RESUMO
Non-uniform self-heating and temperature hotspots are major concerns compromising the performance and reliability of submicron electronic and optoelectronic devices. At deep submicron scales where effects such as contact-related artifacts and diffraction limits accurate measurements of temperature hotspots, non-contact thermal characterization can be extremely valuable. In this work, we use a Bayesian optimization framework with generalized Gaussian Markov random field (GGMRF) prior model to obtain accurate full-field temperature distribution of self-heated metal interconnects from their thermoreflectance thermal images (TRI) with spatial resolution 2.5 times below Rayleigh limit for 530nm illumination. Finite element simulations along with TRI experimental data were used to characterize the point spread function of the optical imaging system. In addition, unlike iterative reconstruction algorithms that use ad hoc regularization parameters in their prior models to obtain the best quality image, we used numerical experiments and finite element modeling to estimate the regularization parameter for solving a real experimental inverse problem.
RESUMO
Controlling the unit-cell topology of microlattice structures can enable the customization of effective anisotropic material properties. A wide range of properties can be obtained by varying connectivity within the unit cell, which then can be further used to optimize structures specific to applications. A methodology for a systematic generation of microlattice structures is presented that focuses on controlling discrete topology instead of average porosity (as is done in conventional porous media). An algorithm is developed to create valid lattice structures without redundancies from a given set of template nodes. A set of possible permutations of structures from an eight-node cubic octant of a unit cell are generated for evaluation of the degree of anisotropy. Generic models are developed to calculate the effective thermal and mechanical properties as an effect of topology and porosity of the micro-architected structure. The thermal and mechanical anisotropies are investigated for the effective properties of micro-architected materials. A few of the structured materials are fabricated using 3D printing technology and their effective properties characterized. Structures are represented as graphs in the form of adjacency matrices. Effective thermal conductivity is analyzed using a resistance network model, and effective stiffness is evaluated using a self-consistent elastic model, respectively. A total of 160â¯000 structures are generated and compared to porous-metal foams in which porosity is one of the design variables. The results show that it is possible to obtain a wide range of properties spanning more than an order of magnitude in comparison to porous-metal structures. Structures with a maximum anisotropy ratios of 7.1 and 8.2 are observed for thermal and mechanical properties, respectively. Preliminary experimental results validated the anisotropy ratio for the thermal conductivity and stiffness.
RESUMO
The energy conversion efficiency of today's thermoelectric generators is significantly lower than that of conventional mechanical engines. Almost all of the existing research is focused on materials to improve the conversion efficiency. Here we propose a general framework to study the cost-efficiency trade-off for thermoelectric power generation. A key factor is the optimization of thermoelectric modules together with their heat source and heat sinks. Full electrical and thermal co-optimization yield a simple analytical expression for optimum design. Based on this model, power output per unit mass can be maximized. We show that the fractional area coverage of thermoelectric elements in a module could play a significant role in reducing the cost of power generation systems.