CN113536489A - Method for determining connection configuration and process parameters of component packaging - Google Patents
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Abstract
The invention provides a method for determining the connection configuration and process parameters of component packaging, which comprises the following steps: acquiring sample data of connection configuration and process parameters; and B: taking the process parameters as input and the connection configuration as output, and training a neural network model; and C: obtaining the relation between the connection configuration and the reliability of the welding spot through simulation analysis; step D: and establishing a process parameter-connection configuration-reliability inversion equation, and adjusting the process parameters based on a particle swarm algorithm until the calculation result meets the reliability requirement. The invention has the advantages that: the connection configuration is used as an intermediate parameter of the process parameter and the welding reliability, the relation between the process parameter and the connection reliability is determined through two times of mapping, an inverse equation is established, the parameters are updated through a particle swarm algorithm, the welding spot reliability under different parameters is calculated, the connection configuration and the process parameter can be obtained according to the welding spot reliability requirement without real welding verification, and the test cost and time are reduced.
Description
Technical Field
The invention relates to the technical field of electronic component packaging, in particular to a method for determining connection configuration and process parameters of component packaging.
Background
With the continuous advance of electronic products toward miniaturization, light weight and multifunction, the packaging density of microsystems is higher and higher, the number of devices in a unit area is increased, the welding spots of the interconnection structure are smaller and smaller, the mechanical, electrical and thermal loads borne by the interconnection structure are heavier and heavier, and the requirement on the reliability of the interconnection interface is increased day by day. According to Rohm aviation data, T/R assembly failure caused by connection process factors is up to one third, and the research on the reliability of the microwave assembly from the connection process perspective has important significance. However, in the current connection process research, due process parameters are usually determined by experience in the design and manufacturing stage of component package connection, actual measurement of the performance of the solder joint is performed after a sample is manufactured in a trial mode, if the performance of the solder joint does not reach the standard, the process parameters need to be changed in the design stage again, and the expected performance index of the solder joint can be reached through continuous trial and error, so that the product development period is long, and the investment cost is high. The evolution mechanism of the interface structure of the connecting welding spot can be rarely analyzed from the data mining angle to optimize the component packaging process and improve the stability of the process; and the influence of the microwave assembly welding spot connection configuration on the connection reliability life is rarely analyzed under the composite action of data mining and optimization algorithms.
The invention patent application with publication number CN104239645A discloses a design method and system for vibration-resistant reliability of micro-assembly components, which is based on a simulation model to analyze the relationship between the weld width and the natural frequency, obtain the weld critical width corresponding to the critical frequency, and determine the key process parameters of the weld with the weld critical width as the criterion, thereby solving the cost problem of the traditional method of welding first, testing later and adjusting the parameters, but only considering the weld width and then determining the welding process parameters, but not fully considering the chain structure and the influence of different process combinations on the welding reliability.
Disclosure of Invention
The invention aims to provide a method for determining welding connection configuration and process parameters aiming at assembly connection.
The invention solves the technical problems through the following technical scheme: a method for determining the connection configuration and process parameters of a component package comprises,
step A: acquiring sample data of connection configuration and process parameters;
and B: taking the process parameters as input and the connection configuration as output, and training a neural network model;
and C: obtaining the relation between the connection configuration and the reliability of the welding spot through simulation analysis;
step D: and establishing a process parameter-connection configuration-reliability inversion equation, and adjusting the process parameters based on a particle swarm algorithm until the calculation result meets the reliability requirement.
The method takes the connection configuration as an intermediate parameter of the process parameter and the welding reliability, determines the relation between the process parameter and the connection reliability through two times of mapping, establishes an inverse equation, updates the parameter through a particle swarm algorithm and calculates the welding spot reliability under different parameters, realizes the purpose of determining the welding process parameter based on the reliability requirement, thereby obtaining the connection configuration and the process parameter according to the welding spot reliability requirement without real welding verification, and reduces the test cost and time.
Preferably, the method for acquiring the sample data of the connection configuration and the process parameters comprises,
step i: determining geometric parameters and physical parameters of the connection configuration;
step ii: determining key technological parameters of a welding process;
step iii: and designing an experiment, namely obtaining the numerical values of key process parameters in the equidistant design welding process, carrying out the welding experiment, and obtaining the connection configuration corresponding to each group of process parameters.
Preferably, the geometric parameters of the connection configuration comprise welding spot climbing height, welding spot extending width and welding spot thickness; the physical parameters include material type, material density, elastic modulus, and temperature characteristics.
Preferably, the key process parameters of the welding process comprise peak temperature, cooling rate and thickness of the soldering paste in welding.
Preferably, the method further comprises the step of designing a simulation experiment based on the experimental data in the step iii, simulating the welding spot connection configuration under different process parameters, and expanding sample data.
Preferably, in the step B, sample data is preprocessed before training, the sample data is cleaned, interpolated and expanded, the preprocessed sample data is randomly divided, a training set and a test set are constructed, the peak temperature, the cooling rate and the solder paste thickness in the process parameters are used as the input of the neural network model, and the solder joint climbing height, the solder joint extending width and the solder joint thickness are used as the output of the neural network model.
Preferably, the method for obtaining the relationship between the connection configuration and the reliability of the solder joint through simulation analysis includes the steps of establishing a corresponding finite element model according to the geometric dimensions of the substrate and the components, setting physical parameters, referring to the load borne by the substrate in the actual environment during working, applying a corresponding boundary condition and a corresponding load, evaluating the fatigue life of the solder joint under temperature impact based on an Engelmaier-Wild solder joint failure model, determining the association relationship between the connection configuration and the stress-strain distribution of the solder joint, and establishing a connection configuration and reliability relationship network.
Preferably, the process parameter-connection configuration-reliability inversion equation comprises,
inversion model of process parameters:
Find X
s.t.min{x1is less than or equal to1≤max{x1}
min{x2Is less than or equal to2≤max{x2}
…
min{xnIs less than or equal ton≤max{xn}
Connecting the configuration inversion models:
Find Y
s.t.min{y1y is inverted1≤max{y1}
min{y2Y is inverted2≤max{y2}
…
min{ynY is invertedn≤max{yn}
Wherein X is a process parameter set, Y is a connection configuration set, Z is a welding spot life, and XiIs the ith process parameter, yiFor the ith connection configuration parameter, s.t. represents the constraint and RMSE represents the root mean square value.
Preferably, the method for adjusting the connection configuration based on the particle swarm optimization algorithm comprises the following steps,
step 1: initializing each parameter of the algorithm, setting maximum iteration times, independent variable quantity, maximum speed of ions, inertial weight, position information, self and group learning factors, randomly initializing speed and position in a speed interval and a search space, setting particle swarm size, initializing a random flight speed for each ion, and setting a target value and constraint conditions;
step 2: randomly generating a plurality of groups of connection configuration parameter combinations, and determining the stress strain of the welding spot based on the relation between the connection configuration and the reliability of the welding spot to obtain a global optimal solution;
and step 3: judging whether the global optimal solution meets a target value or reaches the maximum iteration times, if so, outputting the global optimal solution and the corresponding connection configuration parameters, if not, updating the structural parameters and the corresponding particle speed, returning to the step 2, and in the subsequent iteration process, comparing the updated optimal solution with the global optimal solution to update the global optimal solution;
after the connection configuration is determined, the process parameters are determined based on the same method as above.
Preferably, the optimization target of the particle swarm optimization is the maximum stress and the maximum strain value of the dangerous welding spot, and the constraint condition is the interval range of each parameter; the formula for updating the speed and position is:
Vid＝ωVid+C1random(0,1)(Pid-Xid)+C2random(0,1)(Pgd-Xid)
Xid＝Xid+Vid
wherein, omega is more than or equal to 0 and is an inertia factor, the larger the numerical value is, the stronger the global optimization capability is, and the smaller the numerical value is, the stronger the local optimization capability is; c1And C2The former is an individual learning factor of each ion, and the latter is a social learning factor of each particle, as an acceleration constant; random (0,1) indicates the interval [0,1 ]]Random number of inner, PidD-dimension, P, representing individual extrema of i-th variablegdD-dimension, V, representing a global optimal solutionidD-dimension, X, representing the particle population variation speed of the ith variableidThe d-th dimension representing the particle space value of the i-th variable.
The connection configuration and the process parameter determination method of the component package have the advantages that: the connection configuration is used as an intermediate parameter of the process parameter and the welding reliability, the relation between the process parameter and the connection reliability is determined through two times of mapping, an inverse equation is established, the parameters are updated through a particle swarm algorithm, the reliability of welding spots under different parameters is calculated, the purpose of determining the welding process parameter based on the reliability requirement is achieved, the connection configuration and the process parameter can be obtained according to the requirement of the reliability of the welding spots without real welding verification, and the test cost and time are reduced.
Drawings
Fig. 1 is a flow chart of a method for determining a connection configuration and process parameters of a component package according to an embodiment of the present invention;
fig. 2 is a schematic diagram of a connection configuration of a component package and a connection configuration of a process parameter determination method according to an embodiment of the present invention.
Detailed Description
To make the objects, technical solutions and advantages of the present invention more apparent, the technical solutions of the present invention are described below in detail and completely with reference to the accompanying drawings, and it is apparent that the described embodiments are some, but not all embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
As shown in FIG. 1, the present embodiment provides a method for determining the connection configuration and process parameters of a package, comprising
Step A: acquiring sample data of connection configuration and process parameters;
and B: taking the process parameters as input and the connection configuration as output, and training a neural network model;
and C: obtaining the relation between the connection configuration and the reliability of the welding spot through simulation analysis;
step D: and establishing a process parameter-connection configuration-reliability inversion equation, and adjusting the process parameters based on a particle swarm algorithm until the calculation result meets the reliability requirement.
In the embodiment, the connection configuration is used as an intermediate parameter of the process parameter and the welding reliability, the relationship between the process parameter and the connection reliability is determined through two times of mapping, an inverse equation is established, the parameters are updated through a particle swarm algorithm, the reliability of welding spots under different parameters is calculated, the purpose of determining the welding process parameter based on the reliability requirement is realized, the connection configuration and the process parameter can be obtained according to the welding spot reliability requirement without real welding verification, and the test cost and time are reduced.
Specifically, the method for determining the connection configuration and the process parameters of the component package provided by the embodiment includes,
step A: acquiring sample data of connection configuration and process parameters;
the method for acquiring the sample data of the connection configuration and the process parameters comprises the following steps,
step i: determining geometric parameters and physical parameters of the connection configuration;
referring to fig. 2, the geometric parameters include a solder joint creepage height H, a solder joint extension degree L, and a solder joint thickness T; the physical parameters comprise material types, material density, elastic modulus, temperature characteristics and the like, wherein the temperature characteristics are characteristic parameters of the material changing along with temperature.
Step ii: determining key technological parameters of a welding process;
the key process parameters of the welding process comprise peak temperature, cooling rate and thickness of soldering paste in welding.
Step iii: and designing an experiment, namely obtaining the numerical values of key process parameters in the equidistant design welding process, carrying out the welding experiment, and obtaining the connection configuration corresponding to each group of process parameters.
And further, designing a simulation experiment according to the experimental data in the step iii, simulating the connection configuration of the welding spot after melting, solidifying and forming under different process parameters, and verifying and correcting by referring to the experimental data so as to expand the sample data.
And B: taking the process parameters as input and the connection configuration as output, and training a neural network model;
preprocessing sample data before training, including cleaning, interpolating and expanding the sample data, randomly dividing the preprocessed sample data, and constructing a training set and a test set, wherein in the embodiment, model training is performed by taking 80% of data as the training set and 20% of data as the test set; and taking the peak temperature, the cooling rate and the solder paste thickness in the process parameters as the input of the neural network model, and taking the solder joint climbing height, the solder joint extending width and the solder joint thickness as the output of the neural network model.
During training, the convolutional layer of the convolutional neural network learns to obtain the characteristic parameters of the process parameter training data, then the characteristic parameters are input into the pooling layer to be summarized, the convolutional data are output, after 3 times of convolution operation, the characteristic data of the previous layer are reconstructed in the full-connection layer to be used as the input of the full-connection network, and full-connection calculation is carried out to obtain the output data.
Comparing the predicted data of the convolutional neural network with the real data, when a difference exists, utilizing the back propagation learning of the neural network to finely adjust the weight and the bias in each layer of the network, carrying out forward operation again after the weight and the bias are corrected, calculating errors, increasing the number of network layers in the training process, deepening the depth of the network in a range that the network cannot be over-fitted, adjusting the number of neurons in each layer, increasing the training period, stopping the training when knowing that the errors meet an expected target or a loss value reaches the minimum value and tends to be stable, and thus obtaining a mapping model of the connection configuration predicted by process parameters under a typical connection process.
And C: the relation between the connection configuration and the reliability of the welding spot is obtained through simulation analysis, and the specific method is,
establishing a corresponding finite element model in ANSYS software according to the geometric dimensions of the substrate and the components, setting selected physical parameters, referring to the load borne by the substrate in the actual environment during working, applying corresponding boundary conditions and loads, evaluating the fatigue life of the welding spot under temperature impact based on an Engelmaier-Wild welding spot failure model, determining the incidence relation between the connection configuration and the stress-strain distribution of the welding spot, and establishing a connection configuration and reliability relation network.
Step D: and establishing a process parameter-connection configuration-reliability inversion equation, and adjusting the process parameters based on a particle swarm algorithm until the calculation result meets the reliability requirement.
The process parameter-connection configuration-reliability inversion equation comprises,
inversion model of process parameters:
Find X
s.t.min{x1is less than or equal to1≤max{x1}
min{x2Is less than or equal to2≤max{x2}
…
min{xnIs less than or equal ton≤max{xn}
Connecting the configuration inversion models:
Find Y
s.t.min{y1y is inverted1≤max{y1}
min{y2Y is inverted2≤max{y2}
…
min{ynY is invertedn≤max{yn}
Wherein X is a process parameter set, Y is a connection configuration set, Z is a welding spot life, and XiIs the ith process parameter, yiFor the ith connection configuration parameter, s.t. represents constraint condition, RMSE represents root mean square value, ZiRepresenting the solder joint lifetime in the t-th iteration.
The method for adjusting the process parameters based on the particle swarm optimization comprises the following steps,
step 1: initializing each parameter of the algorithm, setting maximum iteration times, independent variable quantity, maximum speed of ions, inertial weight, position information, self and group learning factors, randomly initializing speed and position in a speed interval and a search space, setting particle swarm size, initializing a random flight speed for each ion, and setting a target value and constraint conditions;
step 2: c, randomly generating a plurality of groups of connection configuration parameter combinations, and determining the stress strain of the welding spot based on the relation between the connection configuration obtained in the step C and the reliability of the welding spot to obtain a global optimal solution;
and step 3: and judging whether the global optimal solution meets a target value or reaches the maximum iteration times, if so, outputting the global optimal solution and the corresponding connection configuration parameters, if not, updating the structural parameters and the corresponding particle speed, returning to the step 2, and in the subsequent iteration process, comparing the updated optimal solution with the global optimal solution to update the global optimal solution.
The optimization target of the particle swarm optimization is the maximum stress and the maximum strain value of the dangerous welding spot, and the constraint condition is the interval range of each parameter; the formula for updating the speed and position is:
Vid＝ωVid+C1random(0,1)(Pid-Xid)+C2random(0,1)(Pgd-Xid)
Xid＝Xid+Vid
wherein, omega is more than or equal to 0 and is an inertia factor, the larger the numerical value is, the stronger the global optimization capability is, and the smaller the numerical value is, the stronger the local optimization capability is; c1And C2The former is an individual learning factor of each ion, and the latter is a social learning factor of each particle, as an acceleration constant; random (0,1) indicates the interval [0,1 ]]Random number of inner, PidD-dimension, P, representing individual extrema of i-th variablegdD-dimension, V, representing a global optimal solutionidD-dimension, X, representing the particle population variation speed of the ith variableidThe d-th dimension representing the particle space value of the i-th variable.
And (4) performing inversion on the connection configuration obtained by inversion by the same method to determine the process parameters of the connection configuration, so as to obtain the connection configuration and the process parameters used for welding.
The above examples are only intended to illustrate the technical solution of the present invention, but not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.
Claims (10)
1. A method for determining the connection configuration and process parameters of a component package is characterized in that: comprises the steps of (a) preparing a mixture of a plurality of raw materials,
step A: acquiring sample data of connection configuration and process parameters;
and B: taking the process parameters as input and the connection configuration as output, and training a neural network model;
and C: obtaining the relation between the connection configuration and the reliability of the welding spot through simulation analysis;
step D: and establishing a process parameter-connection configuration-reliability inversion equation, and adjusting the process parameters based on a particle swarm algorithm until the calculation result meets the reliability requirement.
2. The method for determining connection configuration and process parameters of a component package according to claim 1, wherein: the method for acquiring the sample data of the connection configuration and the process parameters comprises the following steps,
step i: determining geometric parameters and physical parameters of the connection configuration;
step ii: determining key technological parameters of a welding process;
step iii: and designing an experiment, namely obtaining the numerical values of key process parameters in the equidistant design welding process, carrying out the welding experiment, and obtaining the connection configuration corresponding to each group of process parameters.
3. The method for determining connection configuration and process parameters of a component package according to claim 2, wherein: the geometric parameters of the connection configuration comprise welding spot climbing height, welding spot extending width and welding spot thickness; the physical parameters include material type, material density, elastic modulus, and temperature characteristics.
4. The method for determining connection configuration and process parameters of a component package according to claim 2, wherein: the key process parameters of the welding process comprise peak temperature, cooling rate and thickness of soldering paste in welding.
5. The method for determining connection configuration and process parameters of a component package according to claim 2, wherein: and designing a simulation experiment based on the experimental data in the step iii, simulating the connection configuration of the welding spots under different process parameters, and expanding sample data.
6. The method for determining connection configuration and process parameters of a component package according to claim 1, wherein: and B, preprocessing sample data before training, cleaning, interpolating and expanding the sample data, randomly dividing the preprocessed sample data, constructing a training set and a testing set, taking the peak temperature, the cooling rate and the thickness of soldering paste in process parameters as the input of a neural network model, and taking the climbing height, the extending width and the thickness of a welding spot as the output of the neural network model.
7. The method for determining connection configuration and process parameters of a component package according to claim 1, wherein: the method for obtaining the relation between the connection configuration and the reliability of the welding spot through simulation analysis comprises the steps of establishing a corresponding finite element model according to the geometric dimensions of a substrate and components, setting physical parameters, referring to the load borne by the substrate in the actual environment during working, applying corresponding boundary conditions and loads, evaluating the fatigue life of the welding spot under temperature impact based on an Engelmaier-Wild welding spot failure model, determining the incidence relation between the connection configuration and the stress-strain distribution of the welding spot, and establishing a connection configuration and reliability relation network.
8. The method for determining connection configuration and process parameters of a component package according to claim 1, wherein: the process parameter-connection configuration-reliability inversion equation comprises,
inversion model of process parameters:
FindX
s.t.min{x1is less than or equal to1≤max{x1}
min{x2Is less than or equal to2≤max{x2}
…
min{xnIs less than or equal ton≤max{xn}
Connecting the configuration inversion models:
FindY
s.t.min{y1y is inverted1≤max{y1}
min{y2Y is inverted2≤max{y2}
…
min{ynY is invertedn≤max{yn}
Wherein X is a process parameter set, Y is a connection configuration set, Z is a welding spot life, and XiIs the ith process parameter, yiFor the ith connection configuration parameter, s.t. represents constraint condition, RMSE represents root mean square value, ZiRepresenting the solder joint lifetime in the t-th iteration.
9. The method for determining connection configuration and process parameters of a component package according to claim 8, wherein: the method for adjusting the connection configuration based on the particle swarm optimization comprises the following steps,
step 1: initializing each parameter of the algorithm, setting maximum iteration times, independent variable quantity, maximum speed of ions, inertial weight, position information, self and group learning factors, randomly initializing speed and position in a speed interval and a search space, setting particle swarm size, initializing a random flight speed for each ion, and setting a target value and constraint conditions;
step 2: randomly generating a plurality of groups of connection configuration parameter combinations, and determining the stress strain of the welding spot based on the relation between the connection configuration and the reliability of the welding spot to obtain a global optimal solution;
and step 3: judging whether the global optimal solution meets a target value or reaches the maximum iteration times, if so, outputting the global optimal solution and the corresponding connection configuration parameters, if not, updating the structural parameters and the corresponding particle speed, returning to the step 2, and in the subsequent iteration process, comparing the updated optimal solution with the global optimal solution to update the global optimal solution;
after the connection configuration is determined, the process parameters are determined based on the same method as above.
10. The method for determining connection configuration and process parameters of a component package according to claim 9, wherein: the optimization target of the particle swarm optimization is the maximum stress and the maximum strain value of the dangerous welding spot, and the constraint condition is the interval range of each parameter; the formula for updating the speed and position is:
Vid＝ωVid+C1random(0,1)(Pid-Xid)+C2random(0,1)(Pgd-Xid)
Xid＝Xid+Vid
wherein, omega is more than or equal to 0 and is an inertia factor, the larger the numerical value is, the stronger the global optimization capability is, and the smaller the numerical value is, the stronger the local optimization capability is; c1And C2The former is an individual learning factor of each ion, and the latter is a social learning factor of each particle, as an acceleration constant; random (0,1) indicates the interval [0,1 ]]Random number of inner, PidD-dimension, P, representing individual extrema of i-th variablegdD-dimension, V, representing a global optimal solutionidD-dimension, X, representing the particle population variation speed of the ith variableidThe d-th dimension representing the particle space value of the i-th variable.
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