Ferroelectric tunneling junctions (FTJs) with tunable tunneling electroresistance (TER) are promising for many emerging applications, including non-volatile memories and neurosynaptic computing. One of the key challenges in FTJs is the balance between the polarization value and the tunneling current. In order to achieve a sizable on-current, the thickness of the ferroelectric layer needs to be scaled down below 5 nm. However, the polarization in these ultra-thin ferroelectric layers is very small, which leads to a low tunneling electroresistance (TER) ratio. In this paper, we propose and demonstrate a new type of FTJ based on metal/Al2O3/Zr-doped HfO2/Si structure. The interfacial Al2O3 layer and silicon substrate enable sizable TERs even when the thickness of Zr-doped HfO2 (HZO) is above 10 nm. We found that F-N tunneling dominates at read voltages and that the polarization switching in HZO can alter the effective tunneling barrier height and tune the tunneling resistance. The FTJ synapses based on Al2O3/HZO stacks show symmetric potentiation/depression characteristics and widely tunable conductance. We also show that spike-timing-dependent plasticity (STDP) can be harnessed from HZO based FTJs. These novel FTJs will have high potential in non-volatile memories and neural network applications.

How impurity atoms move through a crystal is a fundamental and recurrent question in materials. The previous models of oxygen diffusion in titanium relied on interstitial lattice sites that were recently found to be unstable--leaving no consistent picture of the diffusion pathways. Using first-principles quantum-mechanical methods, we find three oxygen interstitial sites in titanium, and quantify the multiple interpenetrating networks for oxygen diffusion. Surprisingly, all transitions contribute to diffusion.

We demonstrate automated generation of diffusion databases from high-throughput density functional theory (DFT) calculations. A total of more than 230 dilute solute diffusion systems in Mg, Al, Cu, Ni, Pd, and Pt host lattices have been determined using multi-frequency diffusion models. We apply a correction method for solute diffusion in alloys using experimental and simulated values of host self-diffusivity. We find good agreement with experimental solute diffusion data, obtaining a weighted activation barrier RMS error of 0.176 eV when excluding magnetic solutes in non-magnetic alloys. The compiled database is the largest collection of consistently calculated ab-initio solute diffusion data in the world.

We evaluate the performance of four machine learning methods for modeling and predicting FCC solute diffusion barriers. More than 200 FCC solute diffusion barriers from previous density functional theory (DFT) calculations served as our dataset to train four machine learning methods: linear regression (LR), decision tree (DT), Gaussian kernel ridge regression (GKRR), and artificial neural network (ANN). We separately optimize key physical descriptors favored by each method to model diffusion barriers. We also assess the ability of each method to extrapolate when faced with new hosts with limited known data. GKRR and ANN were found to perform the best, showing 0.15 eV cross-validation errors and predicting impurity diffusion in new hosts to within 0.2 eV when given only 5 data points from the host. We demonstrate the success of a combined DFT + data mining approach towards solving materials science challenges and predict the diffusion barrier of all available impurities across all FCC hosts.Keywords: Diffusion; Data-mining; Machine learning; DFT; Neural network 1: Introduction Atomic migration in solids governs the kinetics of many materials processes, including precipitation, high-temperature creep, phase transformation, and solution homogenization. A particular class of atomic diffusion is dilute impurity diffusion, which refers to the diffusion of a dilute solute in a host. Such diffusion is relevant in many materials applications as dilute solutes are common due to either undesired impurities or intentional dopants in materials. Due to its importance to materials science, large experimental catalogues of impurity diffusion measurements have been collected [1,2], and more recently, first-principles predictions of dilute impurity diffusion coefficients have been conducted [3][4][5][6][7][8]. However, both experimental and theoretical approaches are limited by several drawbacks. Experimental diffusivities often vary significantly due to uncertainties introduced by different measurement techniques and other impurity effects in sample materials. In addition, experiments require significant diffusion kinetics to achieve good data, which generally limits them to be relatively high temperature (e.g., above about 50% of the melting temperature of the host) [9]. Finally, experiments are time consuming and expensive relative to first-principles calculations, as they require significant equipment and human interaction. First-principles calculations are an increasingly powerful tool for predicting dilute impurity diffusion, and compared to experiments can be done at a tiny fraction of the cost of equipment and human time. Furthermore, first-principles predicted energies are expected to be most accurate at lower temperatures, where vibrational and electronic excitations play a minor role, which suggests that these methods may have their best accuracy in temperature domains complimentary to experiments. First-principles methods bring with them significant approximations in both the fundamental energetics and in trea...

This work demonstrates how databases of diffusion-related properties can be developed from high-throughput ab initio calculations. The formation and migration energies for vacancies of all adequately stable pure elements in both the face-centered cubic (fcc) and hexagonal close packing (hcp) crystal structures were determined using ab initio calculations. For hcp migration, both the basal plane and z-direction nearest-neighbor vacancy hops were considered. Energy barriers were successfully calculated for 49 elements in the fcc structure and 44 elements in the hcp structure. These data were plotted against various elemental properties in order to discover significant correlations. The calculated data show smooth and continuous trends when plotted against Mendeleev numbers. The vacancy formation energies were plotted against cohesive energies to produce linear trends with regressed slopes of 0.317 and 0.323 for the fcc and hcp structures respectively. This result shows the expected increase in vacancy formation energy with stronger bonding. The slope of approximately 0.3, being well below that predicted by a simple fixed bond strength model, is consistent with a reduction in the vacancy formation energy due to many-body effects and relaxation. Vacancy migration barriers are found to increase nearly linearly New J. Phys. 16 (2014) 015018 T Angsten et al with increasing stiffness, consistent with the local expansion required to migrate an atom. A simple semi-empirical expression is created to predict the vacancy migration energy from the lattice constant and bulk modulus for fcc systems, yielding estimates with errors of approximately 30%.