging systems, thereby effectively ensuring their service life and stability.

[0040] This invention adopts an innovative design concept of "heterogeneous" thermal conductivity distribution. By actively designing the thermal conductivity distribution of the assembly interface, it improves the contact thermal performance and effectively ensures the service performance and stability of heat-sensitive mechanical equipment such as electronic chip packaging systems.

[0041] Referring to Figure 1, the present invention proposes an interface thermal conductivity distribution optimization design method to improve the contact thermal performance of assembly interfaces, comprising the following steps:

[0042] 1) Constructing a numerical analysis model for contact thermal performance: The main reason for insufficient heat dissipation performance in heat-sensitive mechanical equipment such as electronic chip packaging systems is the contact thermal resistance caused by the microscopic roughness of the assembly interface surface. Reconstructing the actual microscopic morphology of the contact surface is the prerequisite and necessary condition for constructing a numerical analysis model for contact thermal resistance. Therefore, we first use a laser confocal roughness testing system to obtain the microscopic morphology characteristics of the contact heat dissipation surface of key components. By mapping these characteristics to the contact surface mesh unit nodes with the same sampling rate, we reconstruct the rough surface and then construct a numerical analysis model for contact thermal performance that considers the actual microscopic roughness of the surface.

[0043] It should be noted that, in order to ensure that the reconstructed rough contact surface meets the requirements of both computational accuracy and computation time, it is necessary to conduct a mesh independence analysis of the numerical analysis model that depends on the sampling rate of the rough surface, and then find the minimum micro-morphology sampling rate required to meet the computational accuracy requirements. The constructed contact thermal performance numerical analysis model is a mechanical-heat transfer dual-iteration sequential coupling analysis that considers secondary thermal effects such as thermal stress and thermal softening. The final contact state in the mechanical analysis is used as the initial state for the heat transfer analysis to achieve the transfer of mechanical analysis results. A method of defining a predefined temperature field is used to initialize the mechanical analysis with the entire temperature distribution process in the heat transfer analysis to achieve the transfer of heat transfer analysis results to mechanical analysis results. This enables high-accuracy analysis of contact thermal performance and can be used for contact thermal performance analysis under various working conditions.

[0044] 2) Defining the design region and design sub-regions: The optimization design method of this invention seeks the thermal conductivity distribution of the assembly interface that minimizes contact thermal resistance under the constraint of thermal conductivity integral. Its physical equation can be expressed as:

[0045] (1)

[0046] in, This indicates the thermal conductivity distribution in the design area. Indicates the design of sub-region grid cells. This represents the thermal conductivity of the grid cells in the design sub-region. This represents the initial thermal conductivity value. This represents the size of the design region. Furthermore, to find the optimal thermal conductivity distribution, the original constraint on the total integral of the thermal conductivity of the design region can be changed to no constraint, in order to find the thermal conductivity distribution that satisfies the best contact thermal performance.

[0047] Therefore, it is first necessary to define the assembly region with a certain thickness in the direction perpendicular to the contact surface as the design region. Each grid cell in the design region is a design sub-region, the thermal conductivity of each design sub-region is a design sub-variable, and the distribution of thermal conductivity in the entire design region is the entire design variable.

[0048] The thickness of the design region is determined based on the actual manufacturing process and assembly dimensions. Different design region thicknesses significantly impact the optimization design results; theoretically, increasing the design region thickness can improve the optimization effect. Therefore, the optimal design region thickness needs to be determined based on actual requirements. To ensure precise control of the thermal conductivity distribution, each grid cell in the design region is the smallest design sub-region. Of course, to match the dimensional constraints of the actual manufacturing process, the number of grid cells contained in each sub-design region can be defined according to actual needs. Different sub-design region sizes correspond to different optimal thermal conductivity distributions and optimized contact thermal performance results.

[0049] 3) Define the thermal conductivity distribution of the design region: Define the unit thermal conductivity of each design sub-region, initialize the thermal conductivity distribution to a uniform value as in traditional design thinking, obtain the defined thermal conductivity distribution of the design region, and use this as a reference result before optimizing the design. Subsequently, in order to find the optimal thermal conductivity distribution, redefine the material thermal conductivity distribution of the design region in the numerical analysis model based on the updated thermal conductivity distribution in each iteration step.

[0050] 4) Calculate the temperature gradient distribution and contact thermal performance of the design area: Based on the thermal conductivity distribution of the design area defined in step 3), the temperature gradient distribution of the design area is calculated using the contact thermal performance numerical analysis model. This calculation is used for updating and iterating the design variables. The design objective, i.e., the contact thermal resistance, is then solved under the thermal conductivity distribution of the design area defined in step 3), providing a basis for judging iterative convergence. The design objective, i.e., the contact thermal resistance TCR, is defined as:

[0051] (2)

[0052] in, This indicates the average temperature of the contact surfaces on the assembly. This indicates the average temperature of the contact surfaces under the assembly. This represents the average heat flux across the contact surfaces of the assembly. This represents the average heat flux across the contact surface of the assembly. Therefore, based on the contact surface temperature and heat flux distribution results obtained from the numerical analysis of contact thermal performance, the contact thermal resistance is calculated under different thermal conductivity distribution conditions within the design region.

[0053] 5) Determine if the convergence condition is met: The iterative design criterion for thermal conductivity distribution is the principle of uniform temperature gradient distribution, which is extended from the principle of minimizing energy dissipation in the heat transfer process. The detailed formula is as follows:

[0054] (3)

[0055] (4)

[0056] in, This represents the grid cell representing the design region at the (n+1)th iteration step. thermal conductivity, This represents the design region grid cell for the nth iteration step. thermal conductivity, Design regional grid units temperature gradient, This represents a control factor used to determine whether the thermal conductivity distribution meets the total integral constraint. If it equals 1, the total integral of the thermal conductivity distribution in the design region is restricted; if it is greater than 1, the optimal thermal conductivity distribution is sought under unrestricted thermal conductivity conditions. This indicates the minimum thermal conductivity limit for the design area. This indicates the maximum limit of thermal conductivity in the design area.

[0057] The specific steps for updating the thermal conductivity are to apply iterative design criteria and update the thermal conductivity distribution for the next iteration based on the temperature gradient distribution and thermal conductivity distribution obtained in the previous iteration. The ultimate goal is to achieve temperature gradient homogenization by optimizing the thermal conductivity distribution, thereby minimizing the contact thermal resistance.

[0058] The thermal conductivity distribution k of the design region is iterated multiple times using the iterative design criterion of thermal conductivity distribution to obtain the design target result. Based on the design target result, it is determined whether each iteration meets the iteration convergence criterion. If the iteration convergence criterion is met, the next step 6) is continued. If the iteration convergence criterion is not met, the thermal conductivity distribution of the next iteration step is calculated based on the iterative design criterion, and the thermal conductivity distribution of the design region in the numerical analysis model is updated, and the next step 4) is continued.

[0059] Iterative convergence criteria include three aspects: convergence of the maximum number of iterations, convergence of the optimization rate of the design objective, and convergence of the rate of change of the design objective between two adjacent iterations. Specifically, they can be expressed as follows:

[0060] (5)

[0061] in, Indicates the maximum number of iterations. , , This represents the TCR settlement results for the (n-1), n, and n+1th iterations. This represents the initial TCR result before optimization. This represents the convergence threshold for the reduction rate of the optimization objective. This represents the convergence threshold of the rate of change of adjacent optimization objectives.

[0062] 6) Output the optimized thermal conductivity distribution and contact thermal resistance calculation results that satisfy the iterative convergence criterion. The optimized thermal conductivity distribution output is set according to the actual optimization design. The thermal conductivity distribution in the vertical direction of the assembly interface is uniform or differentiated. Differentiated distribution can obtain better contact thermal performance but at the same time brings processing difficulties.

[0063] Example 1

[0064] This invention uses a concrete key assembly interface in an electronic chip packaging system as an example for illustration.

[0065] The overall analysis process of this embodiment is shown in Figure 1, which mainly includes 6 steps. The key assembly interface is selected as the research object, and the specific steps are as follows:

[0066] 1) Taking the concrete key assembly interface in the electronic chip packaging system as the specific research object, a laser confocal roughness testing system is used to obtain the microscopic morphology features of the contact surface. The rough surface is reconstructed by mapping it to the contact surface mesh element nodes with the same sampling rate, and then a numerical analysis model of the contact thermal performance considering the real microscopic rough surface is constructed. Furthermore, this numerical analysis model is the optimal numerical model considering the independence of surface microscopic morphology sampling rate and mesh size. As shown in Figure 2, a uniform pressure load of 3 MPa is applied to the upper surface. The heat transfer analysis load condition consists of different temperature heat sources on the left and right sides of the upper surface, respectively... and First, contact mechanical analysis is applied to obtain the initial contact state. Then, the initial contact state is used as the initial mesh state for contact heat transfer analysis. Subsequently, the obtained temperature history is used to conduct secondary contact state analysis considering effects such as thermal stress and thermal softening in a predefined temperature field. Finally, the secondary contact state is used as the initial mesh state for contact heat transfer analysis to obtain the final temperature and heat flux distribution.

[0067] 2) This optimization design method seeks the thermal conductivity distribution at the assembly interface that minimizes contact thermal resistance under the integral constraint of thermal conductivity. Its physical equation can be expressed as:

[0068] (1)

[0069] in, This indicates the thermal conductivity distribution in the design area. Indicates the design of sub-region grid cells. This represents the thermal conductivity of the grid cells in the design sub-region. This represents the initial thermal conductivity value. This represents the size of the design region. Furthermore, to find the optimal thermal conductivity distribution, the original constraint on the total integral of the thermal conductivity of the design region can be changed to no constraint, in order to find the thermal conductivity distribution that satisfies the best contact thermal performance.

[0070] Therefore, based on the processing accuracy and dimensional limitations of the surface coating manufacturing process, the assembly region with a thickness of 0.3 mm perpendicular to the contact surface is defined as the design region. Each grid cell within the design region is a sub-region, and the thermal conductivity of each sub-region is a sub-variable. The distribution of thermal conductivity across the entire design region is the overall design variable. The detailed dimensions of the numerical analysis model and the division of the design region are shown in Figure 2.

[0071] 3) A cyclic algorithm is used to define the unit thermal conductivity of each design sub-region. The initial thermal conductivity distribution is set to a uniform value as in traditional design thinking, and this value is used as a reference result before design optimization. The initial thermal conductivity value for this design object is HTA material, and the thermal conductivity of HTA material is defined as 11 W / mK. Subsequently, in order to find the optimal thermal conductivity distribution, the material thermal conductivity distribution of the design region in the numerical analysis model is redefined based on the updated thermal conductivity distribution in each iteration step.

[0072] 4) Based on the defined thermal conductivity distribution of the design region, the temperature gradient distribution of the design region is calculated using a numerical analysis model of contact thermal performance for iterative updating of design variables. The design objective, i.e., the contact thermal resistance, under this thermal conductivity distribution is then solved for iterative convergence judgment. The design objective, i.e., the contact thermal resistance TCR, is defined as follows:

[0073] (2)

[0074] in, This indicates the average temperature of the contact surfaces on the assembly. This indicates the average temperature of the contact surfaces under the assembly. This represents the average heat flux across the contact surfaces of the assembly. This represents the average heat flux across the contact surface of the assembly. Therefore, based on the contact surface temperature and heat flux distribution results obtained from the numerical analysis of contact thermal performance, the contact thermal resistance is calculated under different thermal conductivity distribution conditions within the design region.

[0075] 5) The iterative design criterion for thermal conductivity distribution is an extension of the principle of minimizing energy dissipation in the heat transfer process, namely, the principle of uniform temperature gradient distribution. The detailed formula is as follows:

[0076] (3)

[0077] (4)

[0078] in, This represents the grid cell representing the design region at the (n+1)th iteration step. thermal conductivity, This represents the design region grid cell for the nth iteration step. thermal conductivity, Design regional grid units temperature gradient, This represents a control factor used to determine whether the thermal conductivity distribution meets the total integral constraint. If it equals 1, the total integral of the thermal conductivity distribution in the design region is restricted; if it is greater than 1, the optimal thermal conductivity distribution is sought under unrestricted thermal conductivity conditions. This indicates the minimum thermal conductivity limit for the design area. This indicates the maximum limit of thermal conductivity in the design area.

[0079] The specific steps for updating the thermal conductivity are to apply iterative design criteria and update the thermal conductivity distribution for the next iteration based on the temperature gradient distribution and thermal conductivity distribution obtained in the previous iteration. The ultimate goal is to achieve temperature gradient homogenization by optimizing the thermal conductivity distribution, thereby minimizing the contact thermal resistance.

[0080] Based on the design target results, determine whether the current iteration meets the iteration convergence criterion. If it meets the iteration convergence criterion, continue to the next step (6). If it does not meet the iteration convergence criterion, calculate the thermal conductivity distribution of the next iteration step based on the iteration design criterion, update the thermal conductivity distribution of the design region in the numerical analysis model, and continue to step (4).

[0081] Iterative convergence criteria include three aspects: convergence of the maximum number of iterations, convergence of the optimization rate of the design objective, and convergence of the rate of change of the design objective between two adjacent iterations. Specifically, they can be expressed as follows:

[0082] (5)

[0083] in, Indicates the maximum number of iterations. , , This represents the TCR settlement results for the (n-1), n, and n+1th iterations. This represents the initial TCR result before optimization. This represents the convergence threshold for the reduction rate of the optimization objective. This represents the convergence threshold for the rate of change of adjacent optimization objectives. The specific iterative convergence criterion for this design is the convergence of the rate of change of two adjacent design objectives, i.e., setting a convergence threshold for the rate of change of adjacent optimization objectives. .

[0084] 6) Output the optimized thermal conductivity distribution and contact thermal resistance calculation results that satisfy the iterative convergence criterion. In order to improve the calculation efficiency, the design variable distribution in the vertical direction of the design area is made uniform. Therefore, it is only necessary to output the thermal conductivity distribution of the mesh element on the contact surface.

[0085] The beneficial effects of this invention are further illustrated using a key assembly interface in an electronic chip packaging system as an example. The temperature distribution cloud maps of the master and slave assembly interfaces before the "heterogeneous" thermal conductivity distribution design are shown in Figure 3(a) and (b), and the temperature distribution cloud maps of the master and slave assembly interfaces after the "heterogeneous" thermal conductivity distribution design are shown in Figure 4(a) and (b). In Figure 3, the introduction of the non-uniform temperature distribution on the upper surface and the existence of microscopic surface roughness lead to an extremely uneven temperature gradient distribution on the contact surface during the heat transfer process, resulting in a small actual contact thermal conductivity, uneven temperature distribution on the upper and lower contact surfaces, and extremely high differences in the mean temperature distribution. Starting from energy dissipation and temperature gradient distribution uniformity, the temperature gradient distribution is made uniform and the actual thermal conductivity is significantly improved by actively designing the thermal conductivity distribution of the assembly interface. By comparing Figure 3 and Figure 4, it can be clearly seen that the temperature distribution uniformity is significantly improved, the temperature distribution difference between the upper and lower contact surfaces is significantly reduced, and the average heat flux is increased. Calculations show that the contact thermal resistance (TCR) is reduced by more than 40%, effectively improving the contact thermal performance.

[0086] Example 2

[0087] Referring to Figure 5, an interface thermal conductivity distribution optimization design system for improving the contact thermal performance of assembly interfaces includes:

[0088] The micro-morphology feature acquisition module is used to acquire the micro-morphology features of the contact heat dissipation surface of the component.

[0089] The contact thermal performance numerical analysis model building module is used to build a contact thermal performance numerical analysis model based on the micro-morphological characteristics of the contact heat dissipation surface of the component.

[0090] The module for calculating thermal conductivity distribution and contact thermal resistance is used to perform multiple iterative calculations on the thermal conductivity distribution of the design area using a numerical analysis model of contact thermal performance, so as to obtain the thermal conductivity distribution and contact thermal resistance.

[0091] The judgment module is used to determine whether each iteration meets the iteration convergence criterion based on the thermal conductivity distribution and contact thermal resistance. If the iteration convergence criterion is met, the thermal conductivity distribution and contact thermal resistance are output. If the iteration convergence criterion is not met, the thermal conductivity distribution of the next iteration step is calculated, and the thermal conductivity distribution of the design area in the contact thermal performance numerical analysis model is updated. The module then returns the steps of iterating the thermal conductivity distribution of the design area multiple times using the contact thermal performance numerical analysis model.

[0092] This invention abandons the traditional design mindset of "homogenizing" the thermal conductivity of assembly interfaces and innovatively proposes a "heterogeneous" design concept for precisely controlling the thermal conductivity and other heat transfer characteristics of assembly interfaces. This method uses a numerical analysis model of the contact thermal performance of the assembly interface, considering the micro-roughness of the surface, as the design object. Minimizing the contact thermal resistance of the assembly interface is the design objective. The contact interface region and the mesh elements within the region are the design region and sub-region, respectively, with the thermal conductivity of the mesh elements within the design region serving as the design variable. A laser confocal roughness testing system is used to obtain the micro-morphological characteristics of the contact surface, establishing a numerical analysis model of contact thermal performance that considers the actual micro-roughness of the surface and secondary thermal effects, thus achieving accurate prediction of contact thermal characteristics. This invention uses a numerical analysis model of the contact thermal performance of the assembly interface, considering the micro-roughness of the surface, as the design object. Minimizing the contact thermal resistance of the assembly interface is the design objective. The contact interface region and the mesh elements within the region are the design region and sub-region, respectively, with the thermal conductivity of the mesh elements within the design region serving as the design variable. An iterative design criterion for the distribution of the thermal conductivity of the assembly interface, driven by the principle of energy dissipation minimization, is applied. This results in a rapid and precise optimization design method for controlling the distribution of the thermal conductivity of the assembly interface, driven by contact thermal performance. This method overcomes the high computational complexity and weak iterative convergence of traditional swarm optimization algorithms, achieving a rapid and precise layout of the thermal conductivity of the assembly interface to match optimal contact thermal performance. This invention can break through the bottleneck in improving contact thermal performance caused by traditional "homogenization" design thinking, significantly improving the contact thermal performance of heat-sensitive mechanical equipment such as electronic chip packaging systems. It solves the problems of excessively high operating temperatures and premature equipment failure caused by local thermal stress due to insufficient contact thermal performance in heat-sensitive mechanical equipment, thereby effectively ensuring its service life and stability.

[0093] The above description only illustrates the preferred embodiments of the present invention and should not be construed as limiting the scope of the claims. The present invention is not limited to the above embodiments, and variations in its specific structure are permitted. All modifications made within the scope of the independent claims of this invention are also within the scope of protection of this invention.

[0094] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. The terminology used herein in the description of the invention is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items.

Claims

1. A method for optimizing the distribution of interfacial thermal conductivity to improve the contact thermal performance of assembly interfaces, characterized in that, Includes the following steps: To obtain the microscopic morphological characteristics of the contact heat dissipation surface of components in an electronic chip packaging system; Based on the microstructure characteristics of the contact heat dissipation surface of the component, a numerical analysis model of contact thermal performance is constructed. The thermal conductivity distribution of the design area was iterated multiple times using a numerical analysis model of contact thermal performance to obtain the thermal conductivity distribution and contact thermal resistance. Determine whether each iteration meets the iteration convergence criterion based on the thermal conductivity distribution and contact thermal resistance. If the iterative convergence criterion is met, output the thermal conductivity distribution and contact thermal resistance; If the iteration convergence criterion is not met, calculate the thermal conductivity distribution for the next iteration step, update the thermal conductivity distribution of the design region in the contact thermal performance numerical analysis model, and return to the step of iterating the thermal conductivity distribution of the design region multiple times using the contact thermal performance numerical analysis model.

2. The method for optimizing the distribution of interfacial thermal conductivity to improve the contact thermal performance of assembly interfaces according to claim 1, characterized in that, A laser confocal roughness testing system was used to obtain the microscopic morphological characteristics of the contact heat dissipation surface of key components.

3. The method for optimizing the distribution of interfacial thermal conductivity to improve the contact thermal performance of assembly interfaces according to claim 1, characterized in that, By reconstructing the rough surface by mapping it to the contact surface grid cell nodes with the same sampling rate, a numerical analysis model of the contact thermal performance considering the real micro-rough surface is constructed.

4. The method for optimizing the distribution of interfacial thermal conductivity to improve the contact thermal performance of assembly interfaces according to claim 1, characterized in that, The iterative design criterion for thermal conductivity distribution is a principle for uniform temperature gradient distribution, extended from the principle of minimizing energy dissipation in the heat transfer process, and is expressed as follows: (1) (2) in, This represents the grid cell representing the design region at the (n+1)th iteration step. thermal conductivity, This represents the design region grid cell for the nth iteration step. thermal conductivity, Design regional grid units temperature gradient, Indicates regulatory factor, This indicates the minimum thermal conductivity limit for the design area. This indicates the maximum limit of thermal conductivity in the design area.

5. The method for optimizing the distribution of interfacial thermal conductivity to improve the contact thermal performance of assembly interfaces according to claim 1, characterized in that, The goal of the iterative design criterion for thermal conductivity distribution is to achieve uniform temperature gradient and minimize contact thermal resistance by optimizing the thermal conductivity distribution.

6. The method for optimizing the distribution of interfacial thermal conductivity to improve the contact thermal performance of assembly interfaces according to claim 1, characterized in that, The design area is the assembly area with a set thickness in the direction perpendicular to the contact surface, and the design sub-area is each grid cell within the design area.

7. The method for optimizing the distribution of interfacial thermal conductivity to improve the contact thermal performance of assembly interfaces according to claim 1, characterized in that, Contact thermal resistance (TCR) is calculated using the following formula: (3) in, This indicates the average temperature of the contact surfaces on the assembly. This indicates the average temperature of the contact surfaces under the assembly. This represents the average heat flux across the contact surfaces of the assembly. This represents the average heat flux across the lower contact surface of the assembly.

8. The method for optimizing the distribution of interfacial thermal conductivity to improve the contact thermal performance of assembly interfaces according to claim 1, characterized in that, Iterative convergence criteria include convergence of the maximum number of iterations, convergence of the optimization rate of the design objective, and convergence of the rate of change of the design objective between two adjacent iterations.

9. The method for optimizing the distribution of interfacial thermal conductivity to improve the contact thermal performance of assembly interfaces according to claim 1, characterized in that, The iterative convergence criterion is: (4) in, Indicates the maximum number of iterations. 、 、 This represents the TCR settlement results for the (n-1), n, and n+1th iterations. This represents the initial TCR result before optimization. This represents the convergence threshold for the reduction rate of the optimization objective. This represents the convergence threshold of the rate of change of adjacent optimization objectives.

10. A system for optimizing the distribution of interfacial thermal conductivity to improve the contact thermal performance of assembly interfaces, characterized in that, include: The micro-morphology feature acquisition module is used to acquire the micro-morphology features of the contact heat dissipation surface of the component. The contact thermal performance numerical analysis model building module is used to build a contact thermal performance numerical analysis model based on the micro-morphological characteristics of the contact heat dissipation surface of the component. The module for calculating thermal conductivity distribution and contact thermal resistance is used to perform multiple iterative calculations on the thermal conductivity distribution of the design area using a numerical analysis model of contact thermal performance, so as to obtain the thermal conductivity distribution and contact thermal resistance. The judgment module is used to determine whether each iteration meets the iteration convergence criterion based on the thermal conductivity distribution and contact thermal resistance. If the iterative convergence criterion is met, output the thermal conductivity distribution and contact thermal resistance; If the iteration convergence criterion is not met, calculate the thermal conductivity distribution for the next iteration step, update the thermal conductivity distribution of the design region in the contact thermal performance numerical analysis model, and return to the step of iterating the thermal conductivity distribution of the design region multiple times using the contact thermal performance numerical analysis model.

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