CN116090310A - Multi-target plastic packaging process parameter optimization method based on field test and response surface method - Google Patents
Multi-target plastic packaging process parameter optimization method based on field test and response surface method Download PDFInfo
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Abstract
The invention discloses a multi-target plastic packaging process parameter optimization method based on a field opening test and a response surface method, which relates to the technical field of chip plastic packaging processes, and adopts the field opening method to design an experiment table by selecting process parameters required by the test; establishing a finite element model, obtaining a quality index by analog simulation, and calculating a signal to noise ratio; determining the weight of each quality index by adopting a signal-to-noise ratio range analysis method; determining comprehensive influence degree ranking of the process parameters by adopting a range analysis method of comprehensive scores weighted by a percentile system aiming at the multi-objective quality index; taking the technological parameters with the comprehensive influence degree ranked at the front as design variables, and establishing a response surface model between the technological parameters and the quality indexes; based on the fitted response surface model, optimizing by adopting a multi-target genetic algorithm; and (3) establishing a chip plastic package process parameter optimization system according to the test data and the response surface model, so that the accuracy of obtaining the optimal process parameter combination is improved, the production efficiency is improved, and the production period and the product quality requirements of chip plastic package are met.
Description
Technical Field
The invention relates to the technical field of chip plastic packaging processes, in particular to a multi-target plastic packaging process parameter optimization method based on a field test and a response surface method.
Background
In order to ensure the performance and quality of the product, the traditional chip plastic packaging process needs engineers to repeatedly adjust the plastic packaging process parameters according to own experience to obtain the best product quality, obviously, the method excessively depends on the working experience of the engineers, cannot ensure the final forming quality of the chip, and seriously affects the production speed and the production cost of the chip.
In the face of the strong competition of the chip industry, the requirements on the processing period and the time to market are shorter and shorter, so that the shortening of the process parameter design time is imperative, the establishment of a mathematical model to guide the production of actual plastic products is more and more common in the intelligent manufacturing era, and the application of an algorithm plays a larger and larger role in the production process.
Disclosure of Invention
In order to solve the technical problems, the invention provides a multi-objective plastic packaging process parameter optimization method based on a field test and a response surface method, which comprises the following steps of
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.
The technical scheme of the invention is as follows:
further, in step S1, the molding process parameters include mold temperature, plastic temperature, molding pressure, molding time, curing pressure, curing time, and air temperature.
In the above-mentioned optimization method of multi-objective plastic packaging process parameters based on field test and response surface method, in step S1, different level combinations are divided for each plastic packaging process parameter by 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.
In the foregoing optimization method of the multi-objective plastic packaging process parameters based on the field test and the response surface method, in 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.
In the method for optimizing the multi-objective plastic packaging process parameters based on the field test and the response surface method, in the step 2, a finite element model of a plastic packaging product is built through Rhino, then a surface grid is derived, and the surface grid is subjected to simulation by using 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.
In the above-mentioned optimization method of multi-objective plastic package process parameters based on field test and response surface method, in 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 quality index, and the larger the R value, the larger the affecting degree;
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.
In the foregoing optimization method of the multi-objective plastic packaging process parameters based on the field test and the response surface method, in step S4, weight distribution is determined according to step S3, and the weight distribution is weighted according to a percentage, where a percentage weighting 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.
In the foregoing multi-objective plastic packaging process parameter optimization method based on field test and response surface method, in step S5, the plastic packaging process parameters with three digits after the quality index is affected and ranked are removed, the plastic packaging process parameters with the three digits before the comprehensive effect degree is ranked are used as Design variables, the Box-Behnken method in Design-Expert is adopted, the response surface function is fitted by the least square method, a response surface model between the response surface model and the quality index is established, and variance and residual error are selected to verify the accuracy of the response surface model.
In the foregoing optimization method of multi-objective plastic package process parameters based on field test and response surface method, in step S6, based on the fitted response surface model, multi-objective optimization is performed by using a genetic algorithm tool box of MATLAB, the optimizing range is the horizontal dividing range determined in step S1, the algorithm optimizing result is compared with the corresponding process parameter simulation result to verify, wherein the removed process parameter value takes the average value of the horizontal dividing range determined in step S1.
In the step S7, a chip plastic packaging process parameter optimizing system is established according to the test data in the experiment 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 packaging process parameter optimizing system reports errors; 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.
The beneficial effects of the invention are as follows:
in the invention, the weight primary optimization of each process parameter to a single quality index is determined by an orthogonal test and a signal-to-noise ratio range analysis method, then the comprehensive influence degree ranking secondary optimization of the process parameters is determined by a range analysis method of a percentile weighted comprehensive score, and then the process parameters with larger comprehensive influence degree are used as design variables to establish the tertiary optimization of a response surface model, finally a chip plastic package process parameter optimization system is developed, the accuracy of obtaining the optimal process parameter combination is improved, the production efficiency is improved, and the production cycle requirement and the product quality requirement of the current chip plastic package are met.
Drawings
FIG. 1 is a schematic flow chart of the present invention;
fig. 2 is a simulation flow chart of a chip molding process according to an embodiment of the invention.
Detailed Description
The multi-objective plastic packaging process parameter optimization method based on the field test and the response surface method provided by the embodiment, as shown in fig. 1 and 2, comprises the following steps of
S1, selecting plastic packaging process parameters required by a test, wherein the plastic packaging process parameters comprise a mold temperature, a plastic temperature, a glue pouring pressure, a glue pouring time, a curing pressure, a curing time and an air temperature; 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;
and 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.
As shown in the following tables 1 and 2, respectively, for selection of plastic packaging process parameters and combination of experimental parameters, the horizontal division in table 1 is determined according to molding conditions recommended by Moldex3D and engineering sample data actually produced by enterprises, and table 2 is designed through field orthogonal test in Minitab analysis software, in this embodiment, warpage and gold wire offset are selected as quality indexes, and the results of the orthogonal test are output in columns C8-C12 in table 2.
Table 1 test factors and levels
Table 2 field test design table
S2, establishing a finite element model of the plastic package product through Rhino, then guiding out a surface grid, and carrying out simulation on the surface grid by utilizing Moldex3D die flow analysis software to obtain a quality index, wherein the quality index is set to be a warping amount and a gold thread offset; calculating the signal to noise ratio and counting the signal to noise ratio to an experiment table.
As shown in table 3, the statistical experiment table shows that the warpage amount and the gold wire offset amount, which are quality indexes, are different in dimension, and data normalization processing is required, and then mapped into the numerical space of [0,1 ].
Table 3 field test design table
The signal-to-noise ratio can truly reflect the influence condition of plastic package process parameters on quality index data, the signal-to-noise ratio can be analyzed based on three characteristics of looking at the eye, looking up the big and looking down the little, the warp and gold thread offset are all looking down the little characteristic, the computational formulas of different characteristics are different:
small observation characteristics:
wherein y is i Actual calculated values or measured values of the ith experimental result;
the telescope characteristic is as follows:
wherein y is i Actual calculated values or measured values of the ith experimental result;
eye viewing characteristics:
wherein,,
is the mean value, y i Is the actual value of the ith time.
S3, obtaining the ranking of the influence degree of each plastic package process parameter on the single quality index by adopting a signal-to-noise ratio range analysis method, and further determining the weight of each quality index.
As shown in table 4 and table 5, the warp average signal-to-noise ratio response and the gold wire offset average signal-to-noise ratio response obtained by the simulation result analysis are respectively listed, the weight R value of each plastic package process parameter affecting the quality index is listed by the range analysis, the greater the R value, the greater the affecting degree, and the ordering of the affecting degree of each plastic package process parameter is shown.
TABLE 4 analysis of very poor results of warpage
| Horizontal 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 |
| Ordering of | 1 | 4 | 5 | 7 | 3 | 6 | 2 |
TABLE 5 analysis of extremely bad results of gold wire offset
| Horizontal 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 |
| Ordering of | 1 | 7 | 3 | 2 | 6 | 4 | 5 |
As shown in table 6, for each quality index weight, each quality index weight is determined by summing all signal to noise ratios.
Table 6 weight of each quality index
| Quality index | Dimensionalized mean value | Weighting of |
| Amount of warpage | 97.1931 | 0.4140 |
| Gold wire offset | 137.5697 | 0.5860 |
| Sum total | 234.7628 | 1 |
S4, determining comprehensive influence degree ranking of the technological parameters by a range analysis method of comprehensive scores weighted by a percentile system, determining weight distribution according to the step S3, and counting calculation results to an experiment table.
The weight evaluation formula of the percentile is: y is Y i ＝w 1 ×Y 1i +w 2 ×Y 2i ；
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.
As shown in table 7, the experimental table after statistical comprehensive scoring; as shown in table 8, the percent weighted composite score response was analyzed for simulation results.
Table 7 field test design table
TABLE 8 analysis of the very poor results of the composite score
| Horizontal 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 |
| Ordering of | 3 | 5 | 6 | 1 | 4 | 7 | 2 |
S5, removing the plastic packaging process parameters with three ranked positions, which affect the quality index, taking the plastic packaging process parameters with the three ranked positions, which affect the quality index comprehensively, as Design variables, adopting a Box-Behnken method in Design-Expert, fitting a response surface function by a least square method, establishing a response surface model between the response surface function and the quality index, and selecting variance and residual error to verify the accuracy of the response surface model.
And S6, based on the fitted response surface model, carrying out multi-objective optimization by utilizing a genetic algorithm tool box of MATLAB, wherein the optimizing range is the horizontal dividing range determined in the step S1, and comparing the algorithm optimizing result with the corresponding process parameter simulation result to verify, wherein the removed process parameter value is the average value of the horizontal dividing range determined in the step S1.
S7, establishing a chip plastic package 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, and reporting errors by the chip plastic package process parameter optimization system when the process parameter range input by an engineer is not in the system setting range; when the optimal parameter combination is within the set range of the system, the optimal parameter combination within the effective range is pushed to an engineer by the chip plastic package process parameter optimization system.
The method comprises the steps of determining the primary optimization of each process parameter to the weight of a single quality index through an orthogonal test and a signal-to-noise ratio range analysis method, determining the secondary optimization of the comprehensive influence degree ranking of the process parameters through a range analysis method of comprehensive grading weighted by a percentile system, establishing the tertiary optimization of a response surface model by taking the process parameters with larger comprehensive influence degree as design variables, and finally developing a chip plastic package process parameter optimization system, improving the accuracy of obtaining the optimal process parameter combination, improving the production efficiency and meeting the production cycle requirement and the product quality requirement of the current chip plastic package.
In addition to the embodiments described above, other embodiments of the invention are possible. All technical schemes formed by equivalent substitution or equivalent transformation fall within the protection scope of the 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.
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| CN117305016A (en) * | 2023-10-19 | 2023-12-29 | 湖南师范大学 | Refined oil neutralization process optimization control method based on intelligent decision-making mode mining |
| CN117910363A (en) * | 2024-03-19 | 2024-04-19 | 中国汽车技术研究中心有限公司 | Collision simulation optimization method, device and equipment for electric two-wheel vehicle and storage medium |
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