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AI-Assisted Design-on-Simulation for Life Prediction
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.
2. Finite Element Method for WLP
3. Machine Learning
3.1. Establishment of Dataset
3.2. ANN Model
3.3. RNN Model
3.4. SVR Model
This regression method evolved from the support vector machine algorithm. It transforms data to high-dimensional feature space and adapts the ε-insensitive loss function (Equation (4)) to perform the linear regression in feature space (Equation (5)). In this regression method, the norm value of w is also minimized to avoid the overfitting problem. In other words, f(X,w), which is the function of the SVR model, will be as flat as possible. The SVR concept is illustrated in Figure 10. The data points outside the ε-insensitive zone are called support vectors, and two slack variables, ξi and ξ∗i, are used to record the loss of each support vector. Thus, the whole SVR problem can be seen as an optimization problem (Equation (6)).
3.5. KRR Model
KRR combines ridge regression with the kernel “trick”. This model can learn a linear function in the space induced by the respective kernel and the dataset. Nonlinear functions in the original space can be used by the nonlinear kernels. The KRR algorithm also analyzes several kernels such as the RBF kernel, sigmoid kernel, and polynomial kernel to find the suitable kernel function for the WLP nonlinear dataset.
The KRR is possibly the most elementary algorithm that can be kernelized to ridge regression . The classic method is used to minimize the quadratic cost, as shown in Equation (8). However, for the nonlinear dataset, the lower-dimensional feature space replaces the higher-dimensional feature space; that is, Xi→Φ(Xi). To convert lower-dimensional space to higher-dimensional space, the predictive model undergoes overfitting. Hence, to avoid overfitting, this function requires regularization.
Hence, Equation (11) is very simple and more flexible due to introducing kernel function K, λ is the regularize factor with the identity matrix I, and y is the response variable. This model can also avoid both model complexity and computational time.
3.6. KNN Model
The KNN model is a statistical tool for estimating the value of an unknown point based on its nearest neighbors . The nearest neighbors are usually calculated as the points with the shortest distance to the unknown point . Several techniques are used to measure the distance between the neighbors. Two simple techniques are used in this study: the Euclidean distance function d(x,y), provided in Equation (12), and the Manhattan distance function d(x,y), provided in Equation (13).
3.7. The RF Regression Model
3.8. Training Methodology
3.8.1. Data Preprocessing
3.8.3. Grid Search Technique
This entry is adapted from 10.3390/ma14185342
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