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Bipolar Analog Memristors as Artificial Synapses for Neuromorphic Computing

Authors :
Rui Wang
Tuo Shi
Xumeng Zhang
Wei Wang
Jinsong Wei
Jian Lu
Xiaolong Zhao
Zuheng Wu
Rongrong Cao
Shibing Long
Qi Liu
Ming Liu
Source :
Materials, Vol 11, Iss 11, p 2102 (2018)
Publication Year :
2018
Publisher :
MDPI AG, 2018.

Abstract

Synaptic devices with bipolar analog resistive switching behavior are the building blocks for memristor-based neuromorphic computing. In this work, a fully complementary metal-oxide semiconductor (CMOS)-compatible, forming-free, and non-filamentary memristive device (Pd/Al2O3/TaOx/Ta) with bipolar analog switching behavior is reported as an artificial synapse for neuromorphic computing. Synaptic functions, including long-term potentiation/depression, paired-pulse facilitation (PPF), and spike-timing-dependent plasticity (STDP), are implemented based on this device; the switching energy is around 50 pJ per spike. Furthermore, for applications in artificial neural networks (ANN), determined target conductance states with little deviation (

Details

Language :
English
ISSN :
19961944
Volume :
11
Issue :
11
Database :
Directory of Open Access Journals
Journal :
Materials
Publication Type :
Academic Journal
Accession number :
edsdoj.1d894dbfa6f4dc1a07c97a4e651fccd
Document Type :
article
Full Text :
https://doi.org/10.3390/ma11112102