Semiconductor materials play a central role in current and future electronics technologies. From microprocessors and advanced computers to optical components, device functionality is dependent on the creation and control of point defects in semiconductors. Incorporating a fundamental understanding of defect kinetics, including formation, migration, and chemistry, is essential for advancing materials science, assessing their device impact, ensuring the reliability of modern electronics, and leveraging new materials for next-generation technologies. This article explores the kinetics of point defects from experimental observations and atomistic modeling, to dynamical multiscale descriptions of defect kinetics. A survey of experiments reveals the important role of kinetics in defect behavior during synthesis, implantation doping, radiation exposure, and long-term defect evolution, while highlighting the impact of evolving material properties on device performance. Atomistic modeling, including molecular dynamics and density functional theory, is surveyed emphasizing its ability to describe dynamical behavior and predict kinetic pathways that govern defect evolution in semiconductors. Dynamical and multiscale modeling methods that integrate experimental and atomistic defect properties into continuum-scale codes are examined for their role in bridging atomic-scale defect behavior to device-level performance. By addressing critical challenges and revealing the inherent difficulties in modeling and experimental validation, this article aims to advance the understanding of defect kinetics and provide insights into the short-term and long-term reliability of materials and devices.
Thermal diffusivity is an important material property for understanding and characterizing transient behavior in many heat transfer applications. This study investigates the accuracy and approximations of inverse mathematical models for measuring thermal diffusivity of materials via the widely used Flash Method. High-fidelity simulations of the Flash Method in copper, silicon carbide, silicon, and glass were performed as numerical experiments and included physics such as in-depth absorption, radial conduction, and surface convection. Data from those numerical experiments were used to estimate material thermal diffusivity using seven traditional and new inverse models. Parker’s original model had relative errors ϵ <5% when the approximations it makes were enforced in numerical experiments. Newer models performed well even when experimental restrictions were relaxed. Models that include radial heat conduction were capable of accurately measuring thermal diffusivity ( ϵ <1% ) when a Gaussian energy source was used. Models with radial conduction and in-depth material absorption of the laser source could calculate thermal diffusivity for semi-transparent materials such as silicon ( ϵ <1% ) and even transparent materials like glass ( ϵ <10% ). Convective losses from the material’s front surface had a negligible impact on measurements except for very low thermal diffusivity materials. Using temperatures from many locations of the test material’s surface increased resilience to noise, reducing the distribution of thermal diffusivity measurements by more than an order of magnitude. The models developed in this study could enable a more relaxed Flash Method experimental setup that maintains thermal diffusivity accuracy and extend the utility of the Flash Method to semi-transparent materials.
This work presents an ongoing Sandia National Laboratories initiative aimed at establishing a laboratory-wide capability for projection-based reduced-order models (ROMs) and demonstrates its utility on two applications of national interest. We outline the motivations and needs of the lab and introduce Pressio, the open-source ROMs software ecosystem under active development constituting the foundation of this ROM capability. Packaged as C++ and Python libraries, Pressio mitigates the intrusiveness of ROM integration by providing a modular, extensible framework for developing, analyzing, and deploying ROM methodologies across diverse application domains. Pressio currently supports model reduction techniques for dynamical systems expressible as parameterized ordinary differential equations. Leveraging this expressive formulation, Pressio offers a minimal API that is natural for dynamical systems. After discussing the distinguishing characteristics of Pressio, we outline its key design features, describe how existing applications can use it, and present two large-scale test cases of interest to Sandia: (1) uncertainty quantification and sensitivity analysis of a steady turbulent flow over a hypersonic vehicle and (2) inference of material properties for a hypersonic vehicle thermal protection system. In both cases, Pressio enables ROMs that substantially accelerate the analyses of interest with a minimal loss in accuracy.
