Molding Simulation

The unbalanced flow of mold compound and entrapped voids can degrade the molding yield of integrated circuit (IC) packages and induce quality and reliability issues in field applications.

To improve mold flow yield and optimize the molding process, an advanced technique has been used to identify the sensitivity factors that affect the melt front advancement and induce entrapped voids during the mold flow process. The technique used was C-Mold software for simulation plus a design of experiments (DOE) approach. The “melt front index” or “void index” was established based on the shape of flow advancement in the C-Mold simulations, and this was applied for virtual DOE analysis. The short shot experimental validation was executed to examine the accuracy of the simulation prediction. It was clearly observed that the prediction of the C-Mold simulation showed good agreement with the actual short shot results. It demonstrated that C-Mold simulation with the virtual DOE analysis using the flow pattern and void index concepts could screen the whole mold flow process and optimize the parameters of the transfer molding process based on the sensitivity analysis of the design, process and material parameter setup.

Flow Pattern and Void Index Definition

Figure 1. a) Dynamic variation of the flow front shape during the transfer molding process, and b) definition of flow pattern index.
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In a study to capture more accurate void formation, the flow pattern and air trap indexes were introduced. The flow index can be used to analyze quantitatively the seriousness of void formation. Figure 1a shows the typical flow front progress in the mold cavity during the transfer molding process. At the initial stage, the flow front has a convex shape. However, the flow pattern at the central portion could gradually change to a concave shape after the mold compound touches the die because of the different flow resistance between the edge and central part of the flow front. As such, the index can be defined after the mold flow has progressed over the die, e.g., 50 to 75 percent of the flow completion time, because the flow front could change during that portion of the filling process.

Fig 2a.
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Hence, the typical shape of the central portion of the flow front could be described in Figure 1b. Based on the experience of cavity studies, the probability of void formation was high if the shape of Figure 1b occurred at the short shot evaluation. As described in Figure 1b, h is the depth and l is the width of flow opening. If h/l is greater or much greater than one, the flow pattern index ranges from 0.5 to 1.0. However, if h/l is smaller than or equivalent to one, the flow pattern index ranges from 0 to 0.5. It should be noted that the flow pattern index is an empirical concept that had to be verified by the actual mold flow evaluation. Additionally, the air trap index was another output parameter from C-Mold that could be used to check the flow pattern index accuracy. As illustrated in Figure 2, the flow pattern and air trap index can be extracted from the results.

Figure 2. The a) typical flow pattern and b) typical air trap output from C-Mold simulations.
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Simulation Design Implementation

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An actual “design of experiments” is a test or a series of tests in which purposeful changes are made to the input variables of a process or design so that the reasons for changes in the output response can be identified. Therefore, the objective of the experiment is to determine which variables are most influential on the response, or to decide where to set the influential variable so that the response is as likely as possible to be near the desired nominal value. In this study, two design parameters, die thickness and lead frame downset, were taken into account because they could affect the flow behaviors. The other process and material properties — mold temperature, transfer time and mold compound viscosity — had to be considered since these factors would impact the balanced flow in the cavity as well. The design of simulation matrix is shown in Table 1, with five input parameters with two levels each, for a total of 25 combinations. The corresponding flow pattern and air trap index were interpreted based on the simulated flow pattern and voids from C-Mold simulation.

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The thermal and molding process properties of the epoxy molding compound, as well as the molding process variables, are shown in Table 2. Using the pre-processor in C-Mold, a thin shell mid-plane surface of the IC package and a representation of the delivery system were constructed based on the package and mold design. The data transfer from AutoCAD was performed via IGES file format. Some minor geometric details were simplified to reduce the turnaround time of the computer simulation, but the model still used about 12,000 elements. The computer running time was about 3.5 hours for each case of the simulation.

DOS Analysis of the Air Trap Index

Figure 3. Standardized Pareto chart of the air trap index for the bottom cavity of the mold.
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The design of simulation (DOS) analysis results for air trap index in the bottom cavity of the mold are presented as the Pareto chart shown in Figure 3. In a Pareto chart, the effects of the factors studied are listed according to their relative magnitude. It can be seen from Figure 3 that among the factors studied for the bottom cavity, the lead frame downset has the most significant effect on the air trap index, followed by the viscosity. The transfer time and its interactions with the downset and viscosity are next. From Figure 4, it can be seen that as the downset increases from 0.005 to 0.011″, the air trap index increases from about 0.1 to 0.6, while the viscosity change from 50 to 250 causes the air trap index to increase from 0.3 to 0.5.

Figure 4. The effect of some input variables on the air trap index for the bottom cavity.
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For the air trap index of the top cavity, a different Pareto chart pattern is observed (Figure 5). The viscosity and die thickness are both significant for the air trap index. There also is a strong interaction between the viscosity and die thickness.

Figure 5. Standardized Pareto chart of the air trap index for the top cavity of the mold.
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Experimental Validation

DOS result validation was performed with a series of “short shots” to compare the actual and the predicted void locations, as well as the shapes of the flow front at different times. The short shots were evaluated by stopping the transfer molding process at 75 percent of the full molding time.

The simulation predicted voiding in the central portion of a bottom cavity, and the experiment confirmed that result. The actual results also had four out of 96 units with conspicuous entrapped voids for a worst-case condition (0.016″ die thickness, 0.011″ lead frame downset and high-viscosity mold compound), while no voids were found in 96 units molded with better process conditions (0.010″ die thickness, 0.005″ lead frame downset and low-viscosity mold compound). This confirms the trends predicted by the DOE exercise.


From the above analysis and short shot studies, it can be concluded that varying the transfer molding process conditions cannot eliminate the air trap completely during the transfer molding process. The air traps could be caused in part by the package design and viscosity of mold compound in this study.

Based on the studies, some conclusions can be made:

  • Downset, die thickness, mold compound viscosity and their interaction effects are the dominating factors for air trap formation.
  • The mold temperature and transfer time do not significantly affect the entrapped voids during the transfer molding process.
  • Optimization of downset, die thickness and mold compound viscosity is critical to achieve balanced cavity filling.
  • Thinner die and smaller downset to reduce voiding must be implemented carefully because they could impact coplanarity and induce reliability issues. The balance between the moldability and reliability should be achieved before mass production starts.
  • The virtual DOE with the application of the flow pattern and air trap index could help capture the most influential factors affecting void formation and provide the possible response surface to minimize the risk of void generation.

T.Y. Lin, formerly of Agere Systems Singapore Pte. Ltd., may be contacted via e-mail at [email protected].


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