Self-assembled nanostructured resistive switching memory devices fabricated by templated bottom-up growth.

Sci Rep

Department of Materials Science and Engineering, Pohang University of Science and Technology (POSTECH), Pohang 790-784, Republic of Korea.

Published: January 2016

Metal-oxide-based resistive switching memory device has been studied intensively due to its potential to satisfy the requirements of next-generation memory devices. Active research has been done on the materials and device structures of resistive switching memory devices that meet the requirements of high density, fast switching speed, and reliable data storage. In this study, resistive switching memory devices were fabricated with nano-template-assisted bottom up growth. The electrochemical deposition was adopted to achieve the bottom-up growth of nickel nanodot electrodes. Nickel oxide layer was formed by oxygen plasma treatment of nickel nanodots at low temperature. The structures of fabricated nanoscale memory devices were analyzed with scanning electron microscope and atomic force microscope (AFM). The electrical characteristics of the devices were directly measured using conductive AFM. This work demonstrates the fabrication of resistive switching memory devices using self-assembled nanoscale masks and nanomateirals growth from bottom-up electrochemical deposition.

Download full-text PDF

Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4704057PMC
http://dx.doi.org/10.1038/srep18967DOI Listing

Publication Analysis

Top Keywords

memory devices
24
resistive switching
20
switching memory
20
devices fabricated
8
bottom-up growth
8
electrochemical deposition
8
memory
7
devices
7
switching
6
resistive
5

Similar Publications

Unprecedented penetration of artificial intelligence (AI) algorithms has brought about rapid innovations in electronic hardware, including new memory devices. Nonvolatile memory (NVM) devices offer one such attractive alternative with ∼2× density and data retention after powering off. Compute-in-memory (CIM) architectures further improve energy efficiency by fusing the computation operations with AI model storage.

View Article and Find Full Text PDF

Two-Dimensional Nonvolatile Valley Spin Valve.

ACS Nano

January 2025

Department of Physics and Astronomy & Nebraska Center for Materials and Nanoscience, University of Nebraska, Lincoln, Nebraska 68588-0299, United States.

A spin valve represents a well-established device concept in magnetic memory technologies, whose functionality is determined by electron transmission, controlled by the relative alignment of magnetic moments of the two ferromagnetic layers. Recently, the advent of valleytronics has conceptualized a valley spin valve (VSV)─a device that utilizes the valley degree of freedom and spin-valley locking to achieve a similar valve effect without relying on magnetism. In this study, we propose a nonvolatile VSV (-VSV) based on a two-dimensional (2D) ferroelectric semiconductor where resistance of -VSV is controlled by a ferroelectric domain wall between two uniformly polarized domains.

View Article and Find Full Text PDF

A mononuclear iron(II) complex constructed using a complementary ligand pair exhibits intrinsic luminescence-spin-crossover coupling.

Dalton Trans

January 2025

State Key Laboratory of Fine Chemicals, Frontier Science Center for Smart Materials, School of Chemical Engineering, Dalian University of Technology, No. 2 Linggong Road, Dalian 116024, P. R. China.

Molecular materials that exhibit synergistic coupling between luminescence and spin-crossover (SCO) behaviors hold significant promise for applications in molecular sensors and memory devices. However, the rational design and underlying coupling mechanisms remain substantial challenges in this field. In this study, we utilized a luminescent complementary ligand pair as an intramolecular luminophore to construct a new Fe-based SCO complex, namely [FeLL](BF)·HO (1-Fe, L is a 2,2':6',2''-terpyridine (TPY) derivative ligand and L is 2,6-di-1-pyrazol-1-yl-4-pyridinecarboxylic acid), and two isomorphic analogs (2-Co, [CoLL](BF)·HO and 3-Zn, [ZnLL](BF)·HO).

View Article and Find Full Text PDF

ShaderNN: A Lightweight and Efficient Inference Engine for Real-time Applications on Mobile GPUs.

Neurocomputing (Amst)

January 2025

Department of Electrical and Computer Engineering, University of Maryland at College Park, 8223 Paint Branch Dr, College Park, MD, 20740, USA.

Inference using deep neural networks on mobile devices has been an active area of research in recent years. The design of a deep learning inference framework targeted for mobile devices needs to consider various factors, such as the limited computational capacity of the devices, low power budget, varied memory access methods, and I/O bus bandwidth governed by the underlying processor's architecture. Furthermore, integrating an inference framework with time-sensitive applications - such as games and video-based software to perform tasks like ray tracing denoising and video processing - introduces the need to minimize data movement between processors and increase data locality in the target processor.

View Article and Find Full Text PDF

Programmable organization of uniform organic/inorganic functional building blocks into large-scale ordered superlattices has attracted considerable attention since the bottom-up self-organization strategy opens up a robust and universal route for designing novel and multifunctional materials with advanced applications in memory storage devices, catalysis, photonic crystals, and biotherapy. Despite making great efforts in the construction of superlattice materials, there still remains a challenge in the preparation of organic/inorganic hybrid superlattices with tunable dimensions and exotic configurations. Here, we report the spontaneous self-organization of polystyrene-tethered gold nanoparticles (AuNPs@PS) into freestanding organic/inorganic hybrid superlattices templated at the diethylene glycol-air interface.

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!