Publications

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First-principles calculations of electrical conductivity (σo) are revisited to determine the atomistic origin of its stochasticity in a distribution generated from sampling 14 ab-initio molecular dynamics configurations from 10 independently quenched models (n = 140) of substoichiometric amorphous Ta2O5, where each structure contains a neutral O monovacancy (VO0). Structural analysis revealed a distinct minimum Ta-Ta separation (dimer/trimer) corresponding to each VO0 location. Bader charge decomposition using a commonality analysis approach based on the σo distribution extremes revealed nanostructural signatures indicating that both the magnitude and distribution of cationic charge on the Ta subnetwork have a profound influence on σo. Furthermore, visualization of local defect structures and their electron densities reinforces these conclusions and suggests σo in the amorphous oxide is best suppressed by a highly charged, compact Ta cation shell that effectively screens and minimizes localized VO0 interaction with the a-Ta2O5 network; conversely, delocalization of VO0 corresponds to metallic character and high σo. The random network of a-Ta2O5 provides countless variations of an ionic configuration scaffold in which small perturbations affect the electronic charge distribution and result in a fixed-stoichiometry distribution of σo; consequently, precisely controlled and highly repeatable oxide fabrication processes are likely paramount for advancement of resistive memory technologies.

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The exponential increase in data over the last decade presents a significant challenge to analytics efforts that seek to process and interpret such data for various applications. Neural-inspired computing approaches are being developed in order to leverage the computational properties of the analog, low-power data processing observed in biological systems. Analog resistive memory crossbars can perform a parallel read or a vector-matrix multiplication as well as a parallel write or a rank-1 update with high computational efficiency. For an N × N crossbar, these two kernels can be O(N) more energy efficient than a conventional digital memory-based architecture. If the read operation is noise limited, the energy to read a column can be independent of the crossbar size (O(1)). These two kernels form the basis of many neuromorphic algorithms such as image, text, and speech recognition. For instance, these kernels can be applied to a neural sparse coding algorithm to give an O(N) reduction in energy for the entire algorithm when run with finite precision. Sparse coding is a rich problem with a host of applications including computer vision, object tracking, and more generally unsupervised learning.

Final report for Cognitive Computing for Security LDRD 165613. It reports on the development of hybrid of general purpose/ne uromorphic computer architecture, with an emphasis on potential implementation with memristors.

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As transistors start to approach fundamental limits and Moore's law slows down, new devices and architectures are needed to enable continued performance gains. New approaches based on RRAM (resistive random access memory) or memristor crossbars can enable the processing of large amounts of data[1, 2]. One of the most promising applications for RRAM crossbars is brain inspired or neuromorphic computing[3, 4].

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In this paper, we present a fully-coupled electrical and thermal transport model for oxide memristors that solves simultaneously the time-dependent continuity equations for all relevant carriers, together with the time-dependent heat equation including Joule heating sources. The model captures all the important processes that drive memristive switching and is applicable to simulate switching behavior in a wide range of oxide memristors. The model is applied to simulate the ON switching in a 3D filamentary TaOx memristor. Simulation results show that, for uniform vacancy density in the OFF state, vacancies fill in the conduction filament till saturation, and then fill out a gap formed in the Ta electrode during ON switching; furthermore, ON-switching time strongly depends on applied voltage and the ON-to-OFF current ratio is sensitive to the filament vacancy density in the OFF state.

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The thermal conductivity of amorphous TaOx memristive films having variable oxygen content is measured using time domain thermoreflectance. Thermal transport is described by a two-part model where the electrical contribution is quantified via the Wiedemann-Franz relation and the vibrational contribution by the minimum thermal conductivity limit for amorphous solids. The vibrational contribution remains constant near 0.9 W/mK regardless of oxygen concentration, while the electrical contribution varies from 0 to 3.3 W/mK. Thus, the dominant thermal carrier in TaOx switches between vibrations and charge carriers and is controllable either by oxygen content during deposition, or dynamically by field-induced charge state migration.

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