Molecular modeling of temperature dependence of solubility parameters for amorphous polymers

A molecular modeling strategy is proposed to describe the temperature (T) dependence of solubility parameter (δ) for the amorphous polymers which exhibit glass-rubber transition behavior. The commercial forcefield “COMPASS” is used to support the atomistic simulations of the polymer. The temperature dependence behavior of δ for the polymer is modeled by running molecular dynamics (MD) simulation at temperatures ranging from 250 up to 650 K. Comparing the MD predicted δ value at 298 K and the glass transition temperature (Tg) of the polymer determined from δ–T curve with the experimental value confirm the accuracy of our method. The MD modeled relationship between δ and T agrees well with the previous theoretical works. We also observe the specific volume (v), cohesive energy (Ucoh), cohesive energy density (ECED) and δ shows a similar temperature dependence characteristics and a drastic change around the Tg. Meanwhile, the applications of δ and its temperature dependence property are addressed and discussed

An AI-Based Adaptive Surrogate Modeling Method for the In-Service Response of UVLED Modules

The response forecasting of in-service complex electronic systems remains a challenge due to its uncertainty. An AI-based adaptive surrogate modeling method, including offline and online learning procedures, is proposed in this research for different systems with significant variety. The offline learning aims to abstract the knowledge from the known information and represent it as root models. The in-service response is modeled by a linear combination of the online learning of these root models against the continuous new measurement. This research applies a performance measurement dataset of the UVLED modules with considerable deviation to verify the proposed method. Part of the datasets is selected to generate the root models by offline learning, and these root models are applied to the online learning procedures for the adaptive surrogate model (ASM) of the different systems. The results show that after approximately 10 online learning iterations, the ASM achieves the capability of predicting 1000 h of response

Solder joint reliability risk estimation by AI-assisted simulation framework with genetic algorithm to optimize the initial parameters for AI models

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.Electronic Components, Technology and Material