Detailed Information

Cited 3 time in webofscience Cited 3 time in scopus
Metadata Downloads

Emulating biological synaptic characteristics of HfOx/AlN-based 3D vertical resistive memory for neuromorphic systems

Authors
Kim, JuriLee, SubaekSeo, YeongkyoKim, Sungjun
Issue Date
Apr-2024
Publisher
AIP Publishing
Keywords
Neurons; Pattern Recognition; Double Layers; Electrical Characteristic; Embeddings; Memory Cell; Memristor; Neuromorphic Systems; Random Access Memory; Resistive Memory; Resistive Switching; Type Structures; Random Access Storage; Artificial Neural Network; Electricity; Electricity; Neural Networks, Computer
Citation
The Journal of Chemical Physics, v.160, no.14, pp 1 - 10
Pages
10
Indexed
SCIE
SCOPUS
Journal Title
The Journal of Chemical Physics
Volume
160
Number
14
Start Page
1
End Page
10
URI
https://scholarworks.dongguk.edu/handle/sw.dongguk/21814
DOI
10.1063/5.0202610
ISSN
0021-9606
1089-7690
Abstract
Here, we demonstrate double-layer 3D vertical resistive random-access memory with a hole-type structure embedding Pt/HfOx/AlN/TiN memory cells, conduct analog resistive switching, and examine the potential of memristors for use in neuromorphic systems. The electrical characteristics, including resistive switching, retention, and endurance, of each layer are also obtained. Additionally, we investigate various synaptic characteristics, such as spike-timing dependent plasticity, spike-amplitude dependent plasticity, spike-rate dependent plasticity, spike-duration dependent plasticity, and spike-number dependent plasticity. This synapse emulation holds great potential for neuromorphic computing applications. Furthermore, potentiation and depression are manifested through identical pulses based on DC resistive switching. The pattern recognition rates within the neural network are evaluated, and based on the conductance changing linearly with incremental pulses, we achieve a pattern recognition accuracy of over 95%. Finally, the device's stability and synapse characteristics exhibit excellent potential for use in neuromorphic systems.
Files in This Item
There are no files associated with this item.
Appears in
Collections
College of Engineering > Department of Electronics and Electrical Engineering > 1. Journal Articles

qrcode

Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.

Related Researcher

Researcher Kim, Sung Jun photo

Kim, Sung Jun
College of Engineering (Department of Electronics and Electrical Engineering)
Read more

Altmetrics

Total Views & Downloads

BROWSE