Thermal analysis of continuous and patterned multilayer films in the presence of a nanoscale hot spot

Thermal responses of multilayer films play essential roles in state-of-the-art electronic systems, such as photo/micro-electronic devices, data storage systems, and silicon-on-insulator transistors. In this paper, we focus on the thermal aspects of multilayer films in the presence of a nanoscale hot spot induced by near field laser heating. The problem is set up in the scenario of heat assisted magnetic recording (HAMR), the next-generation technology to overcome the data storage density limit imposed by superparamagnetism. We characterized thermal responses of both continuous and patterned multilayer media films using transient thermal modeling. We observed that material configurations, in particular, the thermal barriers at the material layer interfaces crucially impact the temperature field hence play a key role in determining the hot spot geometry, transient response and power consumption. With a representative generic media model, we further explored the possibility of optimizing thermal performances by designing layers of heat sink and thermal barrier. The modeling approach demonstrates an effective way to characterize thermal behaviors of micro and nano-scale electronic devices with multilayer thin film structures. The insights into the thermal transport scheme will be critical for design and operations of such electronic devices

Optimization of unequal-active-and-passive-area piezoelectric unimorph cantilevers with collisions for ultra-thin keyboard design

The purpose of this study is to optimize the design of piezoelectric unimorph cantilevers, for ultra-thin keyboard design, so that the first resonant frequency is located in the sensitive frequency range and the first resonant amplitude is above the perception threshold of human hands for vibratory stimulus. The piezoelectric unimorphs used in this study have unequal active and passive areas. Simulations and experiments were first compared to find the effects of the dimensions on the first resonant frequency and displacement frequency response without collisions. A finite element model with collisions based on the verified boundary conditions was then built. Both the experiment data and simulation data was combined to build a regression model to predict the first resonant frequency with collisions for ultra-thin keyboard design. This study can help designers quickly design a vibrotactile device, in the early design stage

Ensemble of heterogeneous flexible neural trees using multiobjective genetic programming

Machine learning algorithms are inherently multiobjective in nature, where approximation error minimization and model's complexity simplification are two conflicting objectives. We proposed a multiobjective genetic programming (MOGP) for creating a heterogeneous flexible neural tree (HFNT), tree-like flexible feedforward neural network model. The functional heterogeneity in neural tree nodes was introduced to capture a better insight of data during learning because each input in a dataset possess different features. MOGP guided an initial HFNT population towards Pareto-optimal solutions, where the final population was used for making an ensemble system. A diversity index measure along with approximation error and complexity was introduced to maintain diversity among the candidates in the population. Hence, the ensemble was created by using accurate, structurally simple, and diverse candidates from MOGP final population. Differential evolution algorithm was applied to fine-tune the underlying parameters of the selected candidates. A comprehensive test over classification, regression, and time-series datasets proved the efficiency of the proposed algorithm over other available prediction methods. Moreover, the heterogeneous creation of HFNT proved to be efficient in making ensemble system from the final population

Measurement of Nonlinear Poisson’s Ratio of Thermoplastic Polyurethanes under Cyclic Softening Using 2D Digital Image Correlation

Thermoplastic polyurethanes (TPUs) and other elastomers are widely used in many applications for the advantages they provide in terms of high elasticity, lightness, resistance to breakage, and impact resistance. These materials exhibit strong hysteresis in the large strain stress-strain behavior, known as cyclic softening or the Mullins effect. Despite the extensive studies on this phenomenon and the importance of Poisson’s ratio, how the Poisson’s ratio of these materials changes during cyclic uniaxial tests is still unclear. Here, we measure the nonlinear Poisson’s ratio of TPU and investigate its correlation with cyclic softening using two-dimensional digital image correlation (2D-DIC) combined with the reference sample compensation (RSC) method. This accuracy-enhanced method can effectively eliminate the measurement errors induced by the unavoidable out-of-plane displacements and lens distortion. We find that the Poisson’s ratio of TPUs also exhibits large hysteresis in the first cycle and then approaches a steady state in subsequent cycles. Specifically, it starts from a relatively low value of 0.45 ± 0.005 in the first loading, then increases to 0.48 ± 0.005 in the first unloading, and remains largely constant afterward. Such a change in the Poisson’s ratio results in a slight volume increase (≈1%) at a maximum strain of 17.5%. Our findings are useful for those who use finite element method to analyze the mechanical behavior of TPU, and shed new light on understanding the physical origin of cyclic softening

Thermal analysis of continuous and patterned multilayer films in the presence of a nanoscale hot spot

Thermal responses of multilayer films play essential roles in state-of-the-art electronic systems, such as photo/micro-electronic devices, data storage systems, and silicon-on-insulator transistors. In this paper, we focus on the thermal aspects of multilayer films in the presence of a nanoscale hot spot induced by near field laser heating. The problem is set up in the scenario of heat assisted magnetic recording (HAMR), the next-generation technology to overcome the data storage density limit imposed by superparamagnetism. We characterized thermal responses of both continuous and patterned multilayer media films using transient thermal modeling. We observed that material configurations, in particular, the thermal barriers at the material layer interfaces crucially impact the temperature field hence play a key role in determining the hot spot geometry, transient response and power consumption. With a representative generic media model, we further explored the possibility of optimizing thermal performances by designing layers of heat sink and thermal barrier. The modeling approach demonstrates an effective way to characterize thermal behaviors of micro and nano-scale electronic devices with multilayer thin film structures. The insights into the thermal transport scheme will be critical for design and operations of such electronic devices

Video of fabricated soft light bulbs from Forming three-dimensional closed shapes from two-dimensional soft ribbons by controlled buckling

This video demonstrates a lamp consists of several "soft light bulbs." The light bulbs were designed and fabricated by the method proposed in this paper

Video of computer simulation from Forming three-dimensional closed shapes from two-dimensional soft ribbons by controlled buckling

This video shows the finite element simulations of the buckling process of an elastic ribbon. We consider nonlinearity due to large deformation and assume linearly elastic material properties

Designing Bioinspired Composite Structures via Genetic Algorithm and Conditional Variational Autoencoder

Designing composite materials with tailored stiffness and toughness is challenging due to the massive number of possible material and geometry combinations. Although various studies have applied machine learning techniques and optimization methods to tackle this problem, we still lack a complete understanding of the material effects at different positions and a systematic experimental procedure to validate the results. Here we study a two-dimensional (2D) binary composite system with an edge crack and grid-like structure using a Genetic Algorithm (GA) and Conditional Variational Autoencoder (CVAE), which can design a composite with desired stiffness and toughness. The fitness of each design is evaluated using the negative mean square error of their predicted stiffness and toughness and the target values. We use finite element simulations to generate a machine-learning dataset and perform tensile tests on 3D-printed specimens to validate our results. We show that adding soft material behind the crack tip, instead of ahead of the tip, tremendously increases the overall toughness of the composite. We also show that while GA generates composite designs with slightly better accuracy (both methods perform well, with errors below 20%), CVAE takes considerably less time (~1/7500) to generate designs. Our findings may provide insights into the effect of adding soft material at different locations of a composite system and may also provide guidelines for conducting experiments and Explainable Artificial Intelligence (XAI) to validate the results