Heat dissipation is a critical bottleneck for microelectronic device performance and longevity. At micrometer and nanometer length scales heat carriers scatter at the boundaries of the material reducing its thermal conductivity. Additionally, thermal boundary conductance across dissimilar material interfaces becomes a dominant factor due to the increase in surface area relative to the volume of device layers. Therefore, techniques for monitoring spatially varying temperature profiles, and methods to improve thermal performance are critical to future device design and optimization. The first half of this thesis focused on frequency domain thermoreflectance (FDTR) to measure thermal transport in nanometer-thick polymer films and across an organic-inorganic interface. Hybrid structures of organic and inorganic materials are widely used in devices such as batteries, solar cells, transistors, and flexible electronics. The Langmuir-Blodgett (LB) technique was used to fabricate nanometer-thick polymer films ranging from 2 - 30 nm. FDTR was then used to experimentally determine the thermal boundary conductance between the polymer and solid substrates. The second half of the thesis focused on developing a fundamental understanding of thermal transport in wide-bandgap (WBG) materials, such as GaN, and ultrawide-bandgap (UWBG) materials, such as diamond, to improve thermal dissipation in power electronic devices. Improvements in WBG materials and device technologies have slowed as thermal properties limit their performance. UWBG materials can provide a dramatic leap in power electronics technologies while temporarily sidestepping the problems associated with their WBG cousins. However, for power electronic devices based on WBG- and UWBG-materials to reach their full potential the thermal dissipation issues in these hard-driven devices must be understood and solved. FDTR provides a comprehensive pathway towards fully understanding the physics governing phonon transport in WBG- and UWBG-based devices. By leveraging FDTR imaging and measuring samples as a function of temperature, defect concentration, and thickness, in conjunction with transport models, a well-founded understanding of the dominant thermal-carrier scattering mechanisms in these devices was achieved. With this knowledge we developed pathways for their mitigation.

京都大学 / 0048 / 新制・課程博士 / 博士(工学) / 甲第21369号 / 工博第4528号 / 新制||工||1705(附属図書館) / 京都大学大学院工学研究科材料工学専攻 / (主査)教授 田中 功, 教授 中村 裕之, 教授 安田 秀幸 / 学位規則第4条第1項該当 / Doctor of Philosophy (Engineering) / Kyoto University / DFAM

Process models are always associated with uncertainty, due to either inaccurate model structure or inaccurate identification. If left unaccounted for, these uncertainties can significantly affect the model-based decision-making. This thesis addresses the problem of model-based optimization in the presence of uncertainties, especially due to model structure error. The optimal solution from standard optimization techniques is often associated with a certain degree of uncertainty and if the model-plant mismatch is very significant, this solution may have a significant bias with respect to the actual process optimum. Accordingly, in this thesis, we developed new strategies to reduce (1) the variability in the optimal solution and (2) the bias between the predicted and the true process optima. Robust optimization is a well-established methodology where the variability in optimization objective is considered explicitly in the cost function, leading to a solution that is robust to model uncertainties. However, the reported robust formulations have few limitations especially in the context of nonlinear models. The standard technique to quantify the effect of model uncertainties is based on the linearization of underlying model that may not be valid if the noise in measurements is quite high. To address this limitation, uncertainty descriptions based on the Bayes’ Theorem are implemented in this work. Since for nonlinear models the resulting Bayesian uncertainty may have a non-standard form with no analytical solution, the propagation of this uncertainty onto the optimum may become computationally challenging using conventional Monte Carlo techniques. To this end, an approach based on Polynomial Chaos expansions is developed. It is shown in a simulated case study that this approach resulted in drastic reductions in the computational time when compared to a standard Monte Carlo sampling technique. The key advantage of PC expansions is that they provide analytical expressions for statistical moments even if the uncertainty in variables is non-standard. These expansions were also used to speed up the calculation of likelihood function within the Bayesian framework. Here, a methodology based on Multi-Resolution analysis is proposed to formulate the PC based approximated model with higher accuracy over the parameter space that is most likely based on the given measurements. For the second objective, i.e. reducing the bias between the predicted and true process optima, an iterative optimization algorithm is developed which progressively corrects the model for structural error as the algorithm proceeds towards the true process optimum. The standard technique is to calibrate the model at some initial operating conditions and, then, use this model to search for an optimal solution. Since the identification and optimization objectives are solved independently, when there is a mismatch between the process and the model, the parameter estimates cannot satisfy these two objectives simultaneously. To this end, in the proposed methodology, corrections are added to the model in such a way that the updated parameter estimates reduce the conflict between the identification and optimization objectives. Unlike the standard estimation technique that minimizes only the prediction error at a given set of operating conditions, the proposed algorithm also includes the differences between the predicted and measured gradients of the optimization objective and/or constraints in the estimation. In the initial version of the algorithm, the proposed correction is based on the linearization of model outputs. Then, in the second part, the correction is extended by using a quadratic approximation of the model, which, for the given case study, resulted in much faster convergence as compared to the earlier version. Finally, the methodologies mentioned above were combined to formulate a robust iterative optimization strategy that converges to the true process optimum with minimum variability in the search path. One of the major findings of this thesis is that the robust optimal solutions based on the Bayesian parametric uncertainty are much less conservative than their counterparts based on normally distributed parameters.