SMT is an assembly technology for core circuit board parts. Unless process parameters are effectively controlled, poor solderability may result in a decline in product quality. This study looks at an SMT manufacturing process in a multinational company. First, the TRIZ contradiction matrix is revised to investigate the association between the 39 parameters in the contradiction matrix and 13 parameters that influence the unevenness of solder paste in the solder paste printing process. Expert verification is then used to screen the key factors affecting the quality of SMT, which are then combined with Taguchi's method to identify the optimal parameter set influencing the thickness of SMT solder paste.
Today, product cycles are short and undergo rapid changes. Consumer electronic products, such as smart phones, notebook computers, and digital cameras, play an important role in our everyday lives. As consumers become more demanding, there is an increasing emphasis on lighter, thinner, high quality, and low-priced products that can be delivered to the consumer quickly. In addition, due to global competition, businesses are increasingly demanding rigorous quality standards to meet customer’s demand [
To meet the demand for light, thin, short, and small electronic products, print circuit boards (PCB) have evolved from single-layer to multilayer boards, with a consequent reduction in their size. As a result, most traditional plated-through hole parts are gradually falling out of use. An effective solution to this is to attach electronic components onto the PCB, resulting in the development of surface mount technology (SMT). However, unless process parameters are effectively controlled, poor solderability may result in a decline in product quality. For example, if insufficient solder paste is deposited at the PCB printing stage, the strength of the solder joints may be inadequate or an empty solder phenomenon may occur. However, if too much solder paste is deposited, bridges may form between the solder joints, leading to short circuits.
Yang et al. [
Andersen and Fagerhaug [
TRIZ is an abbreviation for the “theory of inventive problem solving” in the Russian language. The theory was developed by the Soviet inventor Altshuller [
Altshuller [
The aim of TRIZ is to avoid conflicts between different elements. Hence, Altshuller identified 39 engineering parameters that often produce technical contradictions. In the matrix, each cell indicates the principles used to resolve these contradictions. The matrix provides a fast and simple way to find solutions to technical contradictions. The matrix is a 39 × 39 matrix. To resolve the contradictions, Altshuller [
Loh et al. [
Figure
An example of the contradiction matrix.
Taguchi’s method is derived from traditional experimental design methods. This method was developed by Genichi Taguchi in 1949. When applied in designing communication systems, it enables the number of experiments to be reduced and at the same time identifies problems in such systems [
Taguchi et al. [
Zhang et al. [
Yildiz [
Taguchi’s method specifies the objective function as a certain signal-to-noise ratio (
TRIZ and Taguchi’s method are alternative experimental design methods used by enterprises to develop new products and improve product quality. The two approaches are often used separately. The TRIZ contradiction matrix enables technical variables that influence quality characteristics to be quickly identified. Although such an approach is able to identify inventive principles, it can only help users to speculate about solutions. The two methods show a wide variation in their experimental efficiency and additivity. Taguchi’s method can identify a more optimal value from preset factor levels. However, these variables do not necessarily have a significant effect on quality characteristics.
Therefore, this study attempts to combine TRIZ and Taguchi’s method, screening variables that have a significant influence on quality characteristics by linking factors that cause uneven solder paste in the solder paste printing process to the 39 engineering parameters in the TRIZ contradiction matrix, before applying Taguchi’s method to the screened variables to identify the optimum process parameter set. The steps are as follows. Determine experimental variables: list the factors affecting the evenness of solder paste during the solder paste printing process, integrate the 39 engineering parameters in the TRIZ contradiction matrix to create a correlation table and produce a ranking, screening the variables that have a significant effect on quality characteristics. Design and run experiment: use the orthogonal array function from Taguchi’s method for experiment design, the number of repetitions, and conduct the experiment; calculate the Optimization analysis: a two-phase optimization analysis is carried out on experimental data to identify the optimum combination and predict the optimum model for the manufacturing process. Maximize the Move the mean closer to the target value: at this stage, the selection has no effect on the Forecast optimization: applying the additive model, the expected Confirmation tests: confirmation experiments are run on the optimum combination produced using Taguchi’s method. This result is then compared with the predicted results using Taguchi’s method to confirm the improvement in results.
This study uses the SMT solder paste printing process for PCB production by a multinational company as a case study to investigate the uniformity of solder paste application. The main purpose of solder paste in PCB is to fix parts to the PCB to ensure that the product functions normally. Solder paste printing is the first stage of the SMT manufacturing process. A stencil and solder paste printer squeeze are used to insert the solder paste onto corresponding pads in the PCB through holes in the board. After removing the stencil, the solder paste is left on the pad in the correct shape, completing the printing process, as shown in Figure
The solder paste printing process.
Table
Correlation table between factors influencing the uneven thickness of solder paste and 39 technical parameters.
Technical parameters | Influence factors | ||||||||||||
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1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 | 13 | |
Work temperature | Work humidity | Solder paste type | Solder paste proportion | Squeegee angle | Squeegee pressure | Squeegee speed | Ejection speed | Solder paste poise | Stencil tensity | Squeegee stencil thickness | PCB flatness | Working platform flatness | |
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Number of correlations | 4 | 2 | 6 | 7 | 12 | 13 | 11 | 12 | 10 | 9 | 7 | 9 | 7 |
Ranking | 12 | 13 | 11 | 8 | 2 | 1 | 4 | 2 | 5 | 6 | 8 | 6 | 8 |
We carry out the experiment on the squeegee angle, squeegee pressure, squeegee speed, and ejection speed as the four variables that have a significant influence on quality characteristics. The experimental design uses the
Based on the influence factor and level settings, nine sets of parameter values are input into the solder paste printer to carry out actual PCB printing. The experiment was repeated four times for each run order and the actual solder paste thickness data was recorded. The target value for solder paste thickness is set at 0.15 mm, with an upper limit of 0.20 mm and a lower limit of 0.10 mm. Table
Experimental design using the
Run order |
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Mean |
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Squeegee pressure | Squeegee angle | Squeegee speed | Ejection speed | |||
1 | 1 | 1 | 1 | 1 | 21.2140 | 0.1725 |
2 | 1 | 2 | 2 | 2 | 21.5987 | 0.1700 |
3 | 1 | 3 | 3 | 3 | 25.8433 | 0.1600 |
4 | 2 | 1 | 2 | 3 | 23.3596 | 0.0850 |
5 | 2 | 2 | 3 | 1 | 19.8540 | 0.1475 |
6 | 2 | 3 | 1 | 2 | 25.8433 | 0.1600 |
7 | 3 | 1 | 3 | 2 | 18.1639 | 0.0775 |
8 | 3 | 2 | 1 | 3 | 20.8458 | 0.0900 |
9 | 3 | 3 | 2 | 1 | 20.1436 | 0.1525 |
Carry out optimization analysis on the experimental data and predict the optimal model for the experimental process.
Analysis of the influence of each experiment factor (
Table
Level |
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1 | 22.89 | 20.91 | 22.63 | 20.40 |
2 | 23.02 | 20.77 | 21.70 | 21.87 |
3 | 19.72 | 23.94 | 21.29 | 23.35 |
Delta | 3.30 | 3.18 | 1.35 | 2.95 |
Average | 2.695 | |||
Rank | 1 | 2 | 4 | 3 |
Following Lee [
The ANOVA procedure was used to investigate which design parameters significantly affect quality characteristics. The procedure is performed by separating the total variability of the
Yildiz [
An examination of the calculated percent contribution for all experiment factors also shows a very high influence of factor
Results of the analysis of variance for
Source | DF | SS | MS |
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Pure SS | Contribution (%) |
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2 | 20.949 | 10.475 | 7.33 | 18.891 | 34.14% |
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2 | 19.303 | 9.651 | 6.75 | 17.245 | 31.17% |
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2 | 13.016 | 6.508 | 4.55 | 10.958 | 19.81% |
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2 | 2.058 | 1.429 | — | — | |
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Pooled error | (2) | (2.058) | (1.429) | 8.232 | 14.88% | |
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Total | 8 | 55.326 | 55.326 | 100% |
First, we find that the percent contributions of squeegee pressure (factor
Based on the above analysis, we are able to determine that squeegee pressure (factor
The plots for
Main effect plots for
At this stage, we select appropriate adjustment factors (with no effect on the
Means response table for uniformity.
Level |
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1 | 0.1675 | 0.1117 | 0.1408 | 0.1575 |
2 | 0.1478 | 0.1358 | 0.1358 | 0.1358 |
3 | 0.1067 | 0.1575 | 0.1283 | 0.1117 |
Delta | 0.0608 | 0.0458 | 0.0125 | 0.0458 |
Examination of the calculated percent contribution for all experiment factors also shows a very high influence of factor
Results of the analysis of variance for means.
Source | DF | SS | MS |
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Pure SS | Contribution (%) |
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2 | 0.005629 | 0.002815 | 23.70 | 0.005392 | 44.29% |
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2 | 0.003154 | 0.001577 | 13.28 | 0.002917 | 23.96% |
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2 | 0.003154 | 0.001577 | 13.28 | 0.002917 | 23.96% |
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2 | 0.000237 | 0.000119 | — | — | |
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Pooled error | (2) | (0.000237) | (0.000119) | 0.000949 | 7.79% | |
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Total | 8 | 0.012175 | 0.012175 | 100% |
First, we find that the percent contributions of squeegee pressure (factor
Based on the previous discussion, this study sets the optimal factor level to
Based on the previous discussion, this study sets the optimal factor level to
The mean
Similarly, the mean observation for the nine experiments is
The confirmation experiment under optimal conditions produced 25 individual values and 5
The
The average of the five confirmation experiments is as follows:
Comparison of values before improvement (current), forecast optimization, and confirmation experiments.
Before improvement (current) | Forecast optimization | Confirmation experiment | |
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21.732db | 26.562 db | 26.632 db |
Mean | 0.163 mm | 0.147 mm | 0.155 mm |
Confirmation experiments are run on the optimum combination produced using Taguchi’s method. This result is then compared with the predicted results using Taguchi’s method to confirm the improvement in results.
In this study, the
This study revised the TRIZ contradiction matrix to investigate the correlation between contradiction matrix parameters and parameters that directly influence the uneven thickness of solder paste in the solder paste printing process, screening the squeegee pressure, ejection speed, squeegee speed, and squeegee angle as the key parameters affecting the quality of SMT solder paste thickness. This is an innovative approach that is empirically shown to be feasible.
Taguchi’s method is used to establish an optimal parameter set from the experimental data, with the prediction error rate reaching the required accuracy and delivering real improvements in process capability and product quality. These improvements can help lower the defect rate and reduce production costs, while shortening delivery times and increasing customer satisfaction. These results may help Taiwan’s SMT assembly factories to increase product quality, explore further different machines and productivity factors, and compare different level parameters to produce even better process parameters for realizing additional quality improvements.