Characteristics and Applications of Non-Volatile Resistive Switching (Memristor) Device.

Non-volatile memory technology scaling has been driven by the ever increasing needs of high-capacity and low-cost data storage. Scaling the conventional floating gate device structure, however, has faced with several technical challenges due to constraints of electrostatics and reliability. Alternative memory approaches based on non-transistor structures has been extensively studied. Among the new approaches, resistive switching devices (RRAM) have attracted tremendous attention due to their high endurance, sub-nanosecond switching, long retention, scalability, low power consumption, high ON/OFF ratio and CMOS compatibility. In this thesis, we present a systematic study on the fundamental understanding and potential applications of RRAMs. Firstly, we introduce a quantitative and accurate model of the dynamic resistive switching processes, by solving the coupled equations for oxygen vacancy transport, current continuity and Joule heating. Secondly, we show systematic investigations on the resistance switching mechanism through detailed noise and transport analysis, and develop a unified model to explain the conduction path and account for the resistance switching effects. Thirdly, we perform detailed retention studies of oxide-based RRAMs at elevated temperatures and develop an oxygen diffusion reliability model of RRAM devices. The activation energy for oxygen vacancy diffusion is directly calculated from the measurement. Analytical modeling and detailed numerical multi-physics simulation is discussed. Fourthly, we report that doping tantalum oxide based RRAM with silicon atoms leads to larger dynamic ranges with improved accessibility to the intermediate states which is suited for neuromorphic computing applications. Lastly, we investigate the application of RRAMs in neuromorphic computing by showing data clustering based on unsupervised learning. Through both simulation and experimental studies, we demonstrate that a crossbar array of RRAM devices can perform data clustering through unsupervised learning and enable effective data classification in a real-world problem. These studies have not only helped the development and optimization of RRAM devices but also highlighted their application potential beyond simple memory. We believe continued development of this emerging device structure may lead to future high-performance and energy efficient memory and logic hardware systems.PhDElectrical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/113635/1/choichos_1.pd

Solvent-exposed lipid tail protrusions depend on lipid membrane composition and curvature

The stochastic protrusion of hydrophobic lipid tails into solution, a subclass of hydrophobic membrane defects, has recently been shown to be a critical step in a number of biological processes like membrane fusion. Understanding the factors that govern the appearance of lipid tail protrusions is critical for identifying membrane features that affect the rate of fusion or other processes that depend on contact with solvent-exposed lipid tails. In this work, we utilize atomistic molecular dynamics simulations to characterize the likelihood of tail protrusions in phosphotidylcholine lipid bilayers of varying composition, curvature, and hydration. We distinguish two protrusion modes corresponding to atoms near the end of the lipid tail or near the glycerol group. Through potential of mean force calculations, we demonstrate that the thermodynamic cost for inducing a protrusion depends on tail saturation but is insensitive to other bilayer structural properties or hydration above a threshold value. Similarly, highly curved vesicles or micelles increase both the overall frequency of lipid tail protrusions as well as the preference for splay protrusions, both of which play an important role in driving membrane fusion. In multi-component bilayers, however, the incidence of protrusion events does not clearly depend on the mismatch between tail length or tail saturation of the constituent lipids. Together, these results provide significant physical insight into how system components might affect the appearance of protrusions in biological membranes, and help explain the roles of composition or curvature-modifying proteins in membrane fusion.National Science Foundation (U.S.). MRSEC Program (award number DMR-0819762)National Science Foundation (U.S.). Faculty Early Career Development Program (Award No. DMR-1054671)United States. Department of Energy. Computational Science Graduate Fellowship Program (grant number DE-FG02-97ER25308)National Science Foundation (U.S.) (grant number OCI-1053575

Efficient Si Nanowire Array Transfer via Bi‐Layer Structure Formation Through Metal‐Assisted Chemical Etching

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/106706/1/adfm201303180.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/106706/2/adfm201303180-sup-0001-S1.pd

Oxide Heterostructure Resistive Memory

Resistive switching devices are widely believed as a promising candidate for future memory and logic applications. Here we show that by using multilayer oxide heterostructures the switching characteristics can be systematically controlled, ranging from unipolar switching to complementary switching and bipolar switching with linear and nonlinear on-states and high endurance. Each layer can be tailed for a specific function during resistance switching, thus greatly improving the degree of control and flexibility for optimized device performance

Enhanced performance of lead sulfide quantum dot-sensitized solar cells by controlling the thickness of metal halide perovskite shells

The metal halide perovskite CH3NH3PbI3 (MAP) can be applied as the shell layer of lead sulfide quantum dots (PbS QDs) for improving solar power conversion efficiency. However, basic physics for this PbS core/MAP shell QD system is still unclear and needs to be clarified to further improve efficiency. Therefore, in this study, we investigate how MAP shell thickness affects device performance and dynamics of charge carriers for PbS QD-sensitized solar cells. Covering the PbS QDs with the MAP shell layers of an appropriate thickness around 0.34 nm greatly suppresses charge carrier recombination at surface defects along with improved carrier transport to neighboring oxide and polymer layers. Therefore, this MAP shell thickness provides the highest open-circuit voltage, short-circuit current density, and fill factor for solar cells. Overall power conversion efficiencies of these solar cells reached about 4.1%, which is about six-fold higher than that for solar cells without MAP (about 0.7%). Additionally, use of the MAP shell layers can prevent oxidation of PbS QDs and, therefore, makes a degradation of solar cell performance slower in air

Comprehensive Physical Model of Dynamic Resistive Switching in an Oxide Memristor

Memristors have been proposed for a number of applications from nonvolatile memory to neuro­morphic systems. Unlike conventional devices based solely on electron transport, memristors operate on the principle of resistive switching (RS) based on redistribution of ions. To date, a number of experimental and modeling studies have been reported to probe the RS mechanism; however, a complete physical picture that can quantitatively describe the dynamic RS behavior is still missing. Here, we present a quantitative and accurate dynamic switching model that not only fully accounts for the rich RS behaviors in memristors in a unified framework but also provides critical insight for continued device design, optimization, and applications. The proposed model reveals the roles of electric field, temperature, oxygen vacancy concentration gradient, and different material and device parameters on RS and allows accurate predictions of diverse set/reset, analog switching, and complementary RS behaviors using only material-dependent device parameters

Tuning Resistive Switching Characteristics of Tantalum Oxide Memristors through Si Doping

An oxide memristor device changes its internal state according to the history of the applied voltage and current. The principle of resistive switching (RS) is based on ion transport (<i>e.g.</i>, oxygen vacancy redistribution). To date, devices with bi-, triple-, or even quadruple-layered structures have been studied to achieve the desired switching behavior through device structure optimization. In contrast, the device performance can also be tuned through fundamental atomic-level design of the switching materials, which can directly affect the dynamic transport of ions and lead to optimized switching characteristics. Here, we show that doping tantalum oxide memristors with silicon atoms can facilitate oxygen vacancy formation and transport in the switching layer with adjustable ion hopping distance and drift velocity. The devices show larger dynamic ranges with easier access to the intermediate states while maintaining the extremely high cycling endurance (>10¹⁰ set and reset) and are well-suited for neuromorphic computing applications. As an example, we demonstrate different flavors of spike-timing-dependent plasticity in this memristor system. We further provide a characterization methodology to quantitatively estimate the effective hopping distance of the oxygen vacancies. The experimental results are confirmed through detailed <i>ab initio</i> calculations which reveal the roles of dopants and provide design methodology for further optimization of the RS behavior

Perspective: Uniform switching of artificial synapses for large-scale neuromorphic arrays

Resistive random-access memories are promising analog synaptic devices for efficient bio-inspired neuromorphic computing arrays. Here we first describe working principles for phase-change random-access memory, oxide random-access memory, and conductive-bridging random-access memory for artificial synapses. These devices could allow for dense and efficient storage of analog synapse connections between CMOS neuron circuits. We also discuss challenges and opportunities for analog synaptic devices toward the goal of realizing passive neuromorphic computing arrays. Finally, we focus on reducing spatial and temporal variations, which is critical to experimentally realize powerful and efficient neuromorphic computing systems

Perspective: Uniform switching of artificial synapses for large-scale neuromorphic arrays

Resistive random-Access memories are promising analog synaptic devices for efficient bio-inspired neuromorphic computing arrays. Here we first describe working principles for phase-change random-Access memory, oxide random-Access memory, and conductive-bridging random-Access memory for artificial synapses. These devices could allow for dense and efficient storage of analog synapse connections between CMOS neuron circuits. We also discuss challenges and opportunities for analog synaptic devices toward the goal of realizing passive neuromorphic computing arrays. Finally, we focus on reducing spatial and temporal variations, which is critical to experimentally realize powerful and efficient neuromorphic computing systems