Solder Joint Reliability Modeling by Sequential Artificial Neural Network for Glass Wafer Level Chip Scale Package

Yuan, Chong; Lee, Chang-Chi

doi:10.1109/access.2020.3014156

Cited by 22 publications

(12 citation statements)

References 18 publications

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“…However, the results of simulation analysis are highly dependent on the individual researcher, and the results are usually inconsistent between simulations. In order to overcome this problem, the present work comparatively reviews an artificial intelligence (AI) approach in which electronic packaging design using a machine learning algorithm [ 19 , 20 ]. The use of machine learning for the analysis of electronic packaging reliability is the best way to obtain a reliable prediction result and meet the time-to-market demand.…”

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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“…Because ANN and RNN learnings are more robust due to the GA, these neural networks are suitable for generating response surfaces, as seen in Figure 3g. The predictability of the neural network model enables the exploration of the domain that is outside the training domain (the FEM domain) at a certain range due to the contribution of the nonlinear activation functions [3]. Moreover, due to the continuity of the neural network model, these models are feasible for the optimization procedure.…”

Section: Discussionmentioning

confidence: 99%

“…As illustrated in Figure 1, there is one pair of parents with three genes. Eight (= 2 3 ) offsprings are generated by the recombination of the parents' gene, which is the definition of the crossover operator.…”

Section: Theorymentioning

confidence: 99%

“…In this section, a practical engineering case will be applied to analyze the capability of GA and PCA to generate the initial parameters of the ANN and RNN. However, due to the multiphysical and multiscaled characteristics of the engineering questions, this research obeys a reliable AI-assisted simulation framework [1][2][3], illustrated in Figure 3, to improve the predicting accuracy of the AI model. Figure 4 shows the G-WLCSP structure [14], where the IC is placed on a glass substrate with metal traces and solder bumps Figure 4a for redistribution purposes.…”

Section: The Ai-assisted Simulation Framework and Fe Datasets Prepara...mentioning

confidence: 99%

“…However, these sequential NN methods consider the recurrent parameters and iterations that induce learning difficulties and require considerable computation resources. Yuan and Lee [3] applied the sequential NN to model the time-dependent nature of the solder joint fatigue, and the average error norms below 1.213 × 10 −4 were achieved. Yuan et al [4] developed a gated neural network technique to learn the performance shifting of the solid-state lighting (SSL) lamp over time.…”

Section: Introductionmentioning

confidence: 99%

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Solder Joint Reliability Risk Estimation by AI-Assisted Simulation Framework with Genetic Algorithm to Optimize the Initial Parameters for AI Models

2021

Self Cite

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Solder joint fatigue is one of the critical failure modes in ball-grid array packaging. Because the reliability test is time-consuming and geometrical/material nonlinearities are required for the physics-driven model, the AI-assisted simulation framework is developed to establish the risk estimation capability against the design and process parameters. Due to the time-dependent and nonlinear characteristics of the solder joint fatigue failure, this research follows the AI-assisted simulation framework and builds the non-sequential artificial neural network (ANN) and sequential recurrent neural network (RNN) architectures. Both are investigated to understand their capability of abstracting the time-dependent solder joint fatigue knowledge from the dataset. Moreover, this research applies the genetic algorithm (GA) optimization to decrease the influence of the initial guessings, including the weightings and bias of the neural network architectures. In this research, two GA optimizers are developed, including the “back-to-original” and “progressing” ones. Moreover, we apply the principal component analysis (PCA) to the GA optimization results to obtain the PCA gene. The prediction error of all neural network models is within 0.15% under GA optimized PCA gene. There is no clear statistical evidence that RNN is better than ANN in the wafer level chip-scaled packaging (WLCSP) solder joint reliability risk estimation when the GA optimizer is applied to minimize the impact of the initial AI model. Hence, a stable optimization with a broad design domain can be realized by an ANN model with a faster training speed than RNN, even though solder fatigue is a time-dependent mechanical behavior.

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Machine Learning‐Based Solution for Thermomechanical Analysis of MMIC Packaging

Kang

Lee

Kim

et al. 2022

Adv Materials Technologies

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Thermo-mechanical analysis of monolithic microwave integrated circuit (MMIC) packaging is essential to guarantee the reliability of radio frequency/microwave applications. However, a method for fast and accurate analysis of MMIC packaging structures has not been developed. Here, we demonstrate a machine learning (ML)-based solution for thermo-mechanical analysis of MMIC packaging. This MLbased solution analyzes temperature and thermal stresses considering 13 design parameters categorized into material properties, geometric characteristics, and thermal boundary conditions. Finite element simulation with the Monte Carlo method is utilized to prepare 40,000 data samples for supervised learning and validation of the ML solution, and a laser-assisted thermal experiment veri es the accuracy of the simulation. After data preparation, regression tree ensemble and arti cial neural network (ANN) learning models are investigated. The results indicate that the ANN models accurately predict the temperature and thermal stresses, showing a 1.69 % minimum error. Finally, the developed ML solution is deployed as a web application format for facile approaches. We believe that this study will provide a guideline for developing ML-based solutions in chip packaging design technology.

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Solder Joint Reliability Modeling by Sequential Artificial Neural Network for Glass Wafer Level Chip Scale Package

Cited by 22 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

Solder Joint Reliability Risk Estimation by AI-Assisted Simulation Framework with Genetic Algorithm to Optimize the Initial Parameters for AI Models

Machine Learning‐Based Solution for Thermomechanical Analysis of MMIC Packaging

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