Multilayer structure integration method and system for micro-fluidic chip packaging

Abstract

The invention relates to the field of chip integration simulation, in particular to a multi-layer structure integration method and system for micro-fluidic chip packaging, and the method comprises the following steps: obtaining a chip production log, carrying out multifunctional layer structure definition and global multi-layer structure simulation, and constructing an initial multi-layer packaging three-dimensional model; performing layer-by-layer packaging material identification on the chip production log, and performing multi-layer packaging simulation on the initial multi-layer packaging three-dimensional model to obtain multi-layer packaging simulation data; performing dynamic flow field simulation behavior analysis on the multi-level packaging simulation data, and performing interlayer stress mutual influence analysis to obtain an interlayer stress mutual influence relationship; local thermal expansion effect evolution and multi-layer thermal strain evolution are carried out based on the multi-layer packaging simulation data, so that thermal strain evolution characteristics of different structural layers are generated; according to the invention, efficient and stable chip multilayer structure integration is realized.

Description

Technical Field

[0001] The present invention relates to the field of chip integration simulation, and in particular to a multi-layer structure integration method and system for microfluidic chip packaging. Background Art

[0002] With the continuous development of microfluidic technology, microfluidic chips are increasingly used in biomedicine, chemical analysis, environmental monitoring and other fields. As a key technology of Lab-on-a-Chip, the packaging method of microfluidic chips directly affects the functionality, stability and manufacturing cost of the chip. In traditional microfluidic chip packaging, the single-layer structure packaging method can meet certain needs, but with the continuous improvement of the requirements for multifunctional integration, single-layer packaging is gradually unable to meet the needs of complex application scenarios.

[0003] As an emerging packaging technology, multilayer structure integration methods are gradually being applied to the development of microfluidic chips. By integrating multiple microfluidic channels, sensors, and other components in multiple layers, higher-density chip design can be achieved while improving the versatility and operational flexibility of the chip. However, there are still many challenges in the process of realizing multilayer structure integration of microfluidic chips. For example, how to ensure effective connection between different layers, how to solve the compatibility problem of interlayer packaging materials, and how to maintain airtightness and liquid tightness between layers are all technical problems that need to be solved urgently.

[0004] Traditional microfluidic chip packaging methods usually rely on manual assembly and simple hot pressing technology, which is easily limited by production processes, material properties and structural design, resulting in low efficiency and insufficient stability of multi-layer structure integration. Therefore, in order to meet the requirements of high integration, high functionality and high stability of modern microfluidic chips, an innovative multi-layer structure integration method for microfluidic chip packaging is needed, which can break through the limitations of traditional packaging technology and achieve efficient and precise multi-layer structure integration. Summary of the invention

[0005] In order to solve the above technical problems, the present invention proposes a multi-layer structure integration method and system for microfluidic chip packaging to solve at least one of the above technical problems.

[0006] To achieve the above object, the present invention provides a multi-layer structure integration method for microfluidic chip packaging, comprising the following steps: Step S1: Obtain chip production logs, perform multi-functional layer structure definition and global multi-level structure simulation, and construct an initial multi-layer package three-dimensional model; Step S2: Identify the packaging materials layer by layer in the chip production log, and perform multi-level packaging simulation on the initial multi-layer packaging three-dimensional model to obtain multi-level packaging simulation data; Step S3: performing dynamic flow field simulation behavior analysis on the multi-level packaging simulation data, and performing inter-layer stress mutual influence analysis, so as to obtain the inter-layer stress mutual influence relationship; Step S4: performing local thermal expansion effect evolution and multi-layer thermal strain evolution based on multi-layer packaging simulation data, thereby generating thermal strain evolution characteristics of different structural layers; Step S5: performing multi-level stress fatigue analysis based on the inter-layer stress mutual influence relationship and the thermal strain evolution characteristics of different structural layers, and reconstructing the multi-level fatigue evolution to construct a multi-level fatigue evolution trajectory; Step S6: Optimize global packaging parameters according to the multi-level fatigue evolution trajectory, and perform three-dimensional model iterative calculation on the initial multi-layer packaging three-dimensional model, so as to construct a final optimized three-dimensional chip packaging model.

[0007] By obtaining the chip production log, the present invention can accurately understand each process parameter and material selection in the production process. These data provide a practical basis for the design of chip packaging, ensuring that the actual characteristics of production and materials are taken into account in the design stage. By defining the packaging functional layer structure layer by layer, such as fluid channels, sensor protection layers, contact layers, etc., it is ensured that the function of each layer matches the process requirements, avoiding errors caused by inconsistent functional requirements in subsequent packaging. The preliminary three-dimensional packaging model generated by global multi-level structure simulation lays the foundation for further simulation and optimization of subsequent steps, forming an overall framework containing all functional layers, which can quickly verify whether the design meets expectations. By analyzing the production log, the packaging material of each layer is accurately identified to ensure that the physical properties of the materials used (such as thermal conductivity, elastic modulus, density, etc.) meet the requirements of packaging design. This helps in subsequent heat conduction, mechanical analysis and fluid behavior simulation to ensure the accuracy of the simulation results. By performing multi-level packaging simulation on the initial three-dimensional model, important data such as interaction between layers, stress distribution, fluid behavior, etc. can be obtained. This process can help the design team identify possible design defects and make early corrections. The simulation data can provide a clear direction for subsequent optimization, especially in terms of material selection and structural layout, to ensure that the design meets actual production requirements and has higher reliability. Through dynamic flow field simulation, the flow characteristics of the microfluidic channel in the package can be analyzed to reveal the dynamic behavior of the fluid in the chip, such as flow rate, pressure fluctuation and flow stability. This helps to optimize the fluid control system of the chip and ensure the stability of the fluid when the chip is working. By analyzing the stress interaction between different packaging layers, stress concentration areas between different materials and structural layers can be identified. Effectively identify potential structural weaknesses to avoid package damage or failure caused by excessive stress concentration. The interlayer stress interaction relationship obtained provides a basis for subsequent design improvements. Stress concentration can be reduced by adjusting materials, thickness and structural layout to improve the overall performance and durability of chip packaging. Through the analysis of thermal expansion effects and thermal strains, stress concentration areas caused by temperature differences can be effectively identified, especially thermal stress problems caused by thermal expansion mismatches of different materials. This helps to design a more high-temperature resistant and stable packaging structure. The evolution of thermal expansion effects and the extraction of multi-layer thermal strain characteristics provide a basis for the stability evaluation of packaging under different working environments, and can identify potential failure risks caused by temperature fluctuations in advance. The thermal strain evolution characteristics provide data support for the thermal management design of the packaging structure, helping to optimize the heat dissipation design in the chip package and ensure the stability of the chip under high load and long-term use. Through multi-level stress fatigue analysis, the fatigue life of the chip package during long-term use can be predicted. This helps to evaluate the long-term reliability of the package, especially to predict the durability of key parts such as microfluidic channels, and reduce the risk of fatigue failure.The reconstruction of fatigue evolution trajectory can help the design team find the key areas of fatigue problems, and then improve the fatigue resistance of the packaging structure through material selection, structural optimization and other means to extend the service life of the chip. Through fatigue analysis and the establishment of evolution trajectory, structural design or process problems that may lead to failure can be identified and avoided, significantly reducing the failure rate of the product during use. By adjusting the packaging parameters according to the fatigue evolution trajectory, the global optimization of chip packaging can be achieved, so that the chip can maintain excellent performance and stability under different working conditions. This can not only improve the reliability of the chip, but also reduce production rework and cost waste caused by design defects. Based on the adjustment of packaging optimization parameters, iterative optimization of the three-dimensional model is carried out to ensure that the final packaging design can be smoothly manufactured in actual production. Each iteration can fine-tune the packaging design based on the new simulation data to ensure the perfect combination of stability of the packaging structure, thermal management and fluid control. Through the intelligent optimization engine and simulation platform, the packaging design can be optimized quickly and efficiently in multiple dimensions, and the development cycle can be reduced while ensuring accuracy, thereby improving the overall R&D efficiency.

[0008] In this specification, a multi-layer structure integration system of a microfluidic chip package is provided, which is used to perform the multi-layer structure integration method of the microfluidic chip package as described above, comprising: The multi-layer packaging simulation module is used to obtain chip production logs, perform multi-functional layer structure definition and global multi-level structure simulation, and build an initial multi-layer packaging 3D model; The packaging simulation module is used to identify the packaging materials layer by layer in the chip production log and perform multi-level packaging simulation on the initial multi-layer packaging three-dimensional model to obtain multi-level packaging simulation data; The flow field simulation analysis module is used to perform dynamic flow field simulation behavior analysis on multi-layer packaging simulation data and to analyze the mutual influence of interlayer stresses, thereby obtaining the mutual influence relationship of interlayer stresses; Thermal strain evolution module, which is used to perform local thermal expansion effect evolution and multi-layer thermal strain evolution based on multi-layer packaging simulation data, thereby generating thermal strain evolution characteristics of different structural layers; Fatigue evolution reconstruction module, which is used to perform multi-level stress fatigue analysis based on the mutual influence relationship between inter-layer stresses and the thermal strain evolution characteristics of different structural layers, and to reconstruct multi-level fatigue evolution and construct multi-level fatigue evolution trajectories; The packaging parameter optimization module is used to optimize the global packaging parameters according to the multi-level fatigue evolution trajectory, and perform three-dimensional model iterative calculation on the initial multi-layer packaging three-dimensional model to construct the final optimized three-dimensional chip packaging model.

[0009] The present invention ensures that the package design is consistent with the actual production process by extracting actual production data (such as material selection, process flow, functional requirements, etc.) from the production log, reducing later modifications. The function of each layer of packaging is accurately defined to ensure that the packaging meets multiple functional requirements such as specific fluids, sensor protection, heat dissipation, etc., and provide a clear framework for subsequent simulation and optimization. The constructed initial three-dimensional model provides a basis for subsequent packaging analysis, quickly verifies the feasibility of multi-layer packaging design, and provides space for interactions and design optimization between different layers. According to the material information in the production log, the material properties used for each layer of packaging are accurately identified to ensure that the physical properties of each material match the design requirements and improve the accuracy of the simulation data. Multi-level packaging simulation provides detailed data support for subsequent analysis of fluid mechanics, heat conduction, mechanical stress, etc., which helps to identify potential structural problems or design defects. Through the feedback of simulation data, the interaction between different packaging materials and structural layers can be quickly verified to avoid problems in the later manufacturing process. Dynamic simulation of fluid flow in microfluidic channels reveals the changing trends of flow rate, flow direction, pressure, etc., ensures stable fluid flow, prevents unstable flow or bubble problems, and optimizes the fluid control performance of the chip. By analyzing the stress interaction between the layers, stress concentration points that may cause package failure can be identified, avoiding structural damage or functional failure caused by interlayer stress mismatch. Flow field analysis helps optimize the chip's fluid channel design, ensuring stable and efficient fluid flow, and helping to improve the chip's performance and reliability. By analyzing the thermal expansion effect and thermal strain characteristics of each layer of material, internal stress changes caused by temperature fluctuations can be revealed, especially in key areas such as microfluidic channels and sensor layers. Identifying thermal expansion mismatches between different materials and packaging layers helps design more high-temperature resistant packaging structures and reduce the risk of package failure caused by thermal stress. Thermal strain evolution data provides key information for thermal management solutions, helping to improve the chip's heat dissipation design and ensure stable operation in high-temperature environments. Through fatigue analysis, the performance degradation of the packaging structure in long-term use can be predicted, helping to evaluate the long-term stability of the chip, especially the reliability under repeated use or extreme conditions. By reconstructing the fatigue evolution trajectory, areas or layers prone to fatigue failure can be identified, providing designers with optimization solutions to avoid failures caused by fatigue cracking or material aging. Fatigue evolution analysis provides a basis for improving package design, which helps to improve the durability and stability of the chip during long-term operation. By feedback on the fatigue evolution trajectory, the package parameters (such as material selection, package structure, size adjustment, etc.) are optimized to ensure that the package achieves the best balance in terms of function, reliability and durability. Through iterative calculation of the three-dimensional model, the geometry and material distribution of the package are continuously optimized to ensure that the package design meets production conditions and provides the best performance.The optimization module can significantly shorten the design cycle, quickly correct defects in the packaging design through intelligent optimization algorithms, improve overall design efficiency, and reduce the error rate that may occur during the production process. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] Figure 1 A schematic flow chart of the steps of a multi-layer structure integration method for microfluidic chip packaging according to the present invention; Figure 2 Detailed implementation flow chart of step S1; Figure 3 Detailed implementation flow chart of step S2; Figure 4 Detailed implementation flow chart of step S3. DETAILED DESCRIPTION

[0011] It should be understood that the specific embodiments described herein are only used to explain the present invention, and are not used to limit the present invention.

[0012] The present application example provides a multi-layer structure integration method and system for microfluidic chip packaging. The execution subject of the multi-layer structure integration method and system for microfluidic chip packaging includes but is not limited to: mechanical equipment, data processing platform, cloud server node, network upload equipment, etc. equipped with the system can be regarded as the general computing node of the present application, and the data processing platform includes but is not limited to: at least one of an audio and image management system, an information management system, and a cloud data management system.

[0013] See also Figures 1 to 4 The present invention provides a multi-layer structure integration method for microfluidic chip packaging, and the multi-layer structure integration method for microfluidic chip packaging comprises the following steps: Step S1: Obtain chip production logs, perform multi-functional layer structure definition and global multi-level structure simulation, and construct an initial multi-layer package three-dimensional model; Step S2: Identify the packaging materials layer by layer in the chip production log, and perform multi-level packaging simulation on the initial multi-layer packaging three-dimensional model to obtain multi-level packaging simulation data; Step S3: performing dynamic flow field simulation behavior analysis on the multi-level packaging simulation data, and performing inter-layer stress mutual influence analysis, so as to obtain the inter-layer stress mutual influence relationship; Step S4: performing local thermal expansion effect evolution and multi-layer thermal strain evolution based on multi-layer packaging simulation data, thereby generating thermal strain evolution characteristics of different structural layers; Step S5: performing multi-level stress fatigue analysis based on the inter-layer stress mutual influence relationship and the thermal strain evolution characteristics of different structural layers, and reconstructing the multi-level fatigue evolution to construct a multi-level fatigue evolution trajectory; Step S6: Optimize global packaging parameters according to the multi-level fatigue evolution trajectory, and perform three-dimensional model iterative calculation on the initial multi-layer packaging three-dimensional model, so as to construct a final optimized three-dimensional chip packaging model.

[0014] By obtaining the chip production log, the present invention can accurately understand each process parameter and material selection in the production process. These data provide a practical basis for the design of chip packaging, ensuring that the actual characteristics of production and materials are taken into account in the design stage. By defining the packaging functional layer structure layer by layer, such as fluid channels, sensor protection layers, contact layers, etc., it is ensured that the function of each layer matches the process requirements, avoiding errors caused by inconsistent functional requirements in subsequent packaging. The preliminary three-dimensional packaging model generated by global multi-level structure simulation lays the foundation for further simulation and optimization of subsequent steps, forming an overall framework containing all functional layers, which can quickly verify whether the design meets expectations. By analyzing the production log, the packaging material of each layer is accurately identified to ensure that the physical properties of the materials used (such as thermal conductivity, elastic modulus, density, etc.) meet the requirements of packaging design. This helps in subsequent heat conduction, mechanical analysis and fluid behavior simulation to ensure the accuracy of the simulation results. By performing multi-level packaging simulation on the initial three-dimensional model, important data such as interaction between layers, stress distribution, fluid behavior, etc. can be obtained. This process can help the design team identify possible design defects and make early corrections. The simulation data can provide a clear direction for subsequent optimization, especially in terms of material selection and structural layout, to ensure that the design meets actual production requirements and has higher reliability. Through dynamic flow field simulation, the flow characteristics of the microfluidic channel in the package can be analyzed to reveal the dynamic behavior of the fluid in the chip, such as flow rate, pressure fluctuation and flow stability. This helps to optimize the fluid control system of the chip and ensure the stability of the fluid when the chip is working. By analyzing the stress interaction between different packaging layers, stress concentration areas between different materials and structural layers can be identified. Effectively identify potential structural weaknesses to avoid package damage or failure caused by excessive stress concentration. The interlayer stress interaction relationship obtained provides a basis for subsequent design improvements. Stress concentration can be reduced by adjusting materials, thickness and structural layout to improve the overall performance and durability of chip packaging. Through the analysis of thermal expansion effects and thermal strains, stress concentration areas caused by temperature differences can be effectively identified, especially thermal stress problems caused by thermal expansion mismatches of different materials. This helps to design a more high-temperature resistant and stable packaging structure. The evolution of thermal expansion effects and the extraction of multi-layer thermal strain characteristics provide a basis for the stability evaluation of packaging under different working environments, and can identify potential failure risks caused by temperature fluctuations in advance. The thermal strain evolution characteristics provide data support for the thermal management design of the packaging structure, helping to optimize the heat dissipation design in the chip package and ensure the stability of the chip under high load and long-term use. Through multi-level stress fatigue analysis, the fatigue life of the chip package during long-term use can be predicted. This helps to evaluate the long-term reliability of the package, especially to predict the durability of key parts such as microfluidic channels, and reduce the risk of fatigue failure.The reconstruction of fatigue evolution trajectory can help the design team find the key areas of fatigue problems, and then improve the fatigue resistance of the packaging structure through material selection, structural optimization and other means to extend the service life of the chip. Through fatigue analysis and the establishment of evolution trajectory, structural design or process problems that may lead to failure can be identified and avoided, significantly reducing the failure rate of the product during use. By adjusting the packaging parameters according to the fatigue evolution trajectory, the global optimization of chip packaging can be achieved, so that the chip can maintain excellent performance and stability under different working conditions. This can not only improve the reliability of the chip, but also reduce production rework and cost waste caused by design defects. Based on the adjustment of packaging optimization parameters, iterative optimization of the three-dimensional model is carried out to ensure that the final packaging design can be smoothly manufactured in actual production. Each iteration can fine-tune the packaging design based on the new simulation data to ensure the perfect combination of stability of the packaging structure, thermal management and fluid control. Through the intelligent optimization engine and simulation platform, the packaging design can be optimized quickly and efficiently in multiple dimensions, and the development cycle can be reduced while ensuring accuracy, thereby improving the overall R&D efficiency.

[0015] In the embodiment of the present invention, refer to Figure 1 , is a schematic flow chart of the steps of a multi-layer structure integration method of a microfluidic chip package of the present invention. In this example, the steps of the multi-layer structure integration method of the microfluidic chip package include: Step S1: Obtain chip production logs, perform multi-functional layer structure definition and global multi-level structure simulation, and construct an initial multi-layer package three-dimensional model; In this embodiment, the specific data sources of the chip production management system are determined, which usually include production process records, quality inspection data, and material usage records. These data sources provide important information about each stage of the chip production process. Through the interface with the production management system, relevant chip production log data is extracted. Ensure that the extracted data includes production date, material batch, process parameters (such as temperature, pressure and time), operator information, etc. These data provide a basis for subsequent analysis. Clean the extracted data, remove duplicate and irrelevant information, and ensure the integrity and accuracy of the data. Arrange the data in chronological order and production batch for subsequent analysis and use. Store the cleaned data in an easily accessible database or data table to ensure that the required information can be quickly obtained in subsequent steps. Data management tools (such as Excel or database management systems) can be used for storage and management. According to the design requirements and functional requirements of the Bluetooth headset, analyze the specific structure of the multifunctional layer. This may include signal layer, isolation layer, thermal management layer, and protective layer, etc. The function and material selection of each layer need to be clear. Determine the material properties of each layer, including thermal conductivity, electrical conductivity, mechanical strength, etc. These material properties will affect the performance and reliability of the entire package. You can refer to the material manual or technical data provided by the manufacturer. Set geometric parameters for each layer, such as thickness, width, and shape. These parameters should be adjusted according to the actual production process and design requirements to ensure the manufacturability and functionality of the structure. Select appropriate simulation tools (such as ANSYS, COMSOL, etc.) to ensure that they have the ability to simulate multi-level structures and thermal and mechanical analysis. The choice of simulation tools should take into account the software's functionality and the team's familiarity. In the simulation software, build an initial multi-layer package 3D model based on the previously defined multi-layer structure. Ensure that the geometry, material properties, and interface relationships of each layer are accurate. Set appropriate boundary conditions and loading conditions for the model, including temperature, pressure, and mechanical loads. These conditions should reflect the conditions in the actual working environment to ensure the reliability of the simulation results. Run the simulation calculation and monitor the convergence and stability during the calculation process. After the calculation is completed, analyze the results, including stress distribution, temperature field, and fluid flow characteristics, to ensure that the performance of the model meets the design requirements. Based on the simulation results, optimize the initial multi-layer package model. Identify possible design defects or room for improvement, and adjust layer thickness or material selection to improve performance. Organize all relevant data and simulation results to generate detailed documentation of the initial multi-layer package 3D model. The document should include the model's geometric parameters, material properties, simulation conditions, and analysis results to ensure the systematicness and integrity of the information. The constructed 3D model should be archived and shared with the team to ensure that subsequent design and analysis can be based on the model. Use a version control system to record each modification of the model for traceability and management.

[0016] Step S2: Identify the packaging materials layer by layer in the chip production log, and perform multi-level packaging simulation on the initial multi-layer packaging three-dimensional model to obtain multi-level packaging simulation data; In this embodiment, chip production logs are extracted from the production management system. These logs usually include detailed production records, material batches, process parameters and other information. Ensure that the acquired data covers all necessary fields, such as material type, layer, production date, process used (such as welding, patch, etc.), and specific requirements for each layer. Perform a preliminary review of the extracted data to ensure the integrity and accuracy of the data. Use data processing tools (such as Excel or data management software) to organize the data, remove duplicates and error information, and prepare for subsequent analysis. Based on the information in the production log, identify the type of material used for packaging layer by layer. Typically, the production log records the material selection for each layer in detail, including the name, specifications and performance parameters (such as thermal conductivity, strength, etc.) of the material. Use text analysis tools or keyword matching algorithms to automatically extract material information and improve efficiency. Evaluate the performance of each layer of material, focusing on key parameters such as thermal conductivity, elastic modulus, density, etc. of the material. These performance parameters are critical for subsequent multi-level simulations. The identified material properties are organized into a structured data table, including information such as layer, material name, thermal conductivity, mechanical strength, etc. Ensure that all data is accurate and consistent with the information in the production log. Based on the characteristics of the materials, evaluate their performance under different working conditions to select a suitable material combination. This process may require reference to material manuals or industry standards to ensure the suitability and reliability of the selected materials. In simulation software (such as ANSYS, COMSOL, etc.), build a 3D model of the initial multi-layer package. Based on the model foundation established in step S1, introduce material properties layer by layer to ensure that the geometry, thickness and material properties of each layer accurately match the previous identification results. Check and confirm the connection relationship between each layer, such as solder joints, adhesive layers, etc., to ensure that the simulation model can truly reflect the physical structure of the package. Set appropriate boundary conditions and loading conditions for multi-level package simulation. This includes applied temperature, pressure, mechanical load, etc. These conditions should be based on actual application scenarios and take into account the various environmental factors that the product may face during operation. Set the time step and convergence criteria for the simulation calculation to ensure the stability and accuracy of the calculation. Determine key parameters such as fluid flow, heat conduction and mechanical behavior for comprehensive performance evaluation. Start the simulation calculation and observe the convergence and stability of the calculation process to ensure that the model can effectively calculate the stress, temperature field and fluid flow characteristics of each layer during operation. Monitor the simulation results of key areas, especially locations with stress concentration and large temperature gradients, to evaluate the effectiveness of the design and potential failure risks. After the simulation is completed, extract the simulation data of each layer, including stress distribution, temperature field, heat flux density and fluid flow characteristics. These data are the basis for subsequent analysis of packaging performance and potential failure modes. Record the simulation results of each layer in detail to ensure that the performance of each layer can be clearly identified. Compare the performance differences between different layers and identify the key influencing factors.Organize the extracted simulation data into a structured report, including charts and data tables, to facilitate subsequent analysis and discussion. The report should describe in detail the performance of each layer and the potential for improvement. By comparing the simulation data with the design standards, analyze whether the performance of the model meets expectations and identify design elements that may need further optimization. Organize all simulation results to form a multi-level packaging simulation data report. The report should include a description of the simulation model, boundary conditions, material properties, and analysis of simulation results to ensure the comprehensiveness and traceability of the information. Record any problems and solutions during the simulation process to provide reference for future research and development.

[0017] Step S3: performing dynamic flow field simulation behavior analysis on the multi-level packaging simulation data, and performing inter-layer stress mutual influence analysis, so as to obtain the inter-layer stress mutual influence relationship; In this embodiment, information related to fluid flow is collected from the multi-level packaging simulation data extracted in step S2. These data include physical properties of the fluid (such as density, viscosity), flow boundary conditions and flow paths. This information will be used to establish a dynamic flow field simulation model. Set the flow field model in the simulation software to ensure that the model can accurately reflect the geometric structure and physical properties of the actual fluid flow. Use CAD tools to create the geometry of the fluid channel to ensure that it matches the contact surface of the multi-layer packaging model. Set appropriate boundary conditions and initial conditions for dynamic flow field simulation. This includes fluid flow rate, inlet pressure, fluid temperature, etc. Ensure that these parameters can truly reflect the flow state of the product under working conditions. Set the time step and convergence criteria of the simulation calculation to accurately simulate the dynamic characteristics of the flow field. According to the relevant theories of fluid dynamics, select a suitable turbulence model (such as k-ε model or k-ω model) to improve the accuracy of the simulation results. Start the flow field simulation calculation and observe the development of the flow field during the calculation process. Monitor key parameters such as fluid velocity field, pressure field and temperature distribution to ensure that the model can effectively reflect the dynamic behavior of the fluid during operation. Focus on the flow pattern of the fluid in the channel and identify possible dead zones, eddies, and flow separation. These phenomena may affect the subsequent interlayer stress analysis and structural performance. Extract the stress distribution data of each layer from the multi-level package simulation results in step S2. Ensure that the data includes the stress state under different load and temperature conditions for comprehensive interlayer stress analysis. Integrate the stress data with the flow field simulation results to identify the effect of fluid flow on the stress distribution of each layer. This step can reveal the interaction between fluid dynamics and structural mechanics. Establish an interlayer stress interaction analysis model in the simulation software. The model should be able to reflect the stress transfer and interaction between the layers. Set appropriate boundary conditions and interaction models to ensure the accuracy of the analysis. Collect parameters related to material properties, such as elastic modulus, Poisson's ratio, and fatigue limit, so that the true behavior of the material can be considered in the analysis. Start the interlayer stress analysis calculation and observe the stress changes of each layer under the action of the fluid. Monitor the stress concentration area, strain distribution, and interlayer interaction to ensure that the analysis can effectively reveal the mutual influence relationship between the layers. Focus on stress transfer paths and interaction strengths, and identify interlayer stress relationships that have the greatest impact on the overall performance of the package. Extract key results from the interlayer stress interaction analysis, including stress distribution of each layer, stress concentration location, and interlayer interaction relationships. Record the specific values ​​and locations of the results for subsequent analysis and verification. Organize the analysis results to form a report on the interlayer stress interaction relationship. The report should describe in detail the interaction characteristics, stress transfer paths, and possible failure modes between the layers. Conduct an in-depth analysis of the extracted interlayer stress interaction relationship, and verify the rationality of the analysis results in combination with actual application scenarios. The accuracy of the results can be confirmed by comparing with existing literature or experimental data.Identify key inter-layer interactions to provide a basis for subsequent design optimization and failure prediction.

[0018] Step S4: performing local thermal expansion effect evolution and multi-layer thermal strain evolution based on multi-layer packaging simulation data, thereby generating thermal strain evolution characteristics of different structural layers; In this embodiment, the temperature distribution and stress state of each layer are obtained from the multi-layer packaging simulation data extracted in step S3. Special attention should be paid to the temperature changes of each layer under different working conditions to ensure the integrity and accuracy of the data. The extracted data is sorted out to form a data table, and the temperature, stress and material properties of each layer are recorded. This information will provide a basis for the subsequent analysis of thermal expansion effects. The thermal expansion coefficient data of the materials used are collected to ensure that the data source is reliable. Usually, the material manual or relevant literature can be referred to. The difference in thermal expansion coefficient of different materials will have a significant impact on the evolution of thermal strain, so accurate records are required. The material properties of each layer are associated with its thermal expansion coefficient for subsequent thermal strain calculations. A thermal expansion analysis model is constructed in the simulation software to ensure that the actual situation of the multi-layer packaging structure can be reflected. The model should include the geometry, material properties and thermal expansion coefficient of each layer. Appropriate boundary conditions are set to take into account the influencing factors such as external ambient temperature, internal heat source and working conditions to simulate the actual thermal expansion situation. Start the simulation calculation of the thermal expansion effect and observe the expansion of each layer under temperature changes. Monitor the temperature change, thermal expansion displacement and strain distribution of each layer to ensure that the model can effectively reflect the dynamic behavior of thermal expansion. Focus on the thermal expansion effect of local areas, especially contact surfaces and stress concentration areas, which may produce significant deformation and stress due to temperature changes. According to the results of thermal expansion analysis, calculate the thermal strain of each layer. The calculation of thermal strain can be based on temperature change and thermal expansion coefficient, and the thermal strain value of each layer can be calculated by formula. Organize the thermal strain data of each layer to form a thermal strain distribution diagram to facilitate the observation of the thermal strain evolution between different layers. This will help identify potential structural problems. Based on the thermal strain data, generate the thermal strain evolution characteristics of different structural layers. Graphical tools can be used to visualize the thermal strain data to facilitate intuitive understanding of the distribution of thermal strain. Analyze the thermal strain evolution characteristics to identify strain concentration and potential failure modes in key areas. This information will provide important basis for subsequent design optimization and reliability evaluation. Organize the results of thermal strain evolution analysis to form a thermal strain evolution characteristics report. The report should include the thermal strain value of each layer, the thermal strain distribution diagram and its impact analysis on the overall packaging performance. In the report, describe in detail the key areas of thermal strain evolution and the structural problems that may be caused, and provide targeted suggestions and improvement plans.

[0019] Step S5: performing multi-level stress fatigue analysis based on the inter-layer stress mutual influence relationship and the thermal strain evolution characteristics of different structural layers, and reconstructing the multi-level fatigue evolution to construct a multi-level fatigue evolution trajectory; In this embodiment, key stress and strain information is obtained from the interlayer stress interaction relationship data extracted in step S3. Special attention is paid to the influence of stress concentration areas and interlayer interactions on material fatigue performance. Ensure that the data includes stress state and fluid flow influence under different working conditions. At the same time, combined with the thermal strain evolution characteristics obtained in step S4, these data are integrated to form a complete stress-thermal strain model. The stress state and thermal strain characteristics of each layer are associated to provide comprehensive basic data for subsequent fatigue analysis. Construct a multi-level fatigue analysis model in the simulation software. The model should include material properties, stress distribution, thermal strain effects, etc. of each layer. Set appropriate fatigue parameters for each layer, such as fatigue limit, material strength and cycle life, to ensure the accuracy of the analysis. Set corresponding boundary conditions and loading conditions, simulate mechanical loads and temperature changes in actual work, and ensure that the model can truly reflect the working environment of the product. Start multi-level stress fatigue analysis calculations and monitor the convergence and stability during the calculation process. Ensure that the simulation of stress, strain and fatigue life can effectively reflect the fatigue behavior of each layer. During the analysis, focus on key indicators such as maximum stress, stress amplitude and stress ratio, which have a direct impact on the fatigue performance of the material. It is also necessary to record the fatigue damage of each layer under different loading conditions. Apply fatigue damage models (such as Miner's law) to evaluate the fatigue damage accumulation of each layer. Through the analysis of different loading cycles, identify the key areas of fatigue damage and potential failure modes. Pay special attention to areas with significant stress concentration and thermal strain, and evaluate the fatigue life and failure risk of these areas. This will help identify the weak links in the design for subsequent optimization. Based on the fatigue analysis results, construct a multi-level fatigue evolution trajectory to reflect the fatigue evolution process of different layers under multiple cyclic loads. This trajectory should show the fatigue damage state, key change points and failure modes of different layers. Visualize the fatigue evolution trajectory through graphical tools to facilitate intuitive understanding and analysis of fatigue state changes between different layers. Focus on marking areas with large fatigue damage for subsequent design improvements. Conduct in-depth analysis of the fatigue evolution trajectory to identify key factors affecting fatigue life, such as material properties, thermal strain effects and stress concentration. Based on these factors, make targeted optimization suggestions, such as material replacement, thickness adjustment, and structural optimization. Combined with experimental parameters, evaluate the impact of different design schemes on fatigue performance to improve the reliability and durability of the package. Organize all the results of fatigue analysis, including stress distribution, fatigue damage accumulation, fatigue evolution trajectory, etc., and generate a detailed fatigue analysis report. The report should include model description, analysis method, key results, and optimization suggestions. In the report, describe the fatigue performance and potential failure modes of each layer in detail, and provide targeted improvement solutions to improve the reliability and durability of the package.

[0020] Step S6: Optimize global packaging parameters according to the multi-level fatigue evolution trajectory, and perform three-dimensional model iterative calculation on the initial multi-layer packaging three-dimensional model, so as to construct a final optimized three-dimensional chip packaging model.

[0021] In this embodiment, areas and key layers with more serious fatigue damage are identified. The fatigue performance of these areas is analyzed to understand the relationship between them and factors such as stress concentration and thermal strain. The fatigue life, damage distribution and failure mode of each layer are recorded. This information will provide an important basis for the subsequent optimization of packaging parameters. According to the results of fatigue analysis, the optimization goals are determined, such as improving fatigue life, reducing stress concentration, optimizing thermal management, etc. At the same time, the packaging parameters that need to be optimized are identified, such as material thickness, material type, interlayer bonding strength and structural geometry. Combined with experimental data and industry standards, the scope and constraints of optimization are set to ensure the feasibility and effectiveness of the optimization process. According to the complexity and objectives of the problem, a suitable optimization algorithm is selected. Common methods include genetic algorithms, particle swarm optimization (PSO) and response surface method. These algorithms can find the optimal solution in multidimensional parameter space. When selecting an algorithm, the nonlinear characteristics of the packaging structure and the multi-objective optimization requirements are taken into account to ensure that the selected algorithm can effectively handle these complexities. An optimization model is constructed in the simulation software to integrate the fatigue evolution trajectory analysis results and packaging parameter information. The model should be able to evaluate the impact of each parameter adjustment on fatigue performance in real time. Set the optimization objective function, such as maximizing fatigue life and minimizing stress concentration, and define constraints to ensure that the optimization results meet the actual application requirements. Start the global package parameter optimization calculation and iterate the model using the selected optimization algorithm. Monitor the results of each iteration and evaluate the impact of parameter adjustment on fatigue performance. During the optimization process, record the parameter values ​​and their corresponding fatigue performance indicators for each iteration for subsequent comparison and analysis. During the optimization calculation process, verify the results regularly to ensure the rationality of the optimization direction. If unreasonable results are found, adjust the optimization strategy or parameter range in time. Check the convergence of the optimization process to ensure that the algorithm can stably tend to the global optimal solution. If necessary, adjust the algorithm parameters to improve the convergence speed and reliability. Apply the optimized parameters to the initial multi-layer package 3D model and perform iterative calculations of the 3D model. Ensure that the material properties, geometric dimensions and connection methods of each layer meet the optimization results. Run simulation calculations to evaluate the performance of the optimized model under actual working conditions, especially in terms of fatigue life, stress distribution and thermal management. According to the results of iterative calculations, further adjust the 3D model to ensure that it meets the optimization goals in all performance indicators. Generate the final 3D chip package model and keep detailed records. The final model should have good fatigue performance, thermal management capabilities and structural stability, laying the foundation for subsequent product development and practical applications. Organize the records of the entire optimization process, including the initial model, optimization goals, optimization results and final model. Form a systematic document for subsequent review and reference. Summarize the challenges and solutions encountered during the optimization process, and provide suggestions and ideas for future optimization work.

[0022] In this embodiment, refer to Figure 2 , is a flowchart of detailed implementation steps of step S1. In this embodiment, the detailed implementation steps of step S1 include: Step S11: Obtain chip production logs; perform chip functional characteristics analysis based on the chip production logs to generate functional characteristics of the current production chip; Step S12: performing packaging demand analysis based on the functional characteristics of the currently produced chip to obtain chip packaging demand characteristics; Step S13: defining a multifunctional layer structure according to the chip packaging requirement characteristics to obtain a functional layer structure of each layer; Step S14: performing chip three-dimensional shape and size analysis on the chip production log to generate chip three-dimensional shape and size features; Step S15: Perform global multi-level structural simulation on the three-dimensional shape and size characteristics of the chip and the functional layer structure of each layer to construct an initial multi-layer packaging three-dimensional model.

[0023] In this embodiment, the chip production log is extracted from the production management system to ensure that the log contains detailed information of each production batch, such as production time, production equipment, raw material batch, process parameters and production personnel. These data will provide a basis for the subsequent functional characteristics analysis. The log is preliminarily cleaned and sorted to remove irrelevant data to ensure the data quality in the subsequent analysis. The timestamp of each log entry and the associated chip model are recorded for easy tracking. The parameters related to the functional characteristics in the chip production log are analyzed, such as the electrical properties, thermal properties and mechanical strength of the material. These parameters are usually tested during the production process and recorded in the log. Statistical analysis methods are used to calculate the mean, standard deviation and range of each functional characteristic to identify the normal range and potential anomalies of each characteristic. During the analysis process, a distribution map or trend map can be generated using a chart tool to assist in understanding the data. The functional characteristics obtained by the analysis are organized into a report, including the specific values ​​of each characteristic and the corresponding analysis results. Ensure that the test method and results of each characteristic are clearly listed in the report to facilitate the subsequent packaging requirements analysis. Finally, the functional characteristics of the current production chip are archived for subsequent use and comparison to ensure the traceability and reliability of the data. According to the functional characteristics of the current production chip generated in step S11, determine the key factors affecting the packaging requirements. These factors may include the size, heat output, power consumption, environmental tolerance, etc. of the chip. Collect relevant packaging materials and technical parameters to ensure that the analysis process covers the packaging solutions suitable for the current chip characteristics. For example, the thermal conductivity, temperature resistance and mechanical strength of different materials. Use multivariate analysis methods to explore the relationship between the current chip functional characteristics and packaging requirements. Regression analysis or correlation analysis can be used to identify the degree of influence of functional characteristics on packaging requirements characteristics. For each functional characteristic, analyze its specific impact on packaging structure, material selection and process requirements. For example, if the chip power consumption is high, better heat dissipation design and thermal conductive materials need to be considered. Organize the analysis results and generate a packaging requirement characteristics report. The report should include a summary of the packaging requirements for each functional characteristic, ensuring that the required materials, structures and technical requirements are clearly listed. Record the basis and analysis process of the packaging requirement characteristics to facilitate the subsequent definition and model construction of the multifunctional layer structure and ensure the traceability of all data. According to the chip packaging requirement characteristics generated in step S12, determine the design goals and requirements of the multifunctional layer structure. This may include thermal management layer, protective layer, signal transmission layer and power management layer. Collect the material properties and technical parameters required for each layer to provide a basis for the design of each layer. For example, the thermal management layer needs to have good thermal conductivity, while the protective layer needs to have excellent mechanical strength and corrosion resistance. Design the functional layer structure of each layer to ensure that it meets the packaging requirements of the chip. To this end, CAD software can be used for 3D modeling to ensure that the size, shape and material selection of each layer meet the design requirements.During the design process, consider the interaction and coordination between different layers to ensure that each layer can work together effectively and meet the overall packaging performance requirements. Organize the design results of each layer and generate a functional layer structure document. The document should describe the function, material, structural parameters and process requirements of each layer in detail to ensure the integrity and executability of the design. Record the challenges and solutions encountered during the design process to facilitate subsequent optimization and improvement. Extract dimension-related data from the chip production log, including the length, width, height and tolerance range of the chip. These data are usually measured during the production process and recorded in the log. Ensure that all relevant data are organized and cleaned to remove outliers for the accuracy of subsequent analysis. Use statistical analysis methods to analyze the extracted dimension data and calculate the mean, standard deviation and extreme value of each dimension. Use histograms or box plots to visualize the dimension distribution to help identify potential dimension variations. Identify key parameters in dimensional features, such as maximum and minimum values, core size and tolerance, which will affect subsequent packaging design and fit. Organize the analysis results to form a three-dimensional morphological dimension feature report. The report should list the statistical results and distribution of each dimension in detail, and provide visual charts for easy understanding. Ensure that all analysis methods and data sources are recorded in the report to maintain data traceability and provide support for subsequent work. Combined with the three-dimensional morphological size features generated in step S14 and the functional layer structure defined in step S13, use CAD software or simulation tools to build an initial three-dimensional model. This step requires ensuring that the dimensions of the model are accurate and meet actual production requirements. In model construction, ensure that the functions and structures of each layer are correctly nested, and consider the connection method between layers to ensure the stability and reliability of the overall structure. Perform global multi-level structural simulation on the initial multi-layer package three-dimensional model to evaluate its performance under different working conditions. This includes stress analysis, thermal analysis, and fluid dynamics analysis. Apply technologies such as finite element analysis (FEA) or computational fluid dynamics (CFD) to simulate and test the model to identify possible weak links and performance bottlenecks. Record the simulation results, analyze the performance of each layer under different conditions, and identify parameters that need to be optimized. For example, if the temperature of the thermal management layer is too high, it is necessary to consider adjusting the material or adding heat dissipation design. Based on the simulation results, the model is iteratively optimized to ensure that the final multi-layer package design can meet performance requirements and production feasibility.

[0024] In this embodiment, refer to Figure 3 , is a flowchart of detailed implementation steps of step S2. In this embodiment, the detailed implementation steps of step S2 include: Step S21: Identify the packaging materials layer by layer in the chip production log and extract the packaging material information of each layer; Step S22: mining the packaging material attributes of each layer of packaging material information to generate packaging material attributes of each layer; Step S23: performing layer-by-layer microfluidic channel analysis on the initial multi-layer packaging three-dimensional model to extract the microfluidic channels of each layer; Step S24: performing multi-level packaging simulation on the microfluidic channels of each layer according to the packaging material properties of each layer to obtain multi-level packaging simulation data.

[0025] In this embodiment, the production log is extracted from the chip production management system to ensure that the log contains information related to the packaging material, such as material type, batch number, supplier, usage and application level. The data should cover all production batches to ensure the comprehensiveness of the information. The extracted data is cleaned and sorted to remove duplicate and irrelevant data to ensure the accuracy of subsequent analysis. The packaging material information of each layer is recorded, and a unique identifier is assigned to each layer for subsequent traceability and analysis. Each layer is analyzed layer by layer to identify the packaging material used. For example, if the chip is divided into multiple layers (such as a protective layer, a heat dissipation layer, and a signal layer), the specific material type (such as polymer, metal, ceramic, etc.) used in each layer must be recorded separately. Use the material database and technical information provided by the supplier to confirm the material properties of each layer, and check the information recorded in the production log to ensure the accuracy and consistency of the material. Sort out the identified material information and generate a detailed material information report. The report should include the material type, batch number, supplier, usage and its characteristics of each layer, such as conductivity, thermal conductivity and mechanical strength. Ensure that the source and quality control information of each layer of material is recorded in the report to provide a basis for subsequent material property mining and packaging performance analysis. Collect performance data related to each material, including thermal conductivity, dielectric constant, compressive strength, tensile strength, and chemical stability. This data can usually be obtained from material manuals or supplier's technical documents. Determine the key attribute indicators to be mined for subsequent analysis and evaluation. For example, if the packaging material of a certain layer is a conductive polymer, its electrical conductivity and thermal conductivity should be focused on. Use statistical analysis and data mining techniques to analyze the material properties of each layer. Descriptive statistics can be used to calculate the mean, standard deviation, and range of each material attribute to identify its performance characteristics. Use data visualization tools, such as radar charts or heat maps, to display the different properties of each layer of material for intuitive understanding and comparison. Pay special attention to materials with abnormal performance or that do not meet design requirements. Organize the analysis results and generate a packaging material property report for each layer. The report should include a detailed description of all key attributes, statistical data, and visualization charts. Ensure that the applicability and constraints of each material are recorded in the report to provide a reference for subsequent microfluidic channel analysis and packaging simulation. Start with the initial multi-layer packaging 3D model in step S15 to ensure the integrity and accuracy of the model. Check the geometry of each layer to ensure that the channel layout meets the design requirements. Use CAD software to extract the microfluidic channels of each layer to ensure that the size, shape and connection method of the channels meet the actual design. Analyze the microfluidic channels of each layer layer by layer to identify the key parameters of the channels, such as channel width, height, length and curvature. These parameters will affect the flow characteristics of the fluid and the overall packaging performance. Use fluid dynamics simulation tools to perform preliminary flow analysis of the channels to evaluate the flow behavior of the fluid in the channels and ensure that the channel design can meet the expected flow requirements.Organize the microfluidic channel information obtained from the layer-by-layer analysis and generate a channel characteristics report. The report should include the geometric parameters, flow characteristics, and potential flow bottlenecks or design defects of each layer of the channel. Ensure that the information in the record is clear and easy to understand, and provide basic data for subsequent multi-level packaging simulation. Combine the material properties and microfluidic channel information generated in steps S22 and S23 to prepare for multi-level packaging simulation. Ensure that the material properties of each layer in the model can accurately reflect its performance characteristics. Select appropriate simulation software (such as ANSYS Fluent or COMSOL Multiphysics) to ensure that the software has multi-level structure simulation capabilities and can handle complex fluid dynamics problems. Set the material properties of each layer and the flow conditions of the microfluidic channel in the simulation software, including flow rate, pressure, and temperature. Set appropriate boundary conditions and initial conditions according to the actual application scenario. Run the simulation to obtain the flow characteristics and performance data of each layer under different conditions. Monitor key indicators such as flow rate distribution, pressure loss, and temperature change. Analyze the simulation results and extract the flow data of the multi-level package. Determine the performance of each layer under different working conditions and identify potential flow bottlenecks and optimization space. Organize simulation data and generate multi-level packaging simulation reports. The reports should include key data, analysis results and optimization suggestions to provide a basis for subsequent packaging design and performance improvement.

[0026] In this embodiment, refer to Figure 4 , is a flowchart of detailed implementation steps of step S3. In this embodiment, the detailed implementation steps of step S3 include: Step S31: performing dynamic flow field simulation behavior analysis on multi-level packaging simulation data to extract dynamic flow field simulation behavior features; Step S32: performing micro-flow field stress evolution on the dynamic flow field simulation behavior characteristics, thereby generating micro-flow field stress evolution laws; Step S33: performing multi-layer structure package association mining according to the micro-flow field stress evolution law, thereby extracting multi-layer structure package association features; Step S34: performing interlayer stress mutual influence analysis on the initial multilayer package three-dimensional model according to the multilayer structure package association characteristics, thereby obtaining the interlayer stress mutual influence relationship.

[0027] In this embodiment, the relevant parameters of the flow field, including flow velocity, pressure distribution and temperature change, are extracted from the multi-level packaging simulation data obtained in step S24. These data should cover the flow state under different conditions and time points for dynamic analysis. Clean and organize the data to ensure the integrity and accuracy of the data. Classify the data by level and time sequence for subsequent analysis. Use fluid dynamics analysis software to dynamically analyze the organized flow field data. This step includes a detailed study of the time-varying characteristics of the velocity field, pressure field and temperature field. The time series analysis method is used to identify the dynamic characteristics of the flow field behavior, such as the pulsation, oscillation and vortex structure of the flow. By comparing the data at different time points, the changing trend of the flow behavior is analyzed. Key features such as maximum flow velocity, flow field mean, vorticity, etc. are extracted from the dynamic analysis. These features will be used for subsequent stress evolution analysis to help understand the impact of flow on structural stress. The analysis results are organized to form a flow field behavior feature report, which should list the dynamic characteristics and change laws of each layer of flow field in detail for subsequent use and reference. According to the dynamic flow field behavior characteristics extracted in step S31, a microfluidic field stress calculation model is established. The model should consider the force exerted by the fluid on the packaging structure and the stress transmission path. Select a suitable calculation tool (such as finite element analysis software) to ensure that it can handle the interaction between fluid and solid. Input the flow velocity, pressure and other data of the dynamic flow field into the calculation model to perform stress evolution analysis. Simulate the stress changes imposed by the fluid on the packaging structure under different flow conditions, especially focusing on high flow velocity and high pressure areas. Identify the time-varying characteristics of stress, and analyze the impact of changes in flow conditions on the packaging structure by calculating parameters such as stress distribution, strain and displacement. Sort out the analysis results and generate a report on the evolution law of micro-flow field stress. The report should include the stress distribution and evolution trend under different flow conditions to help understand the impact of flow field changes on structural performance. Ensure that the stress state under each flow condition is described in detail in the record, so as to provide an important basis for subsequent structural packaging association mining. Based on the micro-flow field stress evolution law generated in step S32, analyze the stress transfer and mutual influence between different layers. Determine which layers' stress changes will affect the performance of other layers. Collect the material properties, geometry, and interconnections of each layer for comprehensive correlation analysis. Use data mining techniques (such as cluster analysis and principal component analysis) to identify the correlation between stress evolution and packaging performance in multilayer structures. This step helps to reveal the relationship between the layers and their impact on the overall performance. By analyzing the stress data under different flow conditions, extract significant correlation features and clarify which factors play a key role in the multilayer structure. Organize the results of correlation mining to form a multilayer structure packaging correlation feature report. The report should include the correlation analysis results between the layers and their impact on packaging performance to ensure that the results are clear and understandable. Record the practical application potential of these features to facilitate subsequent packaging design optimization and performance improvement.Based on the multilayer structure package association features extracted in step S33, select appropriate analysis tools and methods to evaluate the mutual influence of stress between layers. Ensure that the model can truly reflect the physical properties and interactions of each layer of material. Collect the material parameters and geometric dimensions required for the model, and prepare for a detailed analysis of interlayer stress. In the simulation software, build a multilayer package model containing all layers, input the stress data and material properties of each layer, and perform simulation analysis of the mutual influence of interlayer stress. Identify the interaction between different layers during stress application and transfer, focus on analyzing the impact of high stress areas on adjacent layers, and ensure that the design of each layer can withstand the stress caused by flow and temperature changes. Sort out the analysis results and form a report on the mutual influence relationship between interlayer stress. The report should include the specific situation and degree of influence of stress transfer between layers to ensure the systematic and comprehensive information. Record the practical application value of the analysis results to guide the subsequent packaging design and optimization process to ensure the reliability and performance stability of the final product.

[0028] In this embodiment, the specific steps of step S32 are: Analyze the velocity distribution of dynamic flow field simulation behavior characteristics to obtain velocity distribution data of different structures; Calculate the transient fluid pressure based on the dynamic flow field simulation behavior characteristics and extract the transient packaging fluid pressure of each layer; Identify the packaging pressure change of each layer's transient packaging fluid pressure and extract the packaging pressure change characteristics of each layer; Predicting potential turbulence phenomena based on multi-level packaging simulation data to obtain potential turbulence phenomena; Conduct turbulence intensity estimation on potential turbulence phenomena to generate potential turbulence intensity; The micro-flow field stress evolution is carried out according to the velocity distribution data of different structures, the packaging pressure change characteristics of each layer and the potential turbulence intensity, thereby generating the micro-flow field stress evolution law.

[0029] In this embodiment, the velocity field data is extracted from the previous dynamic flow field simulation results. These data should include the velocity distribution at different time points to ensure that different states of flow are covered, especially the velocity changes in key areas. Arrange the data for easy analysis and ensure that the velocity data is classified by level and position for subsequent analysis and comparison. Use fluid dynamics analysis software to analyze the extracted velocity data in detail. Contour maps or streamline maps can be used to display the velocity distribution of different structures to help identify flow characteristics. Calculate the mean, maximum and minimum values ​​of the velocity at different positions to identify the trend of the velocity distribution and possible abnormal areas. These data are crucial for subsequent pressure calculations and turbulence predictions. Arrange the analysis results to form a velocity distribution data report. The report should include velocity characteristics, velocity change trends and flow characteristics descriptions of different structures. Record the potential impact of velocity distribution on packaging performance for subsequent analysis and optimization design. In the fluid dynamics analysis software, a transient fluid pressure calculation model is established. Ensure that the model can accurately reflect the pressure changes of the fluid under different conditions, especially at key positions of each layer. Input flow rate data, fluid properties (such as density and viscosity), and boundary conditions to ensure the accuracy and reliability of the simulation. Run transient pressure calculations to monitor the pressure changes of the fluid at different time points. Pay special attention to pressure fluctuations in high flow rate areas to identify possible transient phenomena. Extract transient fluid pressure data for each layer to ensure that the pressure changes of each layer can be accurately recorded. Organize the transient fluid pressure calculation results and form a report. The report should include pressure change data, pressure peaks and fluctuations for each layer, and analysis of the impact on packaging performance. Record the parameters and assumptions used in the calculation process to ensure data traceability and reliability. Based on the transient fluid pressure data generated in step 2, prepare for pressure change identification analysis. Ensure that the pressure data of each layer can accurately reflect the changes of the fluid during the flow process. Set the identification criteria for pressure changes, such as pressure change rate, fluctuation amplitude, etc., to facilitate subsequent analysis. Analyze the transient pressure data of each layer to identify significant pressure change features. This can be achieved by calculating the derivative or rate of change of pressure to help determine the frequency and amplitude of pressure fluctuations. Focus on the time points when the pressure changes sharply to identify transient phenomena that may cause packaging problems. Organize the identification results and generate a report on the pressure change characteristics of each layer of the package. The report should list in detail the pressure change data of each layer, the change rules and their impact on the package performance. Record the practical application value of the pressure change characteristics to guide subsequent package design and optimization. Prepare a turbulence prediction model based on the flow rate and pressure data. Ensure that the model can reflect the complexity of the flow field and the potential turbulence characteristics. Collect the Reynolds number and other relevant parameters of the fluid to determine the turbulent characteristics of the flow state. Use a turbulence model (such as the k-ε model or the LES model) to simulate the flow field and identify potential turbulent areas. These areas are usually where the flow velocity changes dramatically or the pressure fluctuates greatly.Analyze the turbulence phenomena at different layers, focusing on the turbulence areas that may affect the package performance. Organize the analysis results and form a report on potential turbulence phenomena. The report should include the identified turbulence areas, turbulence intensity and its potential impact on the package performance. Record the basis and model parameters for turbulence prediction to ensure transparency and traceability of the results. Determine the method for estimating turbulence intensity based on the identified potential turbulence phenomena. This includes the calculation of the standard deviation of velocity fluctuations and pressure fluctuations as indicators of turbulence intensity. Collect necessary fluid parameters (such as density and viscosity) for accurate calculations. Analyze the identified turbulence areas and calculate the turbulence intensity. The turbulence intensity can be estimated by calculating the ratio of turbulent kinetic energy to the average flow velocity. Focus on monitoring the flow characteristics of the turbulent areas and identify the change pattern of turbulence intensity. Organize the turbulence intensity estimation results and generate a report. The report should include the turbulence intensity data of each layer, the change trend and its impact analysis on the package performance. Record the parameters and assumptions in the estimation process to ensure transparency and traceability of the results. Establish a stress evolution model for micro flow fields. The model should be able to integrate multiple factors such as velocity distribution, pressure changes, and turbulence intensity. Collect the physical properties of the fluid (such as density and viscosity) to ensure the accuracy of the model. Input velocity, pressure, and turbulence intensity data into the model to calculate stress evolution. Simulate the stress changes imposed by the fluid on the packaging structure and identify the time-varying characteristics of stress. Calculate the stress distribution under different structures and analyze the impact of flow conditions on structural performance, especially the identification of high stress areas. Organize the results of stress evolution analysis to form a report on the stress evolution law of micro-flow fields. The report should list in detail the specific laws of stress evolution, influencing factors, and the impact on packaging performance. Record the data and methods in the analysis process for subsequent optimization and improvement.

[0030] In this embodiment, step S4 includes the following steps: Step S41: calculating the thermal conductivity difference of the materials according to the properties of the packaging materials of each layer, and obtaining the thermal conductivity difference values ​​of packaging materials with different structures; Step S42: performing layer-by-layer structure heat conduction analysis on the multi-level packaging simulation data to extract the heat conduction characteristics of each layer; Step S43: performing multi-layer thermal stress analysis on the heat conduction characteristics of each layer to generate multi-layer thermal stress data; Step S44: performing temperature gradient analysis on the multi-level packaging simulation data one by one to obtain a temperature gradient curve of each layer structure; Step S45: Evolving the local thermal expansion effect according to the temperature gradient curve of each layer structure and the multi-layer thermal stress data to obtain the local thermal expansion effect of each layer; Step S46: performing multi-layer thermal strain evolution on the local thermal expansion effect of each layer based on the thermal conductivity difference value of packaging materials with different structures, thereby generating thermal strain evolution characteristics of different structural layers.

[0031] In this embodiment, the packaging material properties of each layer, especially the thermal conductivity data, are extracted from step S22. These data usually come from the technical data manual of the material or the experimental measurement results to ensure their accuracy and availability. The data is sorted and the thermal conductivity of each layer of material is listed in a table to facilitate subsequent analysis and comparison. For packaging materials with different structures, the difference in thermal conductivity is calculated. The formula: thermal conductivity difference = absolute value (thermal conductivity of material A-thermal conductivity of material B) can be used for calculation to compare the thermal conductivity of each layer of material. The difference in thermal conductivity between different layers is recorded to provide basic data for subsequent thermal conduction analysis. During the calculation process, attention is paid to materials with significant thermal conductivity differences in order to conduct in-depth thermal stress analysis. The calculation results are sorted and a thermal conductivity difference report is generated. The report should list in detail the thermal conductivity of each layer of material and its difference value, and analyze its impact on the overall thermal performance of the package. Record the data sources and assumptions used in the calculation process to ensure the transparency and traceability of the results for subsequent analysis. Based on the multi-level packaging simulation data in step S24, a thermal conduction analysis model is established. Ensure that the model can accurately reflect the geometry, material properties and interface conditions of each layer. Input the physical properties of each layer such as thermal conductivity, specific heat capacity and density, and set appropriate boundary conditions such as heat source, ambient temperature, etc. Run the heat conduction simulation to analyze the heat flow, temperature distribution and heat conduction efficiency layer by layer. Focus on the thermal bridge effect and heat loss between the layers. Extract the heat conduction characteristics of each layer, including heat flux density, temperature gradient and heat conduction efficiency, for subsequent analysis. Sort out the analysis results and generate a heat conduction characteristic report for each layer. The report should include the heat flow characteristics, temperature distribution and thermal efficiency data of each layer to ensure the integrity and accuracy of the information. Record the model parameters and assumptions to ensure the credibility and traceability of the results, and provide a basis for subsequent thermal stress analysis. Based on the heat conduction characteristic data extracted in step S42, establish a multi-layer thermal stress analysis model. Ensure that the model can reflect the thermal stress caused by temperature changes on each layer. Input the heat conduction characteristics of each layer, including temperature change rate, heat flow and material thermal expansion coefficient, for accurate thermal stress calculation. Run the thermal stress simulation to analyze the stress and deformation caused by temperature changes on each layer. Focus on high temperature gradient areas to identify potential thermal stress concentration points. Extract the thermal stress data of each layer, record the stress distribution, stress peak and change trend, and provide a basis for subsequent thermal expansion effect analysis. Organize the thermal stress analysis results and generate a multi-layer thermal stress data report. The report should include the thermal stress characteristics, stress distribution diagram and influencing factor analysis of each layer. Record the parameters and model settings used in the analysis process to ensure the transparency and traceability of the results for subsequent analysis. Based on the multi-layer packaging simulation data in step S24, establish a temperature gradient analysis model. Ensure that the model can accurately reflect the temperature distribution and its changes of each layer. Input the heat flow data and thermal conduction characteristics of each layer, and set boundary conditions such as ambient temperature and heat source for temperature gradient analysis.Run the simulation and analyze the temperature gradient change layer by layer. Extract the temperature data of each layer, calculate the temperature gradient, and draw the temperature gradient curve. Focus on the areas with large temperature gradient changes to identify the layers that may cause thermal stress and deformation. Organize the temperature gradient analysis results and generate a temperature gradient curve report for each layer of the structure. The report should include the trend of temperature gradient changes, key data and influencing factors. Record the parameters and assumptions used in the analysis process to ensure the credibility and traceability of the results for subsequent analysis. Based on the multi-layer thermal stress data generated in step S43 and the temperature gradient curve in step S44, establish a local thermal expansion effect evolution model. The model should be able to reflect the effect of temperature changes on the local expansion of each layer. Enter the thermal stress data, temperature gradient and material thermal expansion coefficient of each layer for thermal expansion calculation. Run the simulation to analyze the local thermal expansion effects of temperature gradient and thermal stress on each layer. Focus on high temperature gradients and high stress areas to identify potential thermal expansion problems. Extract the local thermal expansion data of each layer, record the expansion amplitude and change trend, and provide a basis for subsequent thermal strain analysis. Organize the local thermal expansion effect analysis results and generate a report. The report should include the local thermal expansion characteristics, influencing factors and potential problem analysis of each layer. Record the parameters and assumptions in the analysis process to ensure the transparency and traceability of the results, and provide a basis for subsequent optimization. According to the thermal conductivity difference data in step S41 and the local thermal expansion effect in step S45, a multi-layer thermal strain evolution model is established. Ensure that the model can integrate thermal conductivity, temperature change and thermal expansion effects. Input the thermal conductivity difference, local thermal expansion data and elastic modulus of each layer for thermal strain calculation. Run the simulation to analyze the impact of thermal conductivity difference on the local thermal strain of each layer. Focus on the strain transfer and interaction between different materials. Extract the thermal strain data of each layer, record the strain amplitude and change law, and identify potential structural problems. Sort out the results of thermal strain evolution analysis and generate a report on the thermal strain evolution characteristics of different structural layers. The report should include the thermal strain characteristics, influencing factors and potential problem analysis of each layer. Record the data and methods in the analysis process to ensure the transparency and traceability of the results, and provide a basis for subsequent structural optimization.

[0032] In this embodiment, step S5 includes the following steps: Step S51: performing multi-level stress fatigue analysis based on the inter-layer stress mutual influence relationship and the thermal strain evolution characteristics of different structural layers, and extracting multi-level stress fatigue data; Step S52: extracting stress distribution and strain energy density of multi-layer contact surfaces according to multi-level stress fatigue data; Step S53: performing interlayer contact fatigue analysis on the stress distribution and strain energy density to generate interlayer contact fatigue characteristics; Step S54: analyzing the internal stress release mechanism of the multi-level stress fatigue data to generate internal stress release mechanism characteristics; Step S55: predicting the propagation of local stress failure cracks according to the characteristics of the internal stress release mechanism, thereby obtaining the propagation path of the local failure cracks; Step S56: Perform multi-level fatigue evolution reconstruction on the local failure crack propagation path according to the interlayer contact fatigue characteristics to construct a multi-level fatigue evolution trajectory.

[0033] In this embodiment, the interlayer stress mutual influence relationship and the thermal strain evolution characteristic data of different structural layers are extracted from steps S34 and S46. These data will be used to establish a multi-level stress fatigue analysis model. Collect necessary material fatigue property data, including fatigue limit, fatigue strength and fatigue life of the material, which are usually derived from experimental tests or material manuals. Use finite element analysis software to establish a multi-level fatigue analysis model. The model needs to integrate the stress distribution, thermal strain and material properties of each layer to ensure that the fatigue process can be accurately simulated. Cyclic loads and temperature changes are applied to the model, and fatigue analysis is run to predict the fatigue life and failure mode of each layer. Pay special attention to stress concentration areas, which are usually most prone to fatigue failure. Extract multi-level stress fatigue data from the analysis results, including fatigue life, stress amplitude and failure mode of each layer. Record the starting position and expansion trend of fatigue cracks for subsequent analysis. Organize the data and generate a multi-level stress fatigue characteristic report to ensure the integrity and ease of understanding of the information. Based on the multi-level stress fatigue data extracted in step S51, a contact surface stress analysis model is established. The model should be able to reflect the actual contact situation and stress distribution of the interlayer contact surface. Input stress data, contact surface material properties and geometric parameters of each layer to ensure the accuracy of the model. Run contact surface stress analysis to extract stress distribution of each contact surface. Focus on stress concentration and strain energy density, which are usually the key points of fatigue failure. Calculate the strain energy density of the contact surface to identify the energy distribution of different contact surfaces under load. Organize the analysis results and generate a stress distribution and strain energy density feature report for multi-layer contact surfaces. The report should include contact surface stress distribution diagram, strain energy density data and influencing factor analysis. Record the parameters and assumptions used in the analysis process to ensure the transparency and traceability of the results, and provide a basis for subsequent fatigue analysis. Based on the stress distribution and strain energy density data extracted in step S52, establish an interlayer contact fatigue analysis model. Ensure that the model can reflect the interaction and fatigue behavior of different layers on the contact surface. Collect the fatigue strength, contact stress and corresponding fatigue curves of the material for accurate fatigue analysis. Run fatigue analysis to evaluate the fatigue behavior of the contact surface under load and temperature changes. Focus on fatigue damage and crack propagation in stress concentration areas. Extract the analysis results, identify the starting position and expansion direction of the fatigue crack, and generate the interlayer contact fatigue characteristics. Organize the analysis results and generate an interlayer contact fatigue characteristics report. The report should include the starting position, expansion path and influencing factor analysis of the fatigue crack. Record the data and methods in the analysis process to ensure the transparency and traceability of the results, and provide a basis for subsequent crack propagation analysis. Based on the multi-level stress fatigue data in step S51, establish an internal stress release mechanism analysis model. Ensure that the model can reflect the stress release process and its impact on material properties. Collect relevant material internal stress data and release mechanism literature to determine the key parameters of the analysis.Run the model to analyze the stress release process, including temperature changes, load changes, and material yield behavior. Identify the release mode of internal stress and its impact on material properties. Extract the characteristics of the internal stress release mechanism, including the released stress amplitude, release rate, and impact on fatigue behavior. Organize the analysis results and generate a report on the characteristics of the internal stress release mechanism. The report should include a description of the stress release process, influencing factors, and their potential impact on fatigue behavior. Record the parameters and assumptions in the analysis process to ensure the transparency and traceability of the results, and provide a basis for subsequent crack propagation prediction. Based on the multi-level stress fatigue data in step S51, establish an internal stress release mechanism analysis model. Ensure that the model can reflect the stress release process and its impact on material properties. Collect relevant material internal stress data and release mechanism literature to determine the key parameters of the analysis. Run the model to analyze the stress release process, including temperature changes, load changes, and material yield behavior. Identify the release mode of internal stress and its impact on material properties. Extract the characteristics of the internal stress release mechanism, including the released stress amplitude, release rate, and impact on fatigue behavior. Organize the analysis results and generate a report on the characteristics of the internal stress release mechanism. The report should include a description of the stress release process, influencing factors and their potential impact on fatigue behavior. Record the parameters and assumptions in the analysis process to ensure the transparency and traceability of the results, and provide a basis for subsequent crack propagation prediction. Establish a crack propagation prediction model based on the internal stress release mechanism characteristics generated in step S54. Ensure that the model can reflect the propagation behavior of cracks under internal stress. Collect the fracture toughness, crack growth rate and related fatigue data of the material for accurate prediction and analysis. Run the crack propagation model to analyze the effects of internal stress and fatigue load on local cracks. Focus on the starting position and propagation path of the crack. Extract the local failure crack propagation path data and record the direction and speed of crack growth for subsequent analysis. Organize the crack propagation prediction results and generate a local failure crack propagation path report. The report should include a description of the crack propagation path, influencing factors and their impact on material properties. Record the data and methods in the prediction process to ensure the transparency and traceability of the results, and provide a basis for subsequent fatigue evolution analysis. Based on the fatigue characteristics and crack propagation paths extracted in steps S53 and S55, a multi-level fatigue evolution trajectory model is established. Ensure that the model can integrate the fatigue behavior and crack propagation characteristics of different layers. Collect material data and experimental parameters related to fatigue evolution for accurate evolution analysis. Run the fatigue evolution model to analyze the evolution process of local failure cracks in multi-level structures. Focus on the impact and interaction of crack propagation on surrounding materials. Extract multi-level fatigue evolution trajectory data and record the evolution path, speed and impact range of the crack for subsequent analysis. Organize the fatigue evolution reconstruction results and generate a multi-level fatigue evolution trajectory report. The report should include the evolution path of the crack, influencing factors and their potential impact on structural performance.Record the data and methods during the analysis process to ensure the transparency and traceability of the results and provide a basis for subsequent structural optimization.

[0034] In this embodiment, step S6 includes the following steps: Step S61: optimizing the interlayer contact mode according to the multi-level fatigue evolution trajectory to generate an optimized multi-level contact mode; Step S62: Screening packaging materials for the multi-level fatigue evolution trajectory to obtain packaging material screening information; Step S63: adjusting the microfluidic channel size of the initial multi-layer packaging three-dimensional model according to the multi-level fatigue evolution trajectory to obtain the microfluidic channel adjustment size; Step S64: Optimizing global packaging parameters according to the optimized multi-level contact mode, packaging material screening information and microfluidic channel adjustment size, and constructing global packaging optimization parameters; Step S65: performing three-dimensional model iterative calculation on the initial multi-layer packaging three-dimensional model based on the global packaging optimization parameters, thereby constructing a final optimized three-dimensional chip packaging model.

[0035] In this embodiment, the contact mode between each layer and its influence are identified. Focus on the stress concentration and fatigue failure mode on the contact surface, and analyze the influence of different contact modes on fatigue life. Record the specific parameters of the contact mode, such as contact area, contact pressure and interface friction coefficient, to provide data support for subsequent optimization. Based on the analysis results of the fatigue evolution trajectory, an optimization design scheme is proposed. It can be considered to change the contact surface material, adjust the contact angle or increase the contact area to reduce stress concentration. Computer-aided design (CAD) tools are used to simulate the influence of different contact modes on fatigue performance and evaluate their feasibility and effect in practical applications. Organize the optimization scheme and generate an optimized multi-level contact mode report. The report should describe in detail the optimized design scheme, the expected performance improvement and its impact on the overall packaging performance. Record the parameters and assumptions used in the optimization process to ensure the transparency and traceability of the results to support subsequent material screening and channel adjustment. Collect material performance data suitable for multi-layer packaging from material databases and relevant literature, including thermal conductivity, mechanical strength, fatigue life and thermal expansion coefficient. Focus on the performance of these materials under different temperature and stress conditions. Organize material data for subsequent analysis and screening. Using the fatigue evolution trajectory data generated in step S61, determine the material properties that have a greater impact on fatigue performance. Screen out materials with excellent fatigue performance and thermal management capabilities to improve the reliability and durability of the package. Use a multi-criteria decision analysis method (such as hierarchical analysis or TOPSIS method) to comprehensively consider the performance indicators of each material and prioritize the materials. Organize the screening results and generate a packaging material screening information report. The report should include the final selected materials, performance comparisons, and selection basis. Record the data and assumptions in the material screening process to ensure the transparency and traceability of the results and provide support for subsequent microfluidic channel adjustments. From the multi-level fatigue evolution trajectory data extracted in step S56, identify the key parameters and characteristics of the microfluidic channel. Focus on fluid flow characteristics, pressure distribution, and temperature changes to determine the direction of improvement of channel design. Collect design standards and performance requirements related to microfluidic channels to ensure that the adjusted dimensions meet actual application requirements. Use CAD tools to adjust the initial multi-layer packaging three-dimensional model to optimize the size of the microfluidic channel. The flow efficiency can be improved and the pressure drop can be reduced by changing the width, height, and shape of the channel. Perform fluid dynamics simulation to evaluate the effect of the adjusted channel size on fluid flow and ensure that the optimized design can meet the flow requirements. Organize the adjustment results and generate a microfluidic channel adjustment size report. The report should include the adjusted channel size, design basis and performance evaluation. Record the data and assumptions during the adjustment process to ensure the transparency and traceability of the results, and provide a basis for subsequent global packaging parameter optimization. Based on the information generated in steps S61, S62 and S63, establish a global packaging parameter optimization model.The model should be able to integrate parameters such as contact mode, material properties and channel size. Collect relevant design standards and performance indicators to ensure that the optimization goals are clear. Use multi-objective optimization algorithms (such as genetic algorithms or particle swarm optimization algorithms) to optimize global packaging parameters. Set optimization goals, such as reducing stress concentration, improving flow efficiency and reducing thermal resistance. Run the optimization algorithm to evaluate the performance of different parameter combinations and identify the best packaging parameter combination. Organize the optimization results and generate a global packaging optimization parameter report. The report should include the optimized parameters, design basis and expected performance improvement. Record the data and assumptions in the optimization process to ensure the transparency and traceability of the results, and provide a basis for subsequent three-dimensional model iterative calculations. Obtain the global packaging optimization parameters from step S64 and prepare for iterative calculations of the initial multi-layer packaging three-dimensional model. Ensure that the model can reflect all optimized parameters and design requirements. Collect relevant simulation tools and computing resources to ensure the efficiency and accuracy of model iterative calculations. Enter the global packaging optimization parameters in the simulation software and run the iterative calculation of the three-dimensional model. Focus on key performance indicators such as fluid flow, heat conduction and stress distribution. Monitor key data during the calculation process to ensure the convergence and stability of the model and identify possible design defects or optimization space. Organize iterative calculation results and generate a final optimized 3D chip packaging model report. The report should include the optimized 3D model, performance evaluation and optimization basis. Record the data and methods in the calculation process to ensure the transparency and traceability of the results, and provide a basis for subsequent product development and quality control.

[0036] In this embodiment, a multi-layer structure integration system of a microfluidic chip package is provided, which is used to perform the multi-layer structure integration method of a microfluidic chip package as described above, including: The multi-layer packaging simulation module is used to obtain chip production logs, perform multi-functional layer structure definition and global multi-level structure simulation, and build an initial multi-layer packaging 3D model; The packaging simulation module is used to identify the packaging materials layer by layer in the chip production log and perform multi-level packaging simulation on the initial multi-layer packaging three-dimensional model to obtain multi-level packaging simulation data; The flow field simulation analysis module is used to perform dynamic flow field simulation behavior analysis on multi-layer packaging simulation data and to analyze the mutual influence of interlayer stresses, thereby obtaining the mutual influence relationship of interlayer stresses; Thermal strain evolution module, which is used to perform local thermal expansion effect evolution and multi-layer thermal strain evolution based on multi-layer packaging simulation data, thereby generating thermal strain evolution characteristics of different structural layers; Fatigue evolution reconstruction module, which is used to perform multi-level stress fatigue analysis based on the mutual influence relationship between inter-layer stresses and the thermal strain evolution characteristics of different structural layers, and to reconstruct multi-level fatigue evolution and construct multi-level fatigue evolution trajectories; The packaging parameter optimization module is used to optimize the global packaging parameters according to the multi-level fatigue evolution trajectory, and perform three-dimensional model iterative calculation on the initial multi-layer packaging three-dimensional model to construct the final optimized three-dimensional chip packaging model.

[0037] The present invention ensures that the package design is consistent with the actual production process by extracting actual production data (such as material selection, process flow, functional requirements, etc.) from the production log, reducing later modifications. The function of each layer of packaging is accurately defined to ensure that the packaging meets multiple functional requirements such as specific fluids, sensor protection, heat dissipation, etc., and provide a clear framework for subsequent simulation and optimization. The constructed initial three-dimensional model provides a basis for subsequent packaging analysis, quickly verifies the feasibility of multi-layer packaging design, and provides space for interactions and design optimization between different layers. According to the material information in the production log, the material properties used for each layer of packaging are accurately identified to ensure that the physical properties of each material match the design requirements and improve the accuracy of the simulation data. Multi-level packaging simulation provides detailed data support for subsequent analysis of fluid mechanics, heat conduction, mechanical stress, etc., which helps to identify potential structural problems or design defects. Through the feedback of simulation data, the interaction between different packaging materials and structural layers can be quickly verified to avoid problems in the later manufacturing process. Dynamic simulation of fluid flow in microfluidic channels reveals the changing trends of flow rate, flow direction, pressure, etc., ensures stable fluid flow, prevents unstable flow or bubble problems, and optimizes the fluid control performance of the chip. By analyzing the stress interaction between the layers, stress concentration points that may cause package failure can be identified, avoiding structural damage or functional failure caused by interlayer stress mismatch. Flow field analysis helps optimize the chip's fluid channel design, ensuring stable and efficient fluid flow, and helping to improve the chip's performance and reliability. By analyzing the thermal expansion effect and thermal strain characteristics of each layer of material, internal stress changes caused by temperature fluctuations can be revealed, especially in key areas such as microfluidic channels and sensor layers. Identifying thermal expansion mismatches between different materials and packaging layers helps design more high-temperature resistant packaging structures and reduce the risk of package failure caused by thermal stress. Thermal strain evolution data provides key information for thermal management solutions, helping to improve the chip's heat dissipation design and ensure stable operation in high-temperature environments. Through fatigue analysis, the performance degradation of the packaging structure in long-term use can be predicted, helping to evaluate the long-term stability of the chip, especially the reliability under repeated use or extreme conditions. By reconstructing the fatigue evolution trajectory, areas or layers prone to fatigue failure can be identified, providing designers with optimization solutions to avoid failures caused by fatigue cracking or material aging. Fatigue evolution analysis provides a basis for improving package design, which helps to improve the durability and stability of the chip during long-term operation. By feedback on the fatigue evolution trajectory, the package parameters (such as material selection, package structure, size adjustment, etc.) are optimized to ensure that the package achieves the best balance in terms of function, reliability and durability. Through iterative calculation of the three-dimensional model, the geometry and material distribution of the package are continuously optimized to ensure that the package design meets production conditions and provides the best performance.The optimization module can significantly shorten the design cycle, quickly correct defects in the packaging design through intelligent optimization algorithms, improve overall design efficiency, and reduce the error rate that may occur during the production process.

[0038] Therefore, the embodiments should be regarded as illustrative and non-restrictive from all points, and the scope of the present invention is limited by the appended claims rather than the above description, and it is therefore intended that all changes falling within the meaning and range of equivalent elements of the application documents are included in the present invention.

[0039] The above is only a specific embodiment of the present invention, so that those skilled in the art can understand or implement the present invention. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but will conform to the widest scope consistent with the principles and novel features invented herein.

Claims

1. A multi-layer structure integration method for microfluidic chip packaging, characterized in that: The following steps are involved: Step S1: Obtain chip production logs, perform multi-functional layer structure definition and global multi-level structure simulation, and construct an initial multi-layer package three-dimensional model; Step S2: Identify the packaging materials layer by layer on the chip production log, and perform multi-level packaging simulation on the initial multi-layer packaging three-dimensional model to obtain multi-level packaging simulation data; Step S3: performing dynamic flow field simulation behavior analysis on the multi-level packaging simulation data, and performing inter-layer stress mutual influence analysis, so as to obtain the inter-layer stress mutual influence relationship; Step S4: performing local thermal expansion effect evolution and multi-layer thermal strain evolution based on multi-layer packaging simulation data, thereby generating thermal strain evolution characteristics of different structural layers; Step S5: performing multi-level stress fatigue analysis based on the inter-layer stress mutual influence relationship and the thermal strain evolution characteristics of different structural layers, and reconstructing the multi-level fatigue evolution to construct a multi-level fatigue evolution trajectory; Step S6: Optimize global packaging parameters according to the multi-level fatigue evolution trajectory, and perform three-dimensional model iterative calculation on the initial multi-layer packaging three-dimensional model, so as to construct a final optimized three-dimensional chip packaging model.

2. The multi-layer structure integration method of microfluidic chip packaging according to claim 1, characterized in that: The specific steps of step S1 are: Step S11: Obtain chip production logs; perform chip functional characteristics analysis based on the chip production logs to generate functional characteristics of the current production chip; Step S12: performing packaging demand analysis based on the functional characteristics of the currently produced chip to obtain chip packaging demand characteristics; Step S13: defining a multifunctional layer structure according to the chip packaging requirement characteristics to obtain a functional layer structure of each layer; Step S14: performing chip three-dimensional shape and size analysis on the chip production log to generate chip three-dimensional shape and size features; Step S15: Perform global multi-level structural simulation on the three-dimensional shape and size characteristics of the chip and the functional layer structure of each layer to construct an initial multi-layer packaging three-dimensional model.

3. The multi-layer structure integration method of microfluidic chip packaging according to claim 1, characterized in that: The specific steps of step S2 are: Step S21: Identify the packaging materials layer by layer in the chip production log and extract the packaging material information of each layer; Step S22: mining the packaging material attributes of each layer of packaging material information to generate packaging material attributes of each layer; Step S23: performing layer-by-layer microfluidic channel analysis on the initial multi-layer packaging three-dimensional model to extract the microfluidic channels of each layer; Step S24: performing multi-level packaging simulation on the microfluidic channels of each layer according to the packaging material properties of each layer to obtain multi-level packaging simulation data.

4. The multi-layer structure integration method of microfluidic chip packaging according to claim 1, characterized in that: The specific steps of step S3 are: Step S31: performing dynamic flow field simulation behavior analysis on multi-level packaging simulation data to extract dynamic flow field simulation behavior features; Step S32: performing micro-flow field stress evolution on the dynamic flow field simulation behavior characteristics, thereby generating micro-flow field stress evolution laws; Step S33: performing multi-layer structure package association mining according to the micro-flow field stress evolution law, thereby extracting multi-layer structure package association features; Step S34: performing interlayer stress mutual influence analysis on the initial multilayer package three-dimensional model according to the multilayer structure package association characteristics, thereby obtaining the interlayer stress mutual influence relationship.

5. The multi-layer structure integration method of microfluidic chip packaging according to claim 4, characterized in that: The specific steps of step S32 are: Analyze the velocity distribution of dynamic flow field simulation behavior characteristics to obtain velocity distribution data of different structures; Calculate the transient fluid pressure based on the dynamic flow field simulation behavior characteristics and extract the transient packaging fluid pressure of each layer; Identify the packaging pressure change of each layer's transient packaging fluid pressure and extract the packaging pressure change characteristics of each layer; Predicting potential turbulence phenomena based on multi-level packaging simulation data to obtain potential turbulence phenomena; Conduct turbulence intensity estimation on potential turbulence phenomena to generate potential turbulence intensity; The micro-flow field stress evolution is carried out according to the velocity distribution data of different structures, the packaging pressure change characteristics of each layer and the potential turbulence intensity, thereby generating the micro-flow field stress evolution law.

6. The multi-layer structure integration method of microfluidic chip packaging according to claim 1, characterized in that: The specific steps of step S4 are: Step S41: calculating the thermal conductivity difference of the materials according to the properties of the packaging materials of each layer, and obtaining the thermal conductivity difference values ​​of packaging materials with different structures; Step S42: performing layer-by-layer structure heat conduction analysis on the multi-level packaging simulation data to extract the heat conduction characteristics of each layer; Step S43: performing multi-layer thermal stress analysis on the heat conduction characteristics of each layer to generate multi-layer thermal stress data; Step S44: performing temperature gradient analysis on the multi-level packaging simulation data one by one to obtain a temperature gradient curve of each layer structure; Step S45: Evolving the local thermal expansion effect according to the temperature gradient curve of each layer structure and the multi-layer thermal stress data to obtain the local thermal expansion effect of each layer; Step S46: performing multi-layer thermal strain evolution on the local thermal expansion effect of each layer based on the thermal conductivity difference value of packaging materials with different structures, thereby generating thermal strain evolution characteristics of different structural layers.

7. The multi-layer structure integration method of microfluidic chip packaging according to claim 1, characterized in that: The specific steps of step S5 are: Step S51: performing multi-level stress fatigue analysis based on the inter-layer stress mutual influence relationship and the thermal strain evolution characteristics of different structural layers, and extracting multi-level stress fatigue data; Step S52: extracting stress distribution and strain energy density of multi-layer contact surfaces according to multi-level stress fatigue data; Step S53: performing interlayer contact fatigue analysis on the stress distribution and strain energy density to generate interlayer contact fatigue characteristics; Step S54: analyzing the internal stress release mechanism of the multi-level stress fatigue data to generate internal stress release mechanism characteristics; Step S55: predicting the propagation of local stress failure cracks according to the characteristics of the internal stress release mechanism, thereby obtaining the propagation path of the local failure cracks; Step S56: Perform multi-level fatigue evolution reconstruction on the local failure crack propagation path according to the interlayer contact fatigue characteristics to construct a multi-level fatigue evolution trajectory.

8. The multi-layer structure integration method of microfluidic chip packaging according to claim 1, characterized in that: The specific steps of step S6 are: Step S61: optimizing the interlayer contact mode according to the multi-level fatigue evolution trajectory to generate an optimized multi-level contact mode; Step S62: Screening packaging materials for the multi-level fatigue evolution trajectory to obtain packaging material screening information; Step S63: adjusting the microfluidic channel size of the initial multi-layer packaging three-dimensional model according to the multi-level fatigue evolution trajectory to obtain the microfluidic channel adjustment size; Step S64: Optimizing global packaging parameters according to the optimized multi-level contact mode, packaging material screening information and microfluidic channel adjustment size, and constructing global packaging optimization parameters; Step S65: performing three-dimensional model iterative calculation on the initial multi-layer packaging three-dimensional model based on the global packaging optimization parameters, thereby constructing a final optimized three-dimensional chip packaging model.

9. A multi-layer structure integrated system for microfluidic chip packaging, characterized in that: A multi-layer structure integration method for executing the microfluidic chip packaging as claimed in claim 1, comprising: The multi-layer packaging simulation module is used to obtain chip production logs, perform multi-functional layer structure definition and global multi-level structure simulation, and build an initial multi-layer packaging 3D model; The packaging simulation module is used to identify the packaging materials layer by layer in the chip production log and perform multi-level packaging simulation on the initial multi-layer packaging three-dimensional model to obtain multi-level packaging simulation data; The flow field simulation analysis module is used to perform dynamic flow field simulation behavior analysis on multi-layer packaging simulation data and to analyze the mutual influence of interlayer stresses, thereby obtaining the mutual influence relationship of interlayer stresses; Thermal strain evolution module, which is used to perform local thermal expansion effect evolution and multi-layer thermal strain evolution based on multi-layer packaging simulation data, thereby generating thermal strain evolution characteristics of different structural layers; Fatigue evolution reconstruction module, which is used to perform multi-level stress fatigue analysis based on the mutual influence relationship between inter-layer stresses and the thermal strain evolution characteristics of different structural layers, and to reconstruct multi-level fatigue evolution and construct multi-level fatigue evolution trajectories; The packaging parameter optimization module is used to optimize the global packaging parameters according to the multi-level fatigue evolution trajectory, and perform three-dimensional model iterative calculation on the initial multi-layer packaging three-dimensional model to construct the final optimized three-dimensional chip packaging model.

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