Publications

Abstract not provided.

This report provides an assessment of the value of the LDRD program to Sandia National Laboratories during fiscal year 2019

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Machine learning (ML), including deep learning (DL), has become increasingly popular in the last few years due to its continually outstanding performance. In this context, we apply machine learning techniques to "learn" the microstructure using both supervised and unsupervised DL techniques. In particular, we focus (1) on the localization problem bridging (micro)structure (localized) property using supervised DL and (2) on the microstructure reconstruction problem in latent space using unsupervised DL. The goal of supervised and semi-supervised DL is to replace crystal plasticity finite element model (CPFEM) that maps from (micro)structure (localized) property, and implicitly the (micro)structure (homogenized) property relationships, while the goal of unsupervised DL is (1) to represent high-dimensional microstructure images in a non-linear low-dimensional manifold, and (2) to discover a way to interpolate microstructures via latent space associating with latent microstructure variables. At the heart of this report is the applications of several common DL architectures, including convolutional neural networks (CNN), autoencoder (AE), and generative adversarial network (GAN), to multiple microstructure datasets, and the quest of neural architecture search for optimal DL architectures.

With the elimination of underground nuclear testing and declining defense budgets, science-based stockpile stewardship requires increased reliance on high performance modeling and simulation of weapon systems. Today's weapon systems are comprised of various electrical components and systems. As a result, there is a need for tools that will allow the use of massively parallel modeling and simulation techniques on high performance computers in existing and future weapons' electrical systems models. The Xyce Parallel Electronic Simulator is a SPICE (Simulation Program with Integrated Circuit Emphasis)- compatible circuit simulator designed to run on large-scale parallel computing platforms, though it can also execute efficiently on a variety of architectures including single processor workstations. As a mature platform for large-scale parallel circuit simulation, Xyce supports standard capabilities available in commercial simulators, in addition to various devices and models specific to Sandia's needs. Specifically, Xyce aids in the design and verification of electrical and electronic circuits and systems prior to weapons' manufacturing and deployment.

Advances in FinFET design and fabrication enable manufacturing of denser, more compact integrated circuits (ICs) with substantially reduced leakage while shortening the channel-lengths. The same stress-induced leakage and breakdown degradation mechanisms that affect planar transistors also impact FinFET devices. Reliability concerns such as Bias Temperature Instability (BTI), Time Dependent Dielectric Breakdown (TDDB), and Hot Carrier Injection (HCI) become very important with changes to transistor geometry and fin sidewall crystal orientation. Recent testing has shown that FinFETs respond differently to radiation (radiation effects such as total ionizing dose) when compared to planar transistors. These reliability and radiation effects issues become very important when changing transistor geometry and scaling FinFETs towards smaller feature sizes (22-nm, 16-nm, 14- nm, 10-nm, and smaller critical dimensions). The comparable 2019 state of the art transistor densities in current high-volume manufacturing silicon-based foundries is 7-nm (ISMC, Samsung) and 10-nm (Intel) [www.anandtech.com,fuse.wikichip.org]. Released products include supporting components for the cellphone and commercial microprocessor markets respectively. Extensive development in the foundry industry is driving to a 5-nm technology node in late 2020.

Solid-state ion conductors based on closo-polyborate anions combine high ionic conductivity with a rich array of tunable properties. Cation mobility in these systems is intimately related to the strength of the interaction with the neighboring anionic network and the energy for reorganizing the coordination polyhedra. Here, we explore such factors in solid electrolytes with two anions of the weakest coordinating ability, [HCB11H5Cl6]- and [HCB11H5Br6]-, and a total of 11 polymorphs are identified for their lithium and sodium salts. Our approach combines ab initio molecular dynamics, synchrotron X-ray powder diffraction, differential scanning calorimetry, and AC impedance measurements to investigate their structures, phase-transition behavior, anion orientational mobilities, and ionic conductivities. We find that M(HCB11H5X6) (M = Li, Na, X = Cl, Br) compounds exhibit order-disorder polymorphic transitions between 203 and 305 °C and display Li and Na superionic conductivity in the disordered state. Through detailed analysis, we illustrate how cation disordering in these compounds originates from a competitive interplay among the lattice symmetry, the anion reorientational mobility, the geometric and electronic asymmetry of the anion, and the polarizability of the halogen atoms. These factors are compared to other closo-polyborate-based ion conductors to suggest guidelines for optimizing the cation-anion interaction for fast ion mobility. This study expands the known solid-state poly(carba)borate-based materials capable of liquid-like ionic conductivities, unravels the mechanisms responsible for fast ion transport, and provides insights into the development of practical superionic solid electrolytes.

Interfacial toughness quantifies resistance to crack growth along an interface and in this investigation the toughness of an aluminum/epoxy interface was measured as a function of surface roughness and test temperature. The large strain response of the relatively ductile epoxy adhesive used in this study was also characterized. This epoxy adhesive exhibits intrinsic strain-softening after initial compressive yield and then deforms plastically at a roughly constant flow stress until it rapidly hardens at large compressive strains. Here, we find that interface toughness scales as the product of the temperature dependent epoxy yield strength and a length scale that characterizes surface roughness. The proposed scaling is based upon dimensional considerations of a model problem that assumes that the characteristic length scale of both the roughness and the crack-tip yield zone is small relative to the region dominated by the linear elastic asymptotic crack-tip stress field. Furthermore, the model assumes that interfacial failure occurs only after the epoxy begins to harden at large strains. The proposed relationship is validated by our interfacial toughness measurements.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.