Reliability Evaluation of Fan-Out Type 3D Packaging-On-Packaging

Wang, Pao-Hsiung; Huang, Yu-Wei; Chiang, Kuo‐Ning

doi:10.3390/mi12030295

Cited by 8 publications

(4 citation statements)

References 18 publications

(23 reference statements)

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“…With the increasing complexity of packaging structures, manufacturing reliability test vehicles, and conducting ATCT experiments have become time-consuming and very expensive processes, the design-on-experiment (DoE) methodology for packaging design is becoming infeasible. As a result of the wide adoption of finite element analysis [ 9 , 10 , 11 , 12 , 13 , 14 , 15 ], accelerated thermal cycling tests are reduced significantly in the semiconductor industry, and package development time and cost are reduced as well. In a 3D WLP model, Liu [ 16 ] applied the Coffin–Manson life prediction empirical model to predict the reliability life of a solder joint within an accurate range.…”

Section: Introductionmentioning

confidence: 99%

An Overview of AI-Assisted Design-on-Simulation Technology for Reliability Life Prediction of Advanced Packaging

et al. 2021

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Several design parameters affect the reliability of wafer-level type advanced packaging, such as upper and lower pad sizes, solder volume, buffer layer thickness, and chip thickness, etc. Conventionally, the accelerated thermal cycling test (ATCT) is used to evaluate the reliability life of electronic packaging; however, optimizing the design parameters through ATCT is time-consuming and expensive, reducing the number of experiments becomes a critical issue. In recent years, many researchers have adopted the finite-element-based design-on-simulation (DoS) technology for the reliability assessment of electronic packaging. DoS technology can effectively shorten the design cycle, reduce costs, and effectively optimize the packaging structure. However, the simulation analysis results are highly dependent on the individual researcher and are usually inconsistent between them. Artificial intelligence (AI) can help researchers avoid the shortcomings of the human factor. This study demonstrates AI-assisted DoS technology by combining artificial intelligence and simulation technologies to predict wafer level package (WLP) reliability. In order to ensure reliability prediction accuracy, the simulation procedure was validated by several experiments prior to creating a large AI training database. This research studies several machine learning models, including artificial neural network (ANN), recurrent neural network (RNN), support vector regression (SVR), kernel ridge regression (KRR), K-nearest neighbor (KNN), and random forest (RF). These models are evaluated in this study based on prediction accuracy and CPU time consumption.

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Section: Introductionmentioning

confidence: 99%

An Overview of AI-Assisted Design-on-Simulation Technology for Reliability Life Prediction of Advanced Packaging

et al. 2021

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“…However, this advancement has also brought up a significant technical challenge, in which the physical limit of transistor scaling creates enormous difficulties in continuing on the path of Moore’s Law [ 1 ]. In the post-Moore era, the concept of “More than Moore” based on heterogeneous integration using new packaging technologies [ 2 , 3 , 4 , 5 , 6 , 7 , 8 ] is becoming more critical and demanding. Flip-chip chip-scale packaging (FCCSP) possesses the capacity of a high I/O count, miniaturization, and great electrical performance, and thus becomes one of the promising packaging solutions for realizing heterogeneous system integration.…”

Section: Introductionmentioning

confidence: 99%

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

Cheng

Liu

2023

Micromachines

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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.

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“…Electrical–thermal responses of vias containing glass interposers, the temperature dissipation ability of Cu-filled via and carbon nano tube(CNT)-filled via were analyzed, and the CNT-filled via showed 30 °C lower temperature compared with the Cu-filled via in [ 32 ]. Different mechanical modeling approaches were utilized to investigate the solder joint reliability of TGV-interposer-based fan-out packaging in [ 33 ]. Warpage characterization of the glass interposer with chemically shrunk epoxy molding compound (EMC) were estimated through experimental and simulation work in [ 34 ].…”

Section: Introductionmentioning

confidence: 99%

Reliability Assessment of Thermocompressed Epoxy Molding Compound through Glass via Interposer Architecture by the Submodeling Simulation Approach

et al. 2022

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In glass interposer architecture and its assembly process, the mechanical responses of interposer structure under thermocompression process-induced thermal loading and generated shrinkage of molding material are regarded as a major reliability issue. Thousands of metal-filled via are involved in glass interposers and are regarded as a potential risk that can lead to cracking and the failure of an entire vehicle. In this study, a finite element-based submodeling approach is demonstrated to overcome the complexity of modeling and the relevant convergence issue of interposer architecture. Convergence analysis results revealed that at least four via pitch-wide regions of a local simulation model were needed to obtain the stable results enabled by the submodeling simulation approach. The stress-generation mechanism during thermocompression, the coefficient of thermal expansion mismatch, and the curing process-induced shrinkage were separately investigated. The critical stress location was explored as the outer corner of the chip, and the maximum first principal stress during the thermocompression process generated on the chip and glass interposer were 34 and 120 MPa, respectively.

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Reliability Evaluation of Fan-Out Type 3D Packaging-On-Packaging

Cited by 8 publications

References 18 publications

An Overview of AI-Assisted Design-on-Simulation Technology for Reliability Life Prediction of Advanced Packaging

An Overview of AI-Assisted Design-on-Simulation Technology for Reliability Life Prediction of Advanced Packaging

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

Reliability Assessment of Thermocompressed Epoxy Molding Compound through Glass via Interposer Architecture by the Submodeling Simulation Approach

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