Results 71 to 80 of about 5,289 (241)

Sol–Gel-Processed Y₂O₃ Multilevel Resistive Random-Access Memory Cells for Neural Networks

Nanomaterials, 2023
Yttrium oxide (Y2O3) resistive random-access memory (RRAM) devices were fabricated using the sol–gel process on indium tin oxide/glass substrates. These devices exhibited conventional bipolar RRAM characteristics without requiring a high-voltage forming ...
Taehun Lee, Hae-In Kim, Yoonjin Cho, Sangwoo Lee, Won-Yong Lee, Jin-Hyuk Bae, In-Man Kang, Kwangeun Kim, Sin-Hyung Lee, Jaewon Jang   +9 more
doaj   +1 more source

Ferroelectric Devices for In‐Memory and In‐Sensor Computing

Advanced Science, EarlyView.
Inspired by biological systems, in‐memory and in‐sensor computing overcome von Neumann bottlenecks. Ferroelectric devices can mimic synaptic functions and sense stimuli like light or force, therefore are ideal for these paradigms. This review introduces the ferroelectric devices applied for in‐memory and in‐sensor computing, covering their structures ...
Hong Fang, Ronghuan Xie, Qiang Cao, Shishen Yan, Xiaolin Wang, Limei Zheng   +5 more
wiley   +1 more source

Modeling of Self-Aligned Selector Based on Ultra-Thin Metal Oxide for Resistive Random-Access Memory (RRAM) Crossbar Arrays

Nanomaterials
Resistive random-access memory (RRAM) is a crucial element for next-generation large-scale memory arrays, analogue neuromorphic computing and energy-efficient System-on-Chip applications.
Mikhail Fedotov, Viktor Korotitsky, Sergei Koveshnikov   +2 more
doaj   +1 more source

Investigating the Temperature Effects on ZnO, TiO2, WO3 and HfO2 Based Resistive Random Access Memory (RRAM) Devices [PDF]

Журнал нано- та електронної фізики, 2016
In this paper, we report the effect of filament radius and filament resistivity on the ZnO, TiO2, WO3 and HfO2 based Resistive Random Access Memory (RRAM) devices. We resort to the thermal reaction model of RRAM for the present analysis.
T.D. Dongale, K.V. Khot, S.V. Mohite, S.S. Khandagale, S.S. Shinde, V.L. Patil, S.A. Vanalkar, A.V. Moholkar, K.Y. Rajpure, P.N. Bhosale, P.S. Patil, P.K. Gaikwad, R.K. Kamat   +12 more
doaj   +1 more source

Activation Energy and Bipolar Switching Properties for the Co-Sputtering of ITO_X:SiO₂ Thin Films on Resistive Random Access Memory Devices

Nanomaterials, 2023
Activation energy, bipolar resistance switching behavior, and the electrical conduction transport properties of ITOX:SiO2 thin film resistive random access memory (RRAM) devices were observed and discussed.
Kai-Huang Chen, Chien-Min Cheng, Na-Fu Wang, Ming-Cheng Kao   +3 more
doaj   +1 more source

Advances and Perspectives in Graphene‐Based Quantum Dots Enabled Neuromorphic Devices

Advanced Science, EarlyView.
Graphene‐based QDs are zero‐dimensional carbon nanomaterials with pronounced quantum confinement and tunable electronic structures. Herein, we summarize their synthesis strategies and functionalization methods, and highlight their functional roles and operating mechanisms in devices, as well as recent advances in neuromorphic electronics. We anticipate
Yulin Zhen, Wei Zeng, Zherui Zhao, JiYu Zhao, Ziyan Huang, Zhuo Chen, Kui Zhou, Guanglong Ding, Su‐Ting Han, Ye Zhou   +9 more
wiley   +1 more source

RRAM-based synapse devices for neuromorphic systems [PDF]

, 2019
Hardware artificial neural network (ANN) systems with high density synapse array devices can perform massive parallel computing for pattern recognition with low power consumption.
Moon, K., SEUNYEOL, OH, SEOKJAE, LIM, Sung, C., LEE, JONGWON, Woo, J., Hwang, H., PARKJAEHYUK   +7 more
core   +1 more source

Neuromorphic Near‐Sensor and In‐Sensor Computing Enabled by Next‐Generation Material‐Based Sensors

Advanced Science, EarlyView.
This Review presents a structural framework that classifies neuromorphic sensing into near‐sensor and in‐sensor architectures, clarifying physical coupling between sensing and computation. The framework connects neural and synaptic device functions with recent advances in optical, mechanical, and chemical sensing, compares energy consumption and ...
Su Yeon Jung, Gwang Ya Kim, Sejin Kim, Hyunjong Seo, Se Gi Lee, Sang Min Won, Dongjoon Rhee, Joohoon Kang   +7 more
wiley   +1 more source

Molecularly Engineered Wing‐Shaped Azobenzene Memristors for Logic‐in‐Memory and Edge Visual Intelligence

Advanced Science, EarlyView.
Rational engineering of terminal substituents in symmetric azobenzene‐based molecules enables precise control over conformationally coupled charge‐transfer processes. This design yields tunable nonvolatile resistive memory behaviors, ranging from write‐once‐read‐many‐times (WORM) to rewritable switching.
Yanze Liu, Tao Han, Jiahui Ding, Hong Lian, Lingling Yao, Zhaoxin Xu, Xingyu Zhang, Shuanglong Wang, Jiangnan Xia, Tianchi Zhang, Weiwei Kang, Qingchen Dong   +11 more
wiley   +1 more source

FangTianSim: High-Level Cycle-Accurate Resistive Random-Access Memory-Based Multi-Core Spiking Neural Network Processor Simulator

Frontiers in Neuroscience, 2022
Realization of spiking neural network (SNN) hardware with high energy efficiency and high integration may provide a promising solution to data processing challenges in future internet of things (IoT) and artificial intelligence (AI).
Jinsong Wei, Jinsong Wei, Zhibin Wang, Ye Li, Jikai Lu, Jikai Lu, Hao Jiang, Hao Jiang, Junjie An, Junjie An, Yiqi Li, Lili Gao, Xumeng Zhang, Tuo Shi, Tuo Shi, Qi Liu   +15 more
doaj   +1 more source