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

Materials

Gouqi Zhang¹  Xuejun Fan²  Cadmus Yuan³ 

[1] Department of Electronic Components, Technology, and Materials, Delft University of Technology, 2628 CD Delft, The Netherlands;Department of Mechanical Engineering, Lamar University, Beaumont, TX 77710, USA;Department of Mechanical and Computer-Aided Engineering, Feng Chia University, Taichung 407082, Taiwan;

关键词: solder joint fatigue risk estimation;    wafer level chip-scaled packaging;    artificial neural network;    recurrent neural network;    generic algorithm;    principle component analysis;   

DOI  :  10.3390/ma14174835

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown