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

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

Experimental Demonstration of a Second-Order Memristor and Its Ability to Biorealistically Implement Synaptic Plasticity

Memristors have been extensively studied for data storage and low-power computation applications. In this study, we show that memristors offer more than simple resistance change. Specifically, the dynamic evolutions of internal state variables allow an oxide-based memristor to exhibit Ca²⁺-like dynamics that natively encode timing information and regulate synaptic weights. Such a device can be modeled as a second-order memristor and allow the implementation of critical synaptic functions realistically using simple spike forms based solely on spike activity