The future of memristors: Materials engineering and neural networks

K Sun, J Chen, X Yan - Advanced Functional Materials, 2021 - Wiley Online Library
Abstract From Deep Blue to AlphaGo, artificial intelligence and machine learning are
booming, and neural networks have become the hot research direction. However, due to the …

Review of memristor devices in neuromorphic computing: materials sciences and device challenges

Y Li, Z Wang, R Midya, Q **a… - Journal of Physics D …, 2018 - iopscience.iop.org
The memristor is considered as the one of the promising candidates for next generation
computing systems. Novel computing architectures based on memristors have shown great …

[HTML][HTML] Resistive switching phenomena: A review of statistical physics approaches

JS Lee, S Lee, TW Noh - Applied Physics Reviews, 2015 - pubs.aip.org
Resistive switching (RS) phenomena are reversible changes in the metastable resistance
state induced by external electric fields. After discovery∼ 50 years ago, RS phenomena …

Nonvolatile memories based on graphene and related 2D materials

S Bertolazzi, P Bondavalli, S Roche, T San… - Advanced …, 2019 - Wiley Online Library
The pervasiveness of information technologies is generating an impressive amount of data,
which need to be accessed very quickly. Nonvolatile memories (NVMs) are making inroads …

Memory effects in complex materials and nanoscale systems

YV Pershin, M Di Ventra - Advances in Physics, 2011 - Taylor & Francis
Memory effects are ubiquitous in nature and are particularly relevant at the nanoscale where
the dynamical properties of electrons and ions strongly depend on the history of the system …

Resistive-based gas sensors for detection of benzene, toluene and xylene (BTX) gases: a review

A Mirzaei, JH Kim, HW Kim, SS Kim - Journal of Materials Chemistry C, 2018 - pubs.rsc.org
Benzene, toluene, and xylene gases, which are known collectively as BTX gases, are
volatile organic compounds (VOCs) that are used extensively in many industrial products …

Challenges in materials and devices for resistive-switching-based neuromorphic computing

J Del Valle, JG Ramírez, MJ Rozenberg… - Journal of Applied …, 2018 - pubs.aip.org
This tutorial describes challenges and possible avenues for the implementation of the
components of a solid-state system, which emulates a biological brain. The tutorial is …

Oxide-based RRAM materials for neuromorphic computing

XL Hong, DJJ Loy, PA Dananjaya, F Tan… - Journal of materials …, 2018 - Springer
In this review, a comprehensive survey of different oxide-based resistive random-access
memories (RRAMs) for neuromorphic computing is provided. We begin with the history of …

[PDF][PDF] Anatomy of a nanoscale conduction channel reveals the mechanism of a high-performance memristor

F Miao, JP Strachan, JJ Yang, MX Zhang, I Goldfarb… - Adv. Mater, 2011 - academia.edu
Present memory technologies, including DRAM (dynamic random access memory), SRAM
(static random access memory), and flash, are potentially approaching their scalability limits …

Dynamic evolution of conducting nanofilament in resistive switching memories

JY Chen, CL Hsin, CW Huang, CH Chiu, YT Huang… - Nano …, 2013 - ACS Publications
Resistive random access memory (ReRAM) has been considered the most promising next-
generation nonvolatile memory. In recent years, the switching behavior has been widely …