Abstract
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.
Highlights
Introduction published maps and institutional affilSolder joint reliability is one of the most critical issues for most ball-grid array packaging types
There is no clear statistical evidence that recurrent neural network (RNN) is better than artificial neural network (ANN) in the wafer level chip-scaled packaging (WLCSP) solder joint reliability risk estimation when the genetic algorithm (GA) optimizer is applied to minimize the impact of the initial AI model
The nonlinear material/geometry properties are required for the finite element (FE) modeling to retrieve trustable results, which can be validated by the experimental results
Summary
In GA, the fitness criterion is first defined to quantify the members of the current generation with more compatibility are more likely to generate the population [8,9]. The members higher F values are more the whole offsprings are forming to the generation without any poslikely to generate the population by the crossover and mutation operators. The mutation occurs at the bps level and is mosomes consisted of many genes,the it might induce many duplicated offsprings. When mutation is invoked at certain bps, the representation bpswith will be by the opposite parentto bps These offsprings thereplaced same genes will be removed save computation resource. The mutation operator is first usedgene to make changes will in the genesand of athe member of the bps will generation be replaced by the 9th bps of themember. The principal component analysis (PCA) algorithm is applied to build a super chromosome based on these best. The super chromosome, called the PCA gene, is obtained as the inner product of the best chromosomes and the eigenvector of the first eigenvalue
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