CN116090310A - Optimization method of multi-objective plastic packaging process parameters based on Taguchi test and response surface method - Google Patents

Abstract

The invention discloses a multi-objective plastic packaging process parameter optimization method based on Taguchi test and response surface method, and relates to the technical field of chip plastic packaging technology. By selecting the process parameters required for the test, the Taguchi method is used to design the experimental table; a finite element model is established to simulate The quality index is obtained by simulation, and the signal-to-noise ratio is calculated; the weight of each quality index is determined by the range analysis method of the signal-to-noise ratio; the comprehensive influence degree ranking of the process parameters is determined by the range analysis method of the comprehensive score weighted by the percentage system for the multi-objective quality index ;Take the process parameters with the highest comprehensive influence degree as the design variables, and establish a response surface model with the quality index; based on the fitted response surface model, use a multi-objective genetic algorithm for optimization; The model establishes a chip plastic packaging process parameter optimization system, thereby improving the accuracy of obtaining the optimal process parameter combination, improving production efficiency, and meeting the production cycle and product quality requirements of chip plastic packaging.

Description

Multi-objective plastic packaging process parameter optimization method based on Taguchi test and response surface method

technical field

The invention relates to the technical field of chip plastic packaging technology, in particular to a multi-objective plastic packaging process parameter optimization method based on Taguchi test and response surface method.

Background technique

In the traditional chip plastic packaging process, in order to ensure the performance and quality of the product, engineers need to repeatedly adjust the plastic packaging process parameters based on their own experience in order to obtain the best product quality. Obviously, this method relies too much on the engineer's work experience and cannot guarantee The final molding quality of the chip seriously affects the chip production speed and production cost.

In the face of fierce competition in the chip industry, the processing cycle and time-to-market requirements are getting shorter and shorter. Therefore, it is imperative to shorten the design time of process parameters. In the era of intelligent manufacturing, it is more and more common to establish mathematical models to guide the actual production of plastic parts. The use of algorithms is also playing an increasing role in its production process.

Contents of the invention

In order to solve the above technical problems, the present invention provides a multi-objective plastic packaging process parameter optimization method based on Taguchi test and response surface method, comprising the following steps

S1. Select the plastic sealing process parameters required for the test, divide each plastic sealing process parameter into different levels of combinations, and use the Taguchi method to design the experimental table;

S2. Establish the finite element model of the plastic package product, obtain the quality index through simulation, calculate the signal-to-noise ratio, and put the statistics into the experimental table;

S3. Using the signal-to-noise ratio range analysis method to obtain the ranking of the influence degree of each plastic packaging process parameter on a single quality index, and then determine the weight of each quality index;

S4. For multi-objective quality indicators, adopt the range analysis method of the comprehensive score weighted by the percentage system to determine the ranking of the comprehensive influence degree of each plastic packaging process parameter;

S5. Taking the top three plastic packaging process parameters ranked in the comprehensive influence degree as design variables, using the least squares method to fit the response surface function, establishing a response surface model with quality indicators, and verifying its accuracy;

S6. Based on the fitted response surface model, the multi-objective genetic algorithm is used for optimization, and the algorithm optimization result is compared with the simulation result of the corresponding plastic packaging process parameters for verification;

S7. According to the test data in the experiment table in step S1 and the response surface model in step S6, establish a chip plastic packaging process parameter optimization system, and the chip plastic packaging process parameter optimization system is used to push the generated optimal parameter combination to engineers.

The technical scheme further defined in the present invention is:

Further, in step S1, the plastic sealing process parameters include mold temperature, plastic temperature, glue flushing pressure, glue flushing time, curing pressure, curing time and air temperature.

In the above-mentioned multi-objective plastic packaging process parameter optimization method based on Taguchi test and response surface method, in step S1, through the molding condition parameters recommended by Moldex3D and the engineering sample data actually produced by the enterprise, each plastic packaging process parameter is divided into different level combinations ;

Using the Taguchi method to design the experimental table, the Taguchi method uses the software Minitab to design the orthogonal table, and the selected factors and their respective level values are input into the software for the experimental design.

In the aforementioned multi-objective plastic packaging process parameter optimization method based on Taguchi test and response surface method, in step S2, the quality index is set to warpage, Von Mises stress, gold wire offset, lead frame offset and volume shrinkage two or more of.

In the multi-objective plastic packaging process parameter optimization method based on the Taguchi test and the response surface method mentioned above, in step 2, the finite element model of the plastic packaging product is established through Rhino, and then the surface grid is exported, and the surface grid is analyzed using Moldex3D mold flow analysis software. Quality indicators obtained by simulation;

The signal-to-noise ratio is calculated, and the signal-to-noise ratio is analyzed based on the three characteristics of the eye, the large and the small.

In the aforementioned multi-objective plastic packaging process parameter optimization method based on Taguchi test and response surface method, in step S3, the weight R value of each plastic packaging process parameter affecting the quality index is obtained by using the range analysis method of signal-to-noise ratio. Larger, the greater the degree of influence;

The signal-to-noise ratio results of the orthogonal test are selected as the index of influence degree, and all the dedimensionalized signal-to-noise ratios are summed up to determine the weight of each quality index.

In the multi-objective plastic packaging process parameter optimization method based on Taguchi test and response surface method mentioned above, in step S4, the weight distribution is determined according to step S3, weighted by the percentage system, and the weighted evaluation formula of the percentage system is:

Among them, i represents the test sequence number, Y _i represents the comprehensive score of the i-th group of tests, w _k represents the weight ratio, and Y _ki represents the quality index data results.

In the above-mentioned multi-objective plastic packaging process parameter optimization method based on Taguchi test and response surface method, in step S5, remove the last three plastic packaging process parameters that affect the quality index, and rank the top three plastic packaging process parameters in terms of comprehensive influence As a design variable, the Box-Behnken method in Design-Expert was used to fit the response surface function with the least square method, and the response surface model between the quality index and the quality index was established, and the variance and residual error were selected to verify its accuracy.

The aforementioned multi-objective plastic packaging process parameter optimization method based on Taguchi test and response surface method, in step S6, based on the fitted response surface model, use the genetic algorithm toolbox that comes with MATLAB to perform multi-objective optimization. The range is the horizontal division range determined in step S1, and the algorithm optimization result is compared with the corresponding process parameter simulation results for verification, wherein the value of the removed process parameter is the mean value of the horizontal division range determined in step S1.

In the aforementioned multi-objective plastic packaging process parameter optimization method based on Taguchi test and response surface method, in step S7, according to the test data in the experimental table in step S1 and the response surface model in step S6, a chip plastic packaging process parameter optimization system is established , when the process parameter range input by the engineer is not within the system setting range, the chip plastic packaging process parameter optimization system will report an error; when it is within the system setting range, the chip plastic packaging process parameter optimization system will combine the optimal parameters within the effective range Pushed to engineers, the optimal parameter combination pushed by the chip plastic packaging process parameter optimization system produces the best quality index under the condition of any given parameter value.

The beneficial effects of the present invention are:

In the present invention, the initial optimization of the weight of each process parameter to a single quality index is determined by the orthogonal test and the range analysis method of the signal-to-noise ratio, and then the comprehensive influence degree ranking of the process parameters is determined by the range analysis method of the weighted comprehensive score of the percentage system The second optimization, and then the third optimization of the response surface model is established by taking the process parameters with a large comprehensive influence as the design variables, and finally develops the chip plastic packaging process parameter optimization system to improve the accuracy of obtaining the optimal process parameter combination and improve production efficiency. Meet the current production cycle requirements and product quality requirements of chip plastic packaging.

Description of drawings

Fig. 1 is a schematic flow sheet of the present invention;

Fig. 2 is a simulation flow chart of the chip plastic packaging process according to the embodiment of the present invention.

Detailed ways

A multi-objective plastic packaging process parameter optimization method based on Taguchi test and response surface method provided in this embodiment, as shown in Figure 1 and Figure 2, includes the following steps

S1. Select the plastic sealing process parameters required for the test. The plastic sealing process parameters include mold temperature, plastic temperature, glue punching pressure, glue punching time, curing pressure, curing time, and air temperature; the molding condition parameters recommended by Moldex3D are compared with the actual production projects of the company Sample data, divide different levels of combinations for each plastic packaging process parameter;

The Taguchi method was used to design the experimental table, and the Taguchi method was used to design the orthogonal table using the software Minitab, and the selected factors and their respective level values were input into the software for the experimental design.

As shown in Table 1 and Table 2 below, they are the selection of plastic sealing process parameters and the combination of experimental parameters. The horizontal division of Table 1 is determined according to the molding conditions recommended by Moldex3D and the actual engineering sample data produced by the enterprise. Table 2 is determined by the Minitab analysis software. Taguchi's orthogonal test design, this embodiment selects warpage and gold wire offset as quality indicators, and columns C8-C12 in Table 2 output the results of the orthogonal test.

Table 1 Test factors and levels

Table 2 Taguchi experimental design table

S2. Establish the finite element model of the plastic packaging product through Rhino, and then export the surface mesh, use Moldex3D mold flow analysis software to simulate the surface mesh to obtain the quality index, and the quality index is set to warpage and gold wire offset; calculate Signal-to-noise ratio, statistics to the experiment table.

As shown in Table 3, it is the experimental table after statistics. The warpage amount and the gold wire offset as quality indicators are different in dimension, so the data needs to be normalized, and then the two are mapped to [0, 1] in the numerical space.

Table 3 Taguchi experimental design table

The signal-to-noise ratio can truly reflect the influence of the plastic packaging process parameters on the quality index data. Small features, different calculation formulas for different features:

Hope small features:

Among them, y _i is the actual calculated value or measured value of the ith experimental result;

Expect big features:

Among them, y _i is the actual calculated value or measured value of the ith experimental result;

Wangmu features:

为均值，y_i为第i次的实际值。in,

is the mean value, and y _i is the actual value of the ith time.

S3. Using the signal-to-noise ratio range analysis method to obtain the ranking of the influence degree of each plastic packaging process parameter on a single quality index, and then determine the weight of each quality index.

As shown in Table 4 and Table 5, they are the average signal-to-noise ratio response of the warpage amount and the average signal-to-noise ratio response of the gold wire offset obtained from the analysis of the simulation results. The weight R value of the influence of the index, the greater the R value, the greater the degree of influence, showing the ranking of the influence degree of each plastic packaging process parameter.

Table 4 Analysis of extreme difference in warpage

level A/℃ B/℃ C/MPa D/s E/MPa F/s G/s 1 12.4020 12.1763 12.1240 12.1380 12.2010 12.1605 11.9739 2 11.8963 12.1220 12.1744 12.1603 12.0973 12.1378 12.3244 R 0.5057 0.0543 0.0504 0.0223 0.1037 0.0227 0.3505 to sort 1 4 5 7 3 6 2

Table 5 Analysis of extreme difference results of gold wire offset

level A/℃ B/℃ C/MPa D/s E/MPa F/s G/s 1 16.7154 17.1942 17.2071 16.9889 17.1983 17.1880 17.2043 2 17.6771 17.1982 17.1853 17.4035 17.1942 17.2045 17.1882 R 0.9617 0.0040 0.0218 0.4146 0.0041 0.0165 0.0161 to sort 1 7 3 2 6 4 5

As shown in Table 6, it is the weight of each quality index, and the weight of each quality index is determined by adding all the signal-to-noise ratios.

Table 6 The weight of each quality index

Quality Index dequantized mean Weights Warpage 97.1931 0.4140 gold wire offset 137.5697 0.5860 sum 234.7628 1

S4. Each factor has different influence laws on different product indexes. It is necessary to determine the ranking of the comprehensive influence degree of the process parameters through the range analysis method of the comprehensive score weighted by the percentage system. The weight distribution is determined according to step S3, and the calculation results are counted in the experiment table.

The weighted judgment formula of the percentage system is: Y _i ＝w ₁ ×Y _1i +w ₂ ×Y _2i ;

Among them, i represents the test sequence number, Y _i represents the comprehensive score of the i-th group of tests, w _k represents the weight ratio, and Y _ki represents the quality index data results.

As shown in Table 7, it is the experimental table after the statistical comprehensive score; as shown in Table 8, it is the comprehensive score response of the weighted 100-point scale obtained from the analysis of the simulation results.

Table 7 Taguchi test design table

Table 8 Analysis of Extreme Poor Results of Comprehensive Score

level A/℃ B/℃ C/MPa D/s E/MPa F/s G/s 1 0.1849 0.1830 0.1836 0.1855 0.1829 0.1833 0.1854 2 0.1819 0.1837 0.1832 0.1813 0.1839 0.1835 0.1814 R 0.0030 0.0007 0.0004 0.0042 0.0010 0.0002 0.0040 to sort 3 5 6 1 4 7 2

S5. Remove the last three plastic packaging process parameters that have the highest impact on quality indicators, and use the top three plastic packaging process parameters with the highest comprehensive influence as design variables, and use the Box-Behnken method in Design-Expert to fit the response with the least squares method Surface function, establish a response surface model with quality indicators, and select variance and residual to verify its accuracy.

S6. Based on the fitted response surface model, use the genetic algorithm toolbox that comes with MATLAB to perform multi-objective optimization. The optimization range is the horizontal division range determined in step S1. Compare the algorithm optimization results with the corresponding process parameter simulation The result is verified, wherein the value of the removed process parameter takes the mean value of the horizontal division range determined in step S1.

S7. According to the test data in the experimental table in step S1 and the response surface model in step S6, establish a chip plastic packaging process parameter optimization system. When the process parameter range input by the engineer is not within the system setting range, the chip plastic packaging process parameter optimization system Make an error report; when it is within the system setting range, the chip plastic packaging process parameter optimization system will push the optimal parameter combination within this effective range to the engineer.

Therefore, the initial optimization of the weight of each process parameter to a single quality index is determined through the orthogonal test and the range analysis method of the signal-to-noise ratio, and then the secondary optimization of the ranking of the comprehensive influence degree of the process parameters is determined through the range analysis method of the weighted comprehensive score of the percentage system. , and then take the process parameters with greater comprehensive influence as the design variables to establish the three-time optimization of the response surface model, and finally develop the chip plastic packaging process parameter optimization system to improve the accuracy of obtaining the optimal process parameter combination, improve production efficiency, and meet the needs of the current chip industry. Production cycle requirements and product quality requirements for plastic packaging.

In addition to the above-mentioned embodiments, the present invention can also have other implementations. All technical solutions formed by equivalent replacement or equivalent transformation fall within the scope of protection required by the present invention.

Claims (10)

1. A multi-target plastic packaging process parameter optimization method based on a field test and a response surface method is characterized by comprising the following steps of: comprises the following steps

S1, selecting plastic packaging process parameters required by a test, dividing the plastic packaging process parameters into different horizontal combinations, and designing an experiment table by a field opening method;

s2, establishing a finite element model of the plastic package product, obtaining a quality index through simulation, calculating a signal-to-noise ratio, and counting to an experiment table;

s3, obtaining the degree of influence ranking of each plastic package process parameter on a single quality index by adopting a signal-to-noise ratio range analysis method, and further determining the weight of each quality index;

s4, determining comprehensive influence degree ranking of each plastic packaging process parameter by adopting a minimum analysis method of comprehensive scores weighted by a percentile system according to the multi-objective quality index;

s5, taking plastic packaging process parameters with the top three comprehensive influence degrees as design variables, fitting a response surface function by a least square method, establishing a response surface model between the response surface model and the quality index, and verifying the accuracy of the response surface model;

s6, optimizing by adopting a multi-target genetic algorithm based on the fitted response surface model, and comparing the algorithm optimizing result with the corresponding plastic package process parameter simulation result to verify;

and S7, establishing a chip plastic packaging process parameter optimization system according to the test data in the test table in the step S1 and the response surface model in the step S6, wherein the chip plastic packaging process parameter optimization system is used for pushing the generated optimal parameter combination to engineers.

2. The multi-objective plastic packaging process parameter optimization method based on the field test and the response surface method according to claim 1, wherein the method is characterized by comprising the following steps of: in the step S1, the plastic packaging process parameters include a mold temperature, a plastic temperature, a glue-pouring pressure, a glue-pouring time, a curing pressure, a curing time and an air temperature.

3. The multi-objective plastic packaging process parameter optimization method based on the field test and the response surface method according to claim 1, wherein the method is characterized by comprising the following steps of: in the step S1, different level combinations are divided for each plastic package process parameter through the molding condition parameters recommended by Moldex3D and engineering sample data actually produced by enterprises;

the experimental table is designed by using a field method, the field method adopts a software Minitab to design an orthogonal table, and factors and respective horizontal values are selected to input the software for experimental design.

4. The multi-objective plastic packaging process parameter optimization method based on the field test and the response surface method according to claim 1, wherein the method is characterized by comprising the following steps of: in the step S2, the quality index is set to two or more of warpage, von Mises stress, gold wire offset, lead frame offset, and volume shrinkage.

5. The multi-objective plastic packaging process parameter optimization method based on the field test and the response surface method according to claim 1, wherein the method is characterized by comprising the following steps of: in the step 2, a finite element model of the plastic package product is built through Rhino, then a surface grid is derived, and the surface grid is simulated by utilizing Moldex3D model flow analysis software to obtain a quality index;

and calculating a signal-to-noise ratio, wherein the signal-to-noise ratio is analyzed based on three characteristics of looking, looking big and looking small.

6. The multi-objective plastic packaging process parameter optimization method based on the field test and the response surface method according to claim 1, wherein the method is characterized by comprising the following steps of: in the step S3, a range analysis method of signal to noise ratio is adopted to obtain a weight R value of each plastic package process parameter affecting the quality index, and the larger the R value is, the larger the affecting degree is;

and selecting an orthogonal test signal-to-noise ratio result as an influence degree index, and summing all the dimensionalized signal-to-noise ratios to further determine the weight of each quality index.

7. The multi-objective plastic packaging process parameter optimization method based on the field test and the response surface method according to claim 1, wherein the method is characterized by comprising the following steps of: in the step S4, weight allocation is determined according to the step S3, and the weight is weighted according to a percentage system, where a percentage system weight evaluation formula is as follows:

wherein i represents test number, Y _i Represents the i-th group test comprehensive score, w _k Represents the weight ratio, Y _ki Representing the quality index data results.

8. The multi-objective plastic packaging process parameter optimization method based on the field test and the response surface method according to claim 1, wherein the method is characterized by comprising the following steps of: in the step S5, the plastic packaging process parameters with three ranked positions affecting the quality index are removed, the plastic packaging process parameters with the three ranked positions affecting the degree of the comprehensive effect are used as Design variables, a Box-Behnken method in Design-Expert is adopted, a response surface function is fitted by a least square method, a response surface model between the Design-Expert and the quality index is established, and variance and residual errors are selected to verify the accuracy of the response surface model.

9. The multi-objective plastic packaging process parameter optimization method based on the field test and the response surface method according to claim 1, wherein the method is characterized by comprising the following steps of: in the step S6, based on the fitted response surface model, the MATLAB is utilized to perform multi-objective optimization by using the genetic algorithm tool box, the optimizing range is the horizontal dividing range determined in the step S1, the algorithm optimizing result is verified against the corresponding process parameter simulation result, and the removed process parameter value takes the average value of the horizontal dividing range determined in the step S1.

10. The multi-objective plastic packaging process parameter optimization method based on the field test and the response surface method according to claim 1, wherein the method is characterized by comprising the following steps of: in the step S7, a chip plastic package process parameter optimization system is established according to the test data in the test table in the step S1 and the response surface model in the step S6, and when the process parameter range input by the engineer is not within the system setting range, the chip plastic package process parameter optimization system performs error reporting; when the parameters are within the set range of the system, the optimal parameter combination in the effective range is pushed to an engineer by the chip plastic package process parameter optimization system, and the optimal parameter combination pushed by the chip plastic package process parameter optimization system generates the optimal quality index under the condition of giving any parameter value.

Images