Warpage Prediction Research Articles

From MEMS Strip to MEMS Unit: a Comprehensive Simulation Approach to Warpage

The paper suggests a simulation flow methodology suitable for MEMS package designers that can help R&D teams to develop industrially feasible products by achieving consistent warpage prediction results, both at unit and strip levels. Striplevel- wise, one of the most important findings of this activity is the direct impact of raw material selection. Unit-level-wise, it allows checking a priori and in different conditions (inside the socket or mounted on a PCB for example) stress distribution on different elements of the assembled MEMS unit.

Effective Macroscopic Thermomechanical Characterization of Multilayer Circuit Laminates for Advanced Electronic Packaging

Laminate substrates in advanced IC packages serve as not only the principal heat dissipation pathway but also the critical component governing the thermomechanical performance of advanced packaging technologies. A solid and profound grasp of their thermomechanical properties is of crucial importance to better understand IC packages’ thermomechanical behavior. This study attempts to introduce a subregion homogenization modeling framework for effectively and efficiently modeling and characterizing the equivalent thermomechanical behavior of large-scale and high-density laminate substrates comprising the non-uniform distribution and non-unidirectional orientation of tiny metal traces. This framework incorporates subregion modeling, trace mapping and modeling, and finite element analysis (FEA)-based effective modeling. In addition, the laminates are macroscopically described as elastic orthotropic or elastic anisotropic material. This framework is first validated with simple uniaxial tensile and thermomechanical test simulations, and the calculation results associated with these two effective material models are compared with each other, as well as with those of two existing mixture models, and direct the detailed FEA. This framework is further tested on the prediction of the process-induced warpage of a flip chip chip-scale package, and the results are compared against the measurement data and the results of the whole-domain modeling-based effective approach and two existing mixture models.

Enhancing predictability of thermal warpage by applying temperature-dependent Poisson's ratio of epoxy molding compound

The warpage behavior of electronic packages has become complicated in recent years owing to the miniaturization and heterogeneous integration technologies. Therefore, stress analyses through warpage simulations have become increasingly challenging. Understanding the thermo-mechanical behavior of epoxy-based packaging materials enhances the predictability of warpage simulation and helps in designing mechanically reliable electronic packages. However, the necessity and importance of an accurate database of thermo-elastic properties, especially the temperature dependency of Poisson's ratio, still has been overlooked for warpage prediction. In this study, the warpage prediction accuracy of a molded wafer was dramatically improved by measuring and applying the temperature dependency of elastic modulus and Poisson's ratio. If the temperature dependency of Poisson's ratio and elastic modulus is not considered, the warpage prediction error increases by 4.3 and 8.1 times for the 25 °C and 250 °C warpage, respectively. Surprisingly, even without considering only the temperature dependency of Poisson's ratio, the prediction error increased by 3.3 and 4.4 times for the 25 °C and 250 °C warpage, respectively. These results indicate that Poisson's ratio significantly affects the coefficient of thermal expansion-induced warpage behavior in the form of the biaxial modulus.

Development of ANN-Based Warpage Prediction Model for FCCSP via Subdomain Sampling and Taguchi Hyperparameter Optimization.

This study aims to establish an accurate prediction model using artificial neural networks (ANNs) to effectively and efficiently predict the process-induced warpage of a flip-chip chip-scale package (FCCSP). To enhance model performance, a novel subdomain-based sampling strategy and Taguchi hyperparameter optimization are proposed in the ANN algorithm. To simulate the warpage behavior the FCCSP during fabrication, a process modeling approach is proposed, where the viscoelastic behavior of the epoxy molding compound is included, in which the viscoelastic properties are determined using dynamic mechanical measurement. In addition, the temperature-dependent thermal-mechanical properties of the materials in the FCCSP are assessed through thermal-mechanical analysis and dynamic mechanical analysis. The modeled warpage results are verified by the warpage measurement. Next, warpage parametric analysis is performed to identify the key factors most affecting warpage behavior for use in the construction of the warpage prediction model. Moreover, the advantages of the proposed sampling and hyperparameter tuning approaches are proved by comparing with other existing models, and the validity of the developed ANN-based deep learning warpage prediction model is demonstrated through a validation dataset.

Injection over-molding warpage prediction of continuous fiber-reinforced thermoplastic composites considering yarn reorientation

The injection over-molding process combines thermo-stamping of continuous fiber reinforced thermoplastic (CFRTP) laminates and injection of plastic onto the surface of the CFRTP insert. A challenge for this process is to predict the warpage due to the influence of CFRTP insert. This work proposed a simulation method for predicting the warpage considering yarn reorientation induced in the thermo-stamping. In detail, the geometry and yarns reorientation of the deformed CFRTP inserts in thermo-stamping were calculated by a hybrid lamination model. Then the local thermo-mechanical properties were calculated according to the directions of reorientated yarns. Finally, the geometric and material information of CFRTP insert were imported into injection analysis, especially warpage prediction. The simulation method was validated by experiments on centrally injected bilayer plate and stiffened square box. This work highlights the importance of anisotropy induced from thermo-stamping on the final warpage of injection over-molded composite parts.

Effect of critical properties of epoxy molding compound on warpage prediction: A critical review

For the past couple of decades, a considerable amount of time and effort has been spent to enhance the predictability of warpage of semiconductor packages encapsulated by epoxy molding compound (EMC). With advanced computational mechanics techniques and computing hardware, one can simulate almost any kind of packages. Thermo-mechanical properties required for numerical predictions, including the coefficient of thermal expansion (CTE), the glass transition temperature (Tg), and the temperature and time-dependent viscoelastic properties, are routinely measured by commercial tools such as thermo-mechanical analyzer (TMA) and dynamic mechanical analyzer (DMA). In addition, temperature-dependent warpage can be measured readily using commercial tools based on shadow moiré and digital image correlation (DIC). In spite of this considerable effort, accurate prediction remains a challenging task. EMC typically occupies a large portion of package volume, and thus plays a major role in package warpage behavior. This review paper examines the effect of critical EMC properties on warpage behavior. Based on the data and the analyses reported in the literature, the paper addresses three potential causes that make the prediction still difficult, and discusses what should be done to bring the prediction capability to a desired level.

Dynamic warpage simulation of molded PCB under reflow process

PurposeThis study aims to simulate molded printed circuit board (PCB) warpage behavior under reflow temperature distribution. Simulation models are used to estimate dynamic warpage behavior for different form factor sizes.Design/methodology/approachThis study analyzes warpage during the reflow process. The shadow moiré experiment methodology is used to collect data on the dynamic warpage performance of a model with a form factor of 10mm × 10mm × 1mm. The temperature profile with heating from 25°C to 300°C at intervals of 50°C is used, and the sample is made to undergo a cooling process until it reaches the room temperature. Subsequently, ANSYS static structural simulation is performed on similar form factor models to ascertain the accuracy of the simulation results.FindingsResults show that the deformation and total force induced by coefficient of thermal expansion (CTE) mismatch are examined based on the warpage performance of models with different sizes, that is, 45mm × 45mm × 1mm and 45mm × 15mm × 1mm. Compared with the experimental data, the simulated modeling accuracy yields a less than 5% deviation in the dynamic warpage prediction at a reflow temperature of 300°C. Results also reveal that the larger the model, the larger the warpage changes under the reflow temperature.Research limitations/implicationsThe simulated warpage is limited to the temperature and force induced by CTE mismatch between two materials. The form factor of the ball-grid array model is limited to only three different sizes. The model is assumed to be steady, isothermal and static. The simulation adopts homogenous materials, as it cannot accurately model nonhomogeneous multilayered composite materials.Practical implicationsThis study can provide engineers and researchers with a profound understanding of molded PCB warpage, minimal resource utilization and the improved product development process.Social implicationsThe accurate prediction of molded PCB warpage can enable efficient product development and reduce resources and production time, thereby creating a sustainable environment.Originality/valueThe literature review points out that warpage in various types of PCBs was successfully examined, and that considerable efforts were exerted to investigate warpage reduction in PCB modules. However, PCB warpage studies are limited to bare PCBs. To the best of the authors’ knowledge, the examination of warpage in a molded PCB designed with a molded compound cover, as depicted in Figure 3, is yet to be conducted. A molded compound provides strong lattice support for PCBs to prevent deformation during the reflow process, which is a topic of considerable interest and should be explored.

Warpage Prediction of RHCM Crystalline Parts Based on Multi-Layers.

Warpage is a typical defect for injection-molded parts, especially for crystalline parts molded by rapid heat cycle molding (RHCM). In this paper, a prediction method is proposed for predicting the warpage of crystalline parts molded by the RHCM process. Multi-layer models were established to predict warpage with the same thicknesses as the skin-core structures in the molded parts. Warpages were defined as the deformations calculated by the multi-layer models. The deformations were solved using the classical laminated plate theory by Abaqus. A model was introduced to describe the elastic modulus with the influence of temperature and crystallinity. The simulation process was divided into two procedures, before ejection and after ejection. Thermal stresses and thermal strains were simulated, respectively, in the procedure before ejection and after ejection. The prediction results were compared with the experimental results, which showed that the average errors between predicted warpage and average experimental warpage are, respectively, 7.0%, 3.5%, and 4.4% in conventional injection molding (CIM), in RHCM under a 60 °C heating mold (RHCM60), and in RHCM under a 90 °C heating mold (RHCM90).

Increase in Warpage Prediction Accuracy for Glass Filled Polyamide Material (PA66) through Integrative Simulation Approach

The warpage prediction accuracy of the simulation software depends on part geometry, material model and methodology. However, the material model in the existing simulation software’s does not consider factors such as nonlinear mechanical properties, temperature dependent behaviour, viscoelastic behaviour and transient description of warpage leading to less accuracy. Using an integrative simulation approach, BASF has developed Ultrasim® tool to overcome limitations in the material model of existing simulation software. In the new material model thermomechanical properties, stress relaxation behaviour and nonlinear mechanical properties were considered and this new material model is added to Ultrasim® tool. The model also considers time dependent descriptions of the warpage starting from packing phase of the moulding process, followed by actual ejection and cooling. In this paper warpage results predicted through new integrative simulation approach and existing simulation approach are compared with actual experimental results for 50% glass filled polyamide material (Ultramid®A3WG10). The results revealed that warpage values predicted by integrative simulation based Ultrasim® tool are closer to actual experimental results compared to values predicted by existing simulation technologies. Therefore an integrative simulation approach can be used prior to making real parts to reduce manufacturing cost.

Warpage prediction of Injection-molded PVC part using ensemble machine learning algorithm

In this study, the warpage prediction models have been developed for an injection molded PVC component named a drip chamber. Two popular and widely used ensemble machine learning algorithms, namely random forest and gradient boosted regression tree have been used to develop the predictive models. 40 experiments were carried out for various input process parameters to develop the warpage prediction dataset. ANOVA was performed to identify the significant input process parameters. These process parameters were barrel temperature, holding pressure, holding time, mold temperature, and cooling time. The results shows that the mean absolute percentage errors of random forest and gradient boosted regression tree model are 3.25% and 9.37%, respectively. This indicates that random forest ensemble algorithm outperforms gradient boosted model in predicting the warpage of injection molded part. This model allows production managers to monitor injection molding method parameters and regulate the warpage before actual production and with minimum waste.

Advancement in warpage prediction of thermoplastic glass filled material through integrative simulation approach

Warpage during injection moulding is a common plastic defect that decreases the quality of the finished products and impedes the part assembly. Industries are increasingly embracing a virtual moulding approach to predict and rectify the factors causing this defect. The prediction of warpage is a complex process involving factors related to part geometry, material, mould, machine and moulding conditions. However, the existing process simulation models ignore the temperature dependent mechanical properties and the stress relaxation spectrum for viscoelastic materials, thereby resulting in poor accuracy. To overcome these limitations, BASF has introduced Ultrasim® tool which is based on an integrative simulation technology. Recently, a new model based on a material’s thermomechanical properties was developed and added to Ultrasim® tool. This material model was developed by considering temperature dependent, time dependent, non-linear mechanical properties and stress relaxation behaviour of the material. In this paper, the feasibility of this newly developed approach for warpage simulation of glass filled thermoplastic material is tested by simulating warpage results with Ultrasim® and comparing the same with existing warpage simulation results and actual experimental inspection results. The results demonstrate that, warpage prediction accuracy has improved by 12% for one part and by 5% for another part using newly developed integrative simulation approach.

Optimization and prediction of volume shrinkage and warpage of injection-molded thin-walled parts based on neural network

Warpage and volume shrinkage are important indicators of the quality of thin-walled parts during injection molding. In this study, the optimization goals are warpage and volume shrinkage. Design parameters include mold temperature, melt temperature, injection time, holding time, cooling time, and holding pressure. Based on the orthogonal experimental design and response surface experimental design, the MoldFlow software is applied to the simulation of the thin-walled part injection molding process. The importance of various parameters on warpage and volume shrinkage was analyzed by using analysis of variance. Based on the simulation results, a two-layer hidden-layer back propagation (BP) neural network model is established and the genetic algorithm (GA) is used to optimize the weights and thresholds of the back propagation neural network (BPNN) model to reduce warpage and volume shrinkage by optimizing the design parameters significantly. A support vector machine (SVM) combined with GA-BP was used to build a prediction model for predicting warpage and volume shrinkage. Taking the automobile wire harness protection frame as an example, and verified by numerical simulation, the GA-optimized two-layer hidden-layer BP neural network combination method is an effective method for injection molding to reduce warpage and volume shrinkage of thin-walled parts. SVM-BP-GA can accurately provide predictions for optimization goals; the amount of warpage and the volume shrinkage were 0.93% and 1.9%, respectively.

Improvement of warpage prediction through integrative simulation approach for thermoplastic material

In the injection-molded parts, prediction of accurate warpage at initial level becomes mandatory to avoid iterative work of mold modifications. Simulation teams of many organizations are using existing commercial programs for process simulations. Material models in existing simulation technologies are having certain limitations and assumptions, which can regularly result in up to 50% variation of warpage results as compared to the actual physical warpage measurement. The commonly used Moldflow simulation model, for example, ignores temperature-dependent mechanical properties and the stress relaxation spectrum for viscoelastic materials. These assumptions affect the accuracy of the warpage prediction results significantly. To decrease these kinds of variations, BASF extended its Ultrasim® tool which is based on integrative simulation technology. Recently, a newly developed thermomechanical material model with temperature-dependent nonlinear mechanical properties and stress relaxation behavior was added in the Ultrasim. This model has been used in this work to consider the complete transient description of the warpage, which starts at packing phase of the part inside the mold, followed by actual cooling and ejection. In this article, unreinforced semicrystalline polybutylene terephthalate polymer material (Ultradur® B4520) is considered for warpage correlational study. The accuracy of the warpage prediction is compared between the integrative simulation approach, existing warpage simulation method, and the actual experimental inspection results. The result exhibits that the accuracy of the integrative simulation (Ultrasim)-based warpage simulation is relatively better than existing simulation technologies and closer to the actual measurement.

Modeling and Characterization of Cure-Dependent Viscoelasticity of Molded Underfill in Ultrathin Packages

Warpage control of an ultrathin package is critical in the semiconductor industry because of the large volume of molded underfill (MUF) encapsulated in such packages and because of the low stiffness of the entire package structure. The viscoelastic relaxation behavior that is observed during the MUF curing process results in a major warpage change and thus should be investigated. First, the material model of the cure-dependent viscoelasticity of MUF in the time domain and the curing kinetics model of MUF were successfully established based on the characterization results. Then, a finite-element (FE) model integrated with the material model for package warpage prediction was successfully developed. The final prediction results of package warpage obtained using a novel process modeling approach are in agreement with the experimental data measured using thermal shadow Moire. Furthermore, the effects of MUF volume change observed by changing the die size and thickness in an ultrathin package were studied using the warpage prediction model. The results indicate that package warpage can be effectively reduced by decreasing the die size and increasing the die thickness.

Parametric study of warpage in PBGA packages

Plastic ball grid array (PBGA) is one of the most widely used types of chip packages in various electronic devices such as network servers, microcontrollers, and memory devices. As the demand for higher performance electronic devices grows, the I/O densities of PBGA packages are increasing while requiring superior reliability. Warpage induced during the reflow assembly process is one of the crucial factors affecting the thermo-mechanical reliability of PBGA packages; therefore, accurate warpage prediction is an important task for package design processes. In this study, the effects of four geometric factors (the solder bump pitch, package size, molding compound, and substrate thicknesses) of the PBGA package on its warpage are assessed by using parametric finite element analysis. The correlation between PBGA warpage and the four factors is studied using the analysis of variance and the regression method. These results are expected to provide design guidelines for in-house PBGA designers.

Optimizing Glass Fiber Molding Process Design by Reverse Warping.

The purpose of this study is to clarify the influence of changes in glass fiber properties on warpage prediction, and to demonstrate the importance of accurate material property data in the numerical simulation of injection molding. In addition, this study proposes an optimization method based on the reverse warping agent model, in which the thermal conductivity of the plastic material is reduced, and the solidified layer on the surface of the mold is reduced and transferred from the molding material to the mold wall. This means that by the end of the cooling phase, the shrinkage of the molten zone within the component will continue, resulting in warpage. Based on the optimal process parameters, the sensitivity of the warpage prediction to the relationship between the two most important material properties, the glass fiber and holding pressure time, was analyzed. The material property model constants used for numerical simulations can sometimes vary significantly due to inherent experimental measurement errors, the resolution of the test device, or the manner in which the curve fit is performed to determine the model constants. This model has been developed to intelligently determine the preferred processing parameters and to gradually correct the details of the molding conditions. Thus, the cavity is separated in the critical warpage region, and a new cavity geometry with a reverse warped profile is placed into the mold base slot. The results show that the hypothetical and reasonable variation of the glass fiber model constant and the holding pressure time relationship may significantly affect the magnitude of the warpage prediction of glass fiber products. The greatest differences were found as a result of the warping orientation of the glass fiber material.

Study of Warpage Evolution and Control for Six-Side Molded WLCSP in Different Packaging Processes

A six-side molded wafer-level chip scale package (mWLCSP) is a novel and attractive packaging method for smart phone applications due to its lower cost, better electrical performance, and higher long-term reliability. Nevertheless, wafer warpage in different manufacturing processes is a significant issue for the six-side mWLCSP. This article focuses on wafer warpage evolution in different packaging processes and the development of control method for the six-side mWLCSP. A quarter wafer-level finite-element (FE) model and a strip-level FE model were established to simulate the actual packaging processes by element birth and death method and restart technology. Both the FE models were proved quite efficient and accurate for warpage prediction compared to the experimental results. The effects of geometric parameters and material parameters on the maximum warpage value were studied based on the two established FE warpage models, and some recommendations were given for warpage optimization. Based on the above studies, an improved manufacturing process flow for the six-side mWLCSP was presented and proved feasible to control the maximum warpage value under 3 mm. It offers an insight work for the development and warpage control of the six-side mWLCSP and other wafer-level molded packages.

Processing parameters Optimization of Injection Moulding in DN20 Vent of Water Meter Manufacturing

The conventional optimization process in Injection Moulding includes actual shop floor trials in which melt temperature, mould temperature, injection time, injection pressure, pattern, feeder size, shape and location cores, mould layout, gating etc. are changed in each iteration which involves high machining cost, tooling cost, modification cost, melting cost, and transportation cost as well as, materials, energy, time are wasted in each trial until and unless the required results are obtained. Water meter component (DN20 Vent) is designed in CREO 5.0, and then components are 3D printed to cross check the dimensions and also to confirm whether all the other components can be accommodated or not. Then the mould flow analysis will be performed on a water meter components using different materials and changing the processing parameters. The input processing parameters considered are melt temperature, mould temperature and injection time, whereas the responses are warpage, volumetric shrinkage, cycle time and quality prediction. Grey relational analysis is carried out to determine the optimum injection moulding processing parameters.. The effort has been made to minimize the warpage, volumetric shrinkage, cycle time and maximize the quality prediction mould cavity and core for the components are designed in CREO 5.0 and manufactured using P20 tool steel. Then the water meter components are manufactured by inputting the optimal processing parameters in injection moulding machine to achieve high productivity and quality.

Injection mold design of reverse engineering using injection molding analysis and machine learning

Plastic composites are used in vehicle components to improve fuel efficiency. Thus, the warpage of injection-molded plastic parts has become a quality issue. Factors, such as product shape and thickness, resin, and other injection molding conditions, can be modified to improve the warpage problem. However, if these factors are set with no possible adjustments, reverse engineering may be required. Reverse engineering is a difficult process that requires many trials and errors; thus, it is only used as a last resort. With respect to the warpage issue, reverse engineering considers the following: (1) Predicting and (2) modeling the warpage in opposite directions. Autodesk Moldflow Insight accommodates these key considerations, but many researchers are reluctant to use it. Although existing injectionmolding analysis programs are mainly used to predict qualitative results, computer-aided engineering (CAE) for reverse engineering requires quantitative analysis. Hence, the considerations are different from the existing analyses. An error in warpage prediction may lead to a costly mold modification because of the molds' complex structures. Quantitative warpage prediction for reverse engineering depends on process variables; thus, understanding how warpages are affected by uncertain process variables is important to improve the reliability of reverse engineering. Moreover, even if appropriate process variables are set, they cannot be applied due to tolerance in lengths. For this reason, mold shrinkage must be identified before designing a mold. This study conducted injection molding analysis for a radiator tank that uses glass fiber-reinforced plastic using Autodesk Moldflow Insight 2018.2. Data for warpage prediction were generated in accordance with five process variables to identify the relationship between the level of warpage and process variables. CAE also showed the level of mold shrinkage that can reduce warpage. In addition, a predictive model was created using the multilayer perceptron (MLP)- supervised learning technique, which is a deep learning method for artificial neural networks. The predictive model was compared with typical regression models, such as polynomial regression (also known as response surface model), EDT and RBF, to determine the optimal approximation model. The real modeling time for a radiator tank product is 1 h, but the MLP approximation model required only 1 min and 8 s to perform 8530 iterations with a similar reliability.

Minimum Warpage Prediction in Plastic Injection Process using Taguchi Method and Simulation

Plastic materials are used in automobile, electrical and electronic applications, agricultural utilization, household and furniture products, and medical equipments. Among various plastic manufacturing process, injection molding is one of the most commonly used and common methods applied for forming plastic products in the industry. The process requires a molten polymer being injected into a cavity of a mold, which is cooled and the product ejected from the mold. During the Plastic Injection Molding (PIM) process, various defects, such as volumetric shrinkage, warpage, weld line and sink mark can occur. This paper presents a method to minimizing warpage defect on PolyPropylene AZ564 via PIM simulation using Moldflow software. The approach was based on Taguchi method.Through the effectiveness of this proposed method, it is confirmed using simulation by Moldflow software. The effect of the process parameters on the warpage of a motorcycle number plate bracket, is studied using analysis of variance (ANOVA). From the ANOVA, the significant parameters affecting the process are holding time, holding pressure and injection pressure. The result of the Taguchi prediction shown that minimum warpage is 1.078 mm, which is 7.14% and 9.09% different from the simulation result and experiment, respectively.