A physics-based analytical model is proposed in order to predict the temperature profile during metal additive manufacturing (AM) processes, by considering the effects of temperature history in each layer, temperature-sensitivity of material properties and latent heat. The moving heat source analysis is used in order to predict the temperature distribution inside a semi-infinite solid material. The laser thermal energy deposited into a control volume is absorbed by the material thermodynamic latent heat and conducted through the contacting solid boundaries. The analytical model takes in to account the typical multi-layer aspect of additive manufacturing processes for the first time. The modeling of the problem involving multiple layers is of great importance because the thermal interactions of successive layers affect the temperature gradients, which govern the heat transfer and thermal stress development mechanisms. The temperature profile is calculated for isotropic and homogeneous material. The proposed model can be used to predict the temperature in laser-based metal additive manufacturing configurations of either direct metal deposition or selective laser melting. A numerical analysis is also conducted to simulate the temperature profile in metal AM. These two models are compared with experimental results. The proposed model also well captured the melt pool geometry as it is compared to experimental values. In order to emphasize the importance of solving the problem considering multiple layers, the peak temperature considering the layer addition and peak temperature not considering the layer addition are compared. The results show that considering the layer addition aspect of metal additive manufacturing can help to better predict the surface temperature and melt pool geometry. An analysis is conducted to show the importance of considering the temperature sensitivity of material properties in predicting temperature. A comparison of the computational time is also provided for analytical and numerical modeling. Based on the obtained results, it appears that the proposed analytical method provides an effective and accurate method to predict the temperature in metal AM.

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.

For electronic packaging structure, there are many design parameters that will affect its reliability performance, using experimental way to obtain the reliability result will take a considerable amount of time. Therefore, how to shorten the design time becomes a critical issue for new electronic packaging structure development. This research will combine artificial intelligence (AI) and simulation technology to assess the long-term reliability of wafer level packaging (WLP). A simulation technology using finite element method (FEM) with appropriate mechanics theories has been validated by multiple experiments will replace the experiment to create reliability results for different WLP structures. After a big WLP structure-reliability database created, this study will apply artificial neural network (ANN) theory to analyze this database and obtains a regression model for structure-reliability relationship of WLP. Once the regression model is established and validated, the WLP geometry, such as pad size, die and buffer layer thickness, and solder volume, etc. can be simply entered, and then the WLP reliability results can be immediately obtained through the ANN regression model.

We applied an artificial intelligence (AI) algorithm, the random forest (RF) model, using finite element simulations to predict the reliability life of wafer-level packaging (WLP). Due to rapid growth and an increasingly fast cycle pace of integrated circuits, it is imperative to shorten the development time of electronic packaging. This study focuses on packaging reliability analysis and prediction. In recent years, package reliability analysis has been performed using finite element method simulations, which reduce the required number of accelerated thermal cyclic tests. Compared with conventional ball grid array-type packaging, WLP has become the mainstream packaging type due to its small form factor, batch-type manufacturing process and low cost. We applied the RF model, a machine learning algorithm, to predict the reliability life of WLP. The finite element procedure, theory and mesh size were validated by a set of experiments, and a large dataset was generated for AI training purposes through the finite element simulations. The RF method was built using Python®. A fast and robust reliability assessment AI model of WLP can be achieved once the AI training accuracy is located within the target range; the designer only needs to input the geometry of each WLP component to obtain the reliability life cycle. WLP structural optimization can thus be easily achieved. The AI model also significantly shortens the design cycle to meet current design demands.

Light emitting diode (LED) with a long lifetime, low power consumption, and low pollution has been successfully applied in many products. However, due to its low electro-optical conversion efficiency, high percentage of input power transformed to redundant heat, thus increasing the LED temperature. This phenomenon decreases the luminous flux, changing light color, and useful life span of LED. Therefore, thermal management becomes an important issue in high power LED. In this paper, the variation of luminous flux and light color for different LED lighting modules under long time operation has been measured and discussed. In addition, a detailed finite element model of LED lighting module, MR-16, with a corresponding input power and suitable boundary conditions is established by using the ANSYS finite element analysis program. Furthermore, to validate the simulation results, the current-voltage-temperature method for characterization of a diode is utilized to measure the junction temperature of LED chip indirectly and compare with simulation results. After the simulation is validated, various thermal performance assessments under the different design parameters of the LED package and lighting module are also investigated in this paper. The methodology and analysis results of this paper can provide a guideline for the LED lighting module such as MR-16 design in the future.

This paper presents a physics-based analysis to quantitatively describe the effects of grain size, grain boundaries, and crystallographic orientation on the flow stress of the polycrystalline material and thereby on the cutting and thrust forces. The model has been experimentally validated, in terms of the force intensities and sensitivities to microstructure attributes such as the grain size and the misorientation by comparing the forces to measured data in micromachining of polycrystalline silicon carbide (p-SiC). Molecular dynamics (MD) simulations are performed to explore the effects of grain boundaries and misorientation and to validate the modeling analysis in the context of resulting force ratios.