Driven by the increasing demand for high-power systems, ceramic substrates have received more attention for handling higher power density. Warpage in active metal brazed (AMB) ceramic substrate becomes a critical issue as it can deteriorate the reliability performance. This study comprises three phases, including investigation of the cause of the warpage, validation of the proposed model, and optimization for effective warpage management. At first, the coefficient of thermal expansion (CTE) and yield strength of the copper (Cu) layer in AMB were characterized and adopted in a two-dimensional (2D) finite element model. The evolution of simulated strain and moments revealed the cause of the warpage during the manufacturing processes. Furthermore, the 2D model was extended to a three-dimensional (3D) model. The finite element method (FEM) and experiments were conducted on different heat treatment conditions for 3D model validation. The validated 3D model was applied to carry out a design of experiments (DoEs) for design optimization to reduce the warpage. Consequently, the factor analysis in DoEs was demonstrated by different pattern designs using subtractive milling techniques.@en

This study demonstrates a breakdown analysis of the dynamics of a liquid crystal elastomer (LCE) including quality check, geometric measurement, thermal characterization, and comparison of heat- and light-induced contractions. A blue light-responsive acrylate side chain LCE with 1% azobenzene dye was characterized. From a classical viewpoint, photo-thermal contraction is considered a dominating effect, while direct photo-mechanical deformation can be neglected due to a low dye percentage. However, the findings of this research suggest that a low percentage of azobenzene dye does not necessarily lead to heat-dominating dynamics of LCE. This phenomenon has not yet been quantitatively studied before. The approach reported in this Letter can potentially be used to extract the data to improve the dynamics models of light-driven LCEs.

@en

The nano-copper particles are widely used in the sintering processes of packaging wide bandgap semiconductors. Despite the significant success in the industry, the mechanism bridging the sintering process to the mechanical properties of sintered nano-copper is not yet well-modeled. In this paper, the impacts of different sintering temperatures and initial porosities caused by different stacking patterns on the uniaxial tensile performance of the sintered layer were studied via a molecular dynamics approach. Two stacking patterns, simple cubic and face-centered cubic, were simulated, respectively. Evolution of their structure at temperatures of 300, 400, 500, and 600 K were simulated as the sintering process. Afterward, the sintered structures were subjected to uniaxial tensile with rates of 0.01 and 0.04 Å/ps at different temperatures to compare the mechanical properties. The results show that the sintering rate and density of the sintered structure increase with a higher temperature. However, the tensile strength of the sintered structure is less relevant to the difference in stacking pattern. This study proves that porosity has a greater effect on sintering quality.@en

As a critical part of speeding up industrial electrification, power electronics, and its packaging technology are undergoing rapid development. Cu nanoparticle sintering technology has therefore received extensive attention for its excellent performance in the die-attachment layer, where the mechanical properties are essential to be known for design for reliability. Both sintering and subsequent nanoindentation were studied by simulation. The effect of porosity on the nanoindentation response was investigated by setting different initial packing densities. In addition, the impact of indenter size and indentation positions on the nanoindentation response were discussed. The nanoindentation behaviors were studied by extracting loading-displacement (P-h) curves and calculating elastic modulus and hardness. In addition, the microstructure evolution was adopted using atomic configuration to study the nanoindentation mechanism. This work offers valuable insights into the Cu sinter paste preparation phase for sintering technology.@en

This paper presents a convenient and efficient method to predict the mechanical solutions of a laminated Liquid Crystal Elastomers (LCEs) system subjected to combined thermo-mechanical load, based on a back propagation (BP) neural network which is trained by machine learning from a database established by analytical solutions. Firstly, the general solutions of temperature, displacement, and stress of any single layer in the LCEs system are obtained by solving the two-dimensional (2D) governing equations of both heat conduction and thermoelasticity. Then, the unknown coefficients in above general solutions are determined by a transfer-matrix method based on the continuity condition at the interface of adjacent layers and the combined thermo-mechanical loads condition at the surface of the LCEs system. The formula derivation and calculator program are verified through convergence studies and comparisons with FEM results. Finally, a database with displacements of LCEs system in a temperature field subjected to 561 sets of mechanical loads is established based on the presented analytical model. The BP neural network based on above database is further applied to establish the relationship between deformation and mechanical load to predict the elastic deformation of the LCEs system in a temperature field subjected to a mechanical load. Moreover, the BP network can also inverse the coefficients of mechanical load which induces the specific deformation in a temperature field. The numerical examples show that: (1) The deformation of a laminated LCEs system due to thermal load is limited within the range of human temperature changes from 36 °C to 40 °C. (2) The thickness of the LCE is a sensitive parameter on the deformation at the bottom surface of the system. (3) The accuracy of predicted displacements induced by the thermo-mechanical load and the inversed mechanical load based on deformation of the LCEs system in a temperature field using BP neural network reaches 99.6% and 98.5% respectively.@en

Some atrial contractile assist devices applied on the heart surface can be regarded as a laminated Liquid crystal elastomer (LCE) plate under steady temperature loads and a contact mechanical force. An exact solution for the deformation of the laminated LCE plate under combined thermal and mechanical loads is derived by solving the three-dimensional (3D) equilibrium equations including heat conduction and thermoelastic theory. The validity of mathematical formula and computer programming is proved by convergence and comparison examples with finite element method (FEM). In order to simplify the complex calculation of exact solution, a back propagation neural network (BPNN) is further trained with a database containing 9504 sets of thermo-mechanical load conditions and their corresponding deformation which is solved by the exact solutions. Then the deformations of LCE plate subject to combined thermo-mechanical load can be predicted by this BP neural network instead of complex numerical calculation. Moreover, it is also applied to inverse the contact mechanical force at the bottom surface of LCE plate with a given deformation and temperature conditions. The results show that: (1) The results from the exact theoretical solution are in consistence with that from FEM but have a higher computational efficiency and stability; (2) The deformation of the laminated plate is more sensitive to the layered thickness of LCE than the variation of the temperature; (3) 3-D elasticity solutions of a laminated LCE plate under the combined thermos-mechanical load can be effectively predicted by a trained BP neural network.

@en