Showing papers by "Yuchao Yang published in 2013"

Nanoscale resistive switching devices: mechanisms and modeling

Abstract: Resistive switching devices (also termed memristive devices or memristors) are two-terminal nonlinear dynamic electronic devices that can have broad applications in the fields of nonvolatile memory, reconfigurable logic, analog circuits, and neuromorphic computing. Current rapid advances in memristive devices in turn demand better understanding of the switching mechanism and the development of physics-based as well as simplified device models to guide future device designs and circuit-level applications. In this article, we review the physical processes behind resistive switching (memristive) phenomena and discuss the experimental and modeling efforts to explain these effects. In this article three categories of devices, in which the resistive switching effects are driven by cation migration, anion migration, and electronic effects, will be discussed. The fundamental driving forces and the stochastic nature of resistive switching will also be discussed.

Oxide Heterostructure Resistive Memory

Abstract: 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.

Building Neuromorphic Circuits with Memristive Devices

Abstract: The rapid, exponential growth of modern electronics has brought about profound changes to our daily lives. However, maintaining the growth trend now faces significant challenges at both the fundamental and practical levels [1]. Possible solutions include More Moore?developing new, alternative device structures and materials while maintaining the same basic computer architecture, and More Than Moore?enabling alternative computing architectures and hybrid integration to achieve increased system functionality without trying to push the devices beyond limits. In particular, an increasing number of computing tasks today are related to handling large amounts of data, e.g. image processing as an example. Conventional von Neumann digital computers, with separate memory and processer units, become less and less efficient when large amount of data have to be moved around and processed quickly. Alternative approaches such as bio-inspired neuromorphic circuits, with distributed computing and localized storage in networks, become attractive options [2]?[6].