Underfill thermal fatigue analysis system based on simulation

Abstract

The present invention discloses a bottom filler thermal fatigue analysis system based on simulation, which relates to the field of chip processing technology and includes a data acquisition unit, a simulation modeling unit, a thermal cycle loading unit, a damage calculation unit, and a result output unit. The present invention is used to obtain basic data and thermal fatigue influencing factors of the bottom filler and thermal fatigue damage judgment data, and constructs a bottom filler thermal fatigue analysis model based on the simulation principle to analyze the relationship between the bottom filler thermal fatigue influencing factors and the degree of thermal fatigue damage. The residual stress distribution during the thermal cycle is calculated based on the thermal-mechanical coupling algorithm, and the high stress gradient area is locally refined and analyzed using sub-model technology to obtain thermal fatigue feedback data. The cumulative damage theory is also used to predict the fatigue life of the three-dimensional finite element model after thermal cycle loading, and a stress-strain curve and a damage evolution cloud map are generated to display the thermal fatigue analysis results in a visual form.

Description

Technical Field

[0001] The present invention relates to the technical field of chip processing, and in particular to a bottom filling glue thermal fatigue analysis system based on simulation. Background Art

[0002] Underfill is a liquid sealant applied between the chip and substrate. After curing, it exhibits high modulus, a suitable thermal expansion coefficient, low moisture absorption, and excellent adhesion to the chip and substrate. Typically, underfills are based on epoxy resin, with silica fillers and various additives added for optimal performance. Underfill not only extends the fatigue life of solder joints but also protects the chip and solder joints.

[0003] Thermal fatigue analysis of bottom filler helps to study the service life of chips, but commonly used thermal fatigue assessment methods mainly rely on finite element analysis simulations. These methods are usually based on simplified temperature cycling conditions. However, temperature changes in actual working environments are often non-uniform and complex, and simplified simulations cannot accurately reflect the actual temperature field distribution, resulting in a large deviation between the assessment results and the actual situation. In addition, the traditional Coffin-Manson model only considers the effects of strain range and number of cycles on fatigue life, ignoring the coupling effects of factors such as temperature and time. Especially in high temperature environments, this coupling effect will significantly affect the fatigue performance of the material, resulting in inaccurate life prediction results.

[0004] In view of the above technical defects, a solution is now proposed. Summary of the Invention

[0005] The purpose of the present invention is to obtain the basic data of the bottom filling glue, the thermal fatigue influencing factor and the thermal fatigue damage judgment data, and construct a bottom filling glue thermal fatigue analysis model based on the simulation principle to analyze the relationship between the thermal fatigue influencing factor of the bottom filling glue and the degree of thermal fatigue damage. The residual stress distribution during the thermal cycle is calculated based on the thermal-mechanical coupling algorithm, and the high stress gradient area is locally refined and analyzed through the sub-model technology to obtain thermal fatigue feedback data. The cumulative damage theory is used to predict the fatigue life of the three-dimensional finite element model after thermal cycle loading, and the stress-strain curve and damage evolution cloud map are generated to display the thermal fatigue analysis results in a visual form. By innovatively introducing dynamic constitutive models and sub-model technology, the analysis accuracy of high stress areas is significantly improved, providing quantitative decision support for electronic packaging design.

[0006] In order to achieve the above-mentioned object, the present invention adopts the following technical solution: a simulation-based underfill thermal fatigue analysis system, comprising a data acquisition unit, a simulation modeling unit, a thermal cycle loading unit, a damage calculation unit, and a result output unit;

[0007] The data acquisition unit is used to acquire basic data of the bottom filler and thermal fatigue influencing factors and thermal fatigue damage judgment data, wherein the basic data includes physical parameters and geometric structure data, and the thermal fatigue influencing factors include thermal cycle working conditions;

[0008] The simulation modeling unit is used to obtain basic data of the underfill as an input database, obtain thermal fatigue damage judgment data, and build an underfill thermal fatigue analysis model based on the simulation principle to analyze the relationship between the underfill thermal fatigue influencing factor and the thermal fatigue damage degree;

[0009] The thermal cycle loading unit includes a temperature control module and a thermal fatigue analysis module. The temperature control module is used to define the thermal cycle working conditions. The thermal fatigue analysis module is used to calculate the residual stress distribution during the thermal cycle based on the thermal-mechanical coupling algorithm. It also uses sub-model technology to perform local refinement analysis on high stress gradient areas to obtain thermal fatigue feedback data and send it to the damage calculation module.

[0010] Damage calculation unit, used to obtain thermal fatigue feedback data and use cumulative damage theory to predict fatigue life of the three-dimensional finite element model after thermal cycle loading, generating stress-strain curves and damage evolution cloud maps;

[0011] The result output unit is used to obtain stress-strain curves and damage evolution cloud maps and display the thermal fatigue analysis results in a visual form, and output an evaluation report including fatigue life threshold, crack initiation location and propagation path.

[0012] Furthermore, the physical parameters include dynamic data of the glass transition temperature, thermal expansion coefficient, and elastic modulus of the bottom filler changing with temperature, as well as the bonding strength parameters of the bottom filler and the chip and substrate interface;

[0013] The geometric structure data includes thickness data, length data and width data of the bottom filling glue and size data of the chip and the substrate.

[0014] Furthermore, the specific process of constructing the bottom filler thermal fatigue analysis model is as follows:

[0015] S101, obtaining geometric structure data through reverse engineering scanning, performing mesh encryption processing on the solder ball array and underfill overflow height data, and importing the physical parameters and geometric structure data into a three-dimensional software to construct a three-dimensional finite element model of the underfill;

[0016] S102, exposing the underfill to a periodic high and low temperature alternating environment, and applying thermal cycle conditions, i.e., thermal fatigue influencing factors, through a temperature control module. The thermal cycle conditions include a temperature range, a heating and cooling rate, and a number of cycles, to simulate the temperature changes experienced by the underfill in actual applications;

[0017] S103 , obtaining thermal fatigue damage judgment data from a database, wherein the thermal fatigue damage judgment data includes a stress threshold of an intermetallic compound layer at an edge of a solder joint and a crack length threshold extending along a solder grain boundary.

[0018] Furthermore, the specific process of obtaining thermal fatigue feedback data is as follows:

[0019] S201. Based on the thermal fatigue damage judgment data in S103, calculate the heat conduction energy value Q during the thermal cycle according to the following formula: , where ρ is the density of the underfill, c is the specific heat capacity, k is the thermal conductivity, T is the thermal cycle temperature difference, and t is the thermal cycle time;

[0020] S202. Calculate the momentum equation according to the following formula: , where σ is the stress tensor and f is the body load;

[0021] Then calculate the material response equation: , C is the elastic matrix, ε is the deformation effect value;

[0022] S203, obtaining a three-dimensional finite element model of the underfill, obtaining the temperature field and residual stress distribution, marking stress analysis nodes in the three-dimensional finite element model, dividing the stress analysis region with the stress analysis node as the center and R as the radius, and deriving a geometric model of the stress analysis region;

[0023] S204. Generate a submodel using the SUBSTRUCT command, apply boundary conditions using the SBCT command, define the mesh density of the submodel, cut the stress analysis area, refine the mesh to 0.3 mm, and apply the displacement boundary of the global model;

[0024] S205. Compare the results of the global model and the sub-model to verify the interpolation accuracy. After the interpolation accuracy is achieved, output the stress calculation results, i.e., thermal fatigue feedback data.

[0025] Furthermore, the specific process of fatigue life prediction is as follows:

[0026] S301, obtaining thermal fatigue feedback data, wherein the obtained thermal fatigue feedback data includes stress data of the intermetallic compound layer at the edge of the solder joint and the length of the crack extending along the solder grain boundary, and obtaining time history data of the output key nodes, wherein the time history data includes stress σ(t), strain ε(t) and temperature T(t);

[0027] S302. Apply mechanical loads under thermal cycling conditions, obtain stress-life curves, and calculate the cumulative damage D based on the following formula: , where n iis the number of cycles of the stress amplitude at level i, N i,s is the fatigue life under the corresponding amplitude;

[0028] S303, and then use the stress-life model to calculate the stress amplitude σn: ,in is the fatigue strength coefficient, b is the fatigue strength index;

[0029] S304. Using the improved Coffin-Manson model, combined with the relationship between the plastic strain energy density of the bottom filler and the crack growth rate, fatigue life prediction under multi-axial stress state is achieved, and stress-strain curves and damage evolution cloud maps are generated.

[0030] Furthermore, the result output unit also includes an automatic calibration module, which compares the thermal fatigue feedback data with the accelerated life test data. When the error between the simulation result and the accelerated life test data exceeds a preset threshold, the iterative optimization process of the simulation parameters is triggered.

[0031] In summary, due to the adoption of the above technical solution, the beneficial effects of the present invention are:

[0032] This simulation-based bottom filler thermal fatigue analysis system is used to obtain basic data, thermal fatigue influencing factors, and thermal fatigue damage judgment data of the bottom filler. It then constructs a bottom filler thermal fatigue analysis model based on the simulation principle to analyze the relationship between the bottom filler thermal fatigue influencing factors and the degree of thermal fatigue damage. The residual stress distribution during the thermal cycle is calculated based on the thermal-mechanical coupling algorithm, and the high stress gradient area is locally refined through sub-model technology to obtain thermal fatigue feedback data. The cumulative damage theory is used to predict the fatigue life of the three-dimensional finite element model after thermal cycle loading, generate stress-strain curves and damage evolution cloud maps, and display the thermal fatigue analysis results in a visual form. By innovatively introducing dynamic constitutive models and sub-model technology, the analysis accuracy of high stress areas is significantly improved, providing quantitative decision support for electronic packaging design. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] Figure 1 Shown is a schematic diagram of the overall structure of the present invention. DETAILED DESCRIPTION

[0034] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0035] Example:

[0036] like Figure 1 As shown, the bottom filler thermal fatigue analysis system based on simulation includes a data acquisition unit, a simulation modeling unit, a thermal cycle loading unit, a damage calculation unit and a result output unit;

[0037] The data acquisition unit is used to obtain basic data of the bottom filler, thermal fatigue influencing factors, and thermal fatigue damage judgment data. The basic data includes physical parameters and geometric structure data. The thermal fatigue influencing factors include thermal cycle working conditions.

[0038] Physical parameters include the dynamic data of the glass transition temperature TG, thermal expansion coefficient, elastic modulus of the underfill as a function of temperature, and the bonding strength parameters of the underfill with the chip and substrate interfaces;

[0039] Taking the chip based on FCCSP packaging as an example, its specific physical parameters are shown in the following table:

[0040]

[0041] The geometric structure data includes the thickness data, length data and width data of the bottom filling glue and the size data of the chip and the substrate.

[0042] The simulation modeling unit is used to obtain basic data of the underfill as an input database, obtain thermal fatigue damage judgment data, and build an underfill thermal fatigue analysis model based on the simulation principle to analyze the relationship between the underfill thermal fatigue influencing factor and the thermal fatigue damage degree;

[0043] The specific process of building the underfill thermal fatigue analysis model is as follows:

[0044] S101, obtaining geometric structure data through reverse engineering scanning, performing mesh encryption processing on the solder ball array and underfill overflow height data, and importing the physical parameters and geometric structure data into 3D software to construct a 3D finite element model of the underfill;

[0045] S102, exposing the underfill to a periodic high and low temperature alternating environment, and applying thermal cycle conditions, i.e., thermal fatigue influencing factors, through a temperature control module. The thermal cycle conditions include temperature range, heating and cooling rates, and number of cycles, to simulate the temperature changes experienced by the underfill in actual applications;

[0046] S103. Acquire thermal fatigue damage judgment data from a database, where the thermal fatigue damage judgment data includes a stress threshold of an intermetallic compound layer at an edge of a solder joint and a crack length threshold extending along a solder grain boundary.

[0047] The thermal cycle loading unit includes a temperature control module and a thermal fatigue analysis module. The temperature control module is used to define the thermal cycle working conditions. The thermal fatigue analysis module is used to calculate the residual stress distribution during the thermal cycle based on the thermal-mechanical coupling algorithm. It also uses sub-model technology to perform local refinement analysis on high stress gradient areas to obtain thermal fatigue feedback data and send it to the damage calculation module.

[0048] The specific process of obtaining thermal fatigue feedback data is as follows:

[0049] S201. Based on the thermal fatigue damage judgment data in S103, calculate the heat conduction energy value Q during the thermal cycle according to the following formula: , where ρ is the density of the underfill, c is the specific heat capacity, k is the thermal conductivity, T is the thermal cycle temperature difference, and t is the thermal cycle time;

[0050] S202. Calculate the momentum equation according to the following formula: , where σ is the stress tensor and f is the body load;

[0051] Then calculate the material response equation: , C is the elastic matrix, ε is the deformation effect value;

[0052] S203, obtaining a three-dimensional finite element model of the underfill, obtaining the temperature field and residual stress distribution, marking stress analysis nodes in the three-dimensional finite element model, dividing the stress analysis region with the stress analysis node as the center and R as the radius, and deriving a geometric model of the stress analysis region;

[0053] S204. Generate a submodel using the SUBSTRUCT command, apply boundary conditions using the SBCT command, define the mesh density of the submodel, cut the stress analysis area, refine the mesh to 0.3 mm, and apply the displacement boundary of the global model;

[0054] S205. Compare the results of the global model and the sub-model to verify the interpolation accuracy. After the interpolation accuracy is achieved, output the stress calculation results, i.e., thermal fatigue feedback data.

[0055] Damage calculation unit, used to obtain thermal fatigue feedback data and use cumulative damage theory to predict fatigue life of the three-dimensional finite element model after thermal cycle loading, generating stress-strain curves and damage evolution cloud maps;

[0056] The specific process of fatigue life prediction is as follows:

[0057] S301, obtaining thermal fatigue feedback data, including stress data of the intermetallic compound layer at the edge of the solder joint and the length of the crack extending along the solder grain boundary, and obtaining time history data of key output nodes, including stress σ(t), strain ε(t), and temperature T(t);

[0058] S302. Apply mechanical loads under thermal cycling conditions, obtain stress-life curves, and calculate the cumulative damage D based on the following formula: , where n i is the number of cycles of the stress amplitude at level i, N i,s is the fatigue life under the corresponding amplitude;

[0059] S303, and then use the stress-life model to calculate the stress amplitude σn: ,in is the fatigue strength coefficient, b is the fatigue strength index;

[0060] S304. Using the improved Coffin-Manson model, combined with the relationship between the plastic strain energy density of the bottom filler and the crack growth rate, fatigue life prediction under multi-axial stress state is achieved, and stress-strain curves and damage evolution cloud maps are generated.

[0061] The result output unit is used to obtain stress-strain curves and damage evolution cloud maps and display the thermal fatigue analysis results in a visual form, and output an evaluation report including fatigue life threshold, crack initiation location and propagation path.

[0062] The result output unit also includes an automatic calibration module that compares the thermal fatigue feedback data with the accelerated life test data. When the error between the simulation result and the accelerated life test data exceeds a preset threshold, the iterative optimization process of the simulation parameters is triggered.

[0063] The present invention is used to obtain the basic data of the bottom filling glue, the thermal fatigue influencing factor and the thermal fatigue damage judgment data, and constructs a bottom filling glue thermal fatigue analysis model based on the simulation principle to analyze the relationship between the thermal fatigue influencing factor of the bottom filling glue and the degree of thermal fatigue damage. The residual stress distribution during the thermal cycle is calculated based on the thermal-mechanical coupling algorithm, and the high stress gradient area is locally refined and analyzed through the sub-model technology to obtain thermal fatigue feedback data. The cumulative damage theory is used to predict the fatigue life of the three-dimensional finite element model after thermal cycle loading, and the stress-strain curve and damage evolution cloud map are generated to display the thermal fatigue analysis results in a visual form. By innovatively introducing dynamic constitutive models and sub-model technology, the analysis accuracy of high stress areas is significantly improved, providing quantitative decision support for electronic packaging design.

[0064] The size of the interval and threshold is set to facilitate comparison. The size of the threshold depends on the amount of sample data and the number of bases set by technical personnel in this field for each set of sample data; as long as it does not affect the proportional relationship between the parameter and the quantized value.

[0065] The above formulas are all dimensionless and numerical calculations. The formulas are obtained by collecting a large amount of data and performing software simulation to obtain the most recent real situation. The preset parameters in the formulas are set by those skilled in the art according to actual conditions.

[0066] The above description is only a preferred specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any technician familiar with the technical field, within the technical scope disclosed by the present invention, who makes equivalent replacements or changes based on the technical solution and inventive concept of the present invention, should be covered by the scope of protection of the present invention.

Claims

1. The bottom filler thermal fatigue analysis system based on simulation is characterized by: It includes data acquisition unit, simulation modeling unit, thermal cycle loading unit, damage calculation unit and result output unit; The data acquisition unit is used to acquire basic data of the bottom filler and thermal fatigue influencing factors and thermal fatigue damage judgment data, wherein the basic data includes physical parameters and geometric structure data, and the thermal fatigue influencing factors include thermal cycle working conditions; The physical parameters include the dynamic data of the glass transition temperature, thermal expansion coefficient, and elastic modulus of the bottom filler as a function of temperature, as well as the bonding strength parameters of the bottom filler and the chip and substrate interface; The geometric structure data includes thickness data, length data and width data of the bottom filling glue and size data of the chip and the substrate; The simulation modeling unit is used to obtain basic data of the underfill as an input database, obtain thermal fatigue damage judgment data, and build an underfill thermal fatigue analysis model based on the simulation principle to analyze the relationship between the underfill thermal fatigue influencing factor and the thermal fatigue damage degree; The thermal fatigue damage judgment data includes the stress threshold of the intermetallic compound layer at the edge of the solder joint and the crack length threshold extending along the solder grain boundary; The thermal cycle loading unit includes a temperature control module and a thermal fatigue analysis module. The temperature control module is used to define the thermal cycle working conditions. The thermal fatigue analysis module is used to calculate the residual stress distribution during the thermal cycle based on the thermal-mechanical coupling algorithm. It also uses sub-model technology to perform local refinement analysis on high stress gradient areas to obtain thermal fatigue feedback data and send it to the damage calculation module. The specific process of obtaining thermal fatigue feedback data is as follows: S201. Based on the thermal fatigue damage judgment data, calculate the heat conduction energy value Q during the thermal cycle according to the following formula: , where ρ is the density of the underfill, c is the specific heat capacity, k is the thermal conductivity, T is the thermal cycle temperature difference, and t is the thermal cycle time; S202. Calculate the momentum equation according to the following formula: , where σ is the stress tensor and f is the body load; Then calculate the material response equation: , C is the elastic matrix, ε is the deformation effect value; S203, obtaining a three-dimensional finite element model of the underfill, obtaining the temperature field and residual stress distribution, marking stress analysis nodes in the three-dimensional finite element model, dividing the stress analysis region with the stress analysis node as the center and R as the radius, and deriving a geometric model of the stress analysis region; S204. Generate a submodel using the SUBSTRUCT command, apply boundary conditions using the SBCT command, define the mesh density of the submodel, cut the stress analysis area, refine the mesh to 0.3 mm, and apply the displacement boundary of the global model; S205, comparing the global model and the sub-model results to verify the interpolation accuracy, and outputting the stress calculation results, i.e., thermal fatigue feedback data, after the interpolation accuracy is achieved; The thermal fatigue feedback data includes stress data of the intermetallic compound layer at the edge of the solder joint and the length of the crack extending along the solder grain boundary; Damage calculation unit, used to obtain thermal fatigue feedback data and use cumulative damage theory to predict fatigue life of the three-dimensional finite element model after thermal cycle loading, generating stress-strain curves and damage evolution cloud maps; The result output unit is used to obtain stress-strain curves and damage evolution cloud maps and display the thermal fatigue analysis results in a visual form, and output an evaluation report including fatigue life threshold, crack initiation location and propagation path.

2. The simulation-based underfill thermal fatigue analysis system according to claim 1, characterized in that: The specific process of building the underfill thermal fatigue analysis model is as follows: S101, obtaining geometric structure data through reverse engineering scanning, performing mesh encryption processing on the solder ball array and underfill overflow height data, and importing the physical parameters and geometric structure data into a three-dimensional software to construct a three-dimensional finite element model of the underfill; S102, exposing the underfill to a periodic high and low temperature alternating environment, and applying thermal cycle conditions, i.e., thermal fatigue influencing factors, through a temperature control module. The thermal cycle conditions include a temperature range, a heating and cooling rate, and a number of cycles, to simulate the temperature changes experienced by the underfill in actual applications; S103. Obtain thermal fatigue damage judgment data from a database.

3. The simulation-based underfill thermal fatigue analysis system according to claim 1, characterized in that: The specific process of fatigue life prediction is as follows: S301, obtaining thermal fatigue feedback data, and obtaining time history data of output key nodes, wherein the time history data includes stress σ(t), strain ε(t), and temperature T(t); S302. Apply mechanical loads under thermal cycling conditions, obtain stress-life curves, and calculate the cumulative damage D based on the following formula: , where n i is the number of cycles of the stress amplitude at level i, N i,s is the fatigue life under the corresponding amplitude; S303, and then use the stress-life model to calculate the stress amplitude σn: ,in is the fatigue strength coefficient, b is the fatigue strength index; S304. Using the improved Coffin-Manson model, combined with the relationship between the plastic strain energy density of the bottom filler and the crack growth rate, fatigue life prediction under multi-axial stress state is achieved, and stress-strain curves and damage evolution cloud maps are generated.

4. The simulation-based underfill thermal fatigue analysis system according to claim 1, characterized in that: The result output unit also includes an automatic calibration module that compares the thermal fatigue feedback data with the accelerated life test data. When the error between the simulation result and the accelerated life test data exceeds a preset threshold, the iterative optimization process of the simulation parameters is triggered.

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