Simulation of Low Power Self-Selective Memristive Neural Networks for in situ Digital and Analogue Artificial Neural Network Applications-Reference-Cited by-同舟云学术

Simulation of Low Power Self-Selective Memristive Neural Networks for in situ Digital and Analogue Artificial Neural Network Applications

Published:2022 Issue: Volume:21 Page:505-513
ISSN:1536-125X
Container-title:IEEE Transactions on Nanotechnology
language:
Short-container-title:IEEE Trans. Nanotechnology

Author:

Tsioustas Charalampos¹^ORCID,Bousoulas Panagiotis¹^ORCID,Hadfield Jack¹,Chatzinikolaou Theodoros Panagiotis²,Fyrigos Iosif-Angelos²^ORCID,Ntinas Vasileios²^ORCID,Tsompanas Michail-Antisthenis²,Sirakoulis Georgios Ch.²^ORCID,Tsoukalas Dimitris¹^ORCID

Affiliation:

1. Department of Physics, School of Applied Sciences, National Technical University of Athens, Athens, Greece

2. Department of Electrical and Computer Engineering, Democritus University of Thrace, Xanthi, Greece

Funder

Hellenic Foundation for Research and Innovation

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Computer Science Applications

Link

http://xplorestaging.ieee.org/ielx7/7729/9682520/09887886.pdf?arnumber=9887886

Reference42 articles.

1. Reconfigurable Nonvolatile Logic Operations in Resistance Switching Crossbar Array for Large-Scale Circuits

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4. ZnO-based one diode-one resistor device structure for crossbar memory applications

5. Thermophoresis/diffusion as a plausible mechanism for unipolar resistive switching in metal–oxide–metal memristors

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