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HfO2-based memristors for neuromorphic applications

HfO2-based memristors for neuromorphic applications
HfO2-based memristors for neuromorphic applications
In recent years, biologically inspired systems, which emulate the nervous system of living beings, are becoming more and more requested due to their ability to solve ill-posed problems such as pattern recognition or interaction with the external environment. By virtue of their nanoscaled size and their tunable conductance, memristors are key elements to emulate high-density networks of biological synapses that regulate the communication efficacy among neurons and implement learning capability. We propose a TiN/ HfO2/Ti/TiN memristor as artificial synapse for neuromorphic architectures. The device can gradually change its conductance upon application of proper electrical stimuli. More specifically, it features gradual potentiation and depression when stimulated by trains of identical potentiating or depressing spikes, which are easy to be implemented on-chip. Moreover, we demonstrate that the memristor conductance can be regulated according to the delay time between two spikes incoming to the device terminals. This regulation of memristor conductance implements the typical biological learning process named Spike-Time-Dependent-Plasticity (STDP). Finally, collected STDP data were used to simulate a simple fully connected Spiking Neural Network (SNN) for pattern recognition.
393-396
IEEE
Covi, E.
2db55c46-182c-401d-ba4f-4020457e8d86
Brivo, S.
62a4c3e8-e7bf-4935-bf48-18bf88222b4f
Serb, A.
30f5ec26-f51d-42b3-85fd-0325a27a792c
Prodromakis, T.
d58c9c10-9d25-4d22-b155-06c8437acfbf
Fanciulli, M.
b1503882-bc16-411e-86ad-8201a26f1587
Spiga, S.
3e1f1c09-707f-4508-a31f-3d6bfaef7755
Covi, E.
2db55c46-182c-401d-ba4f-4020457e8d86
Brivo, S.
62a4c3e8-e7bf-4935-bf48-18bf88222b4f
Serb, A.
30f5ec26-f51d-42b3-85fd-0325a27a792c
Prodromakis, T.
d58c9c10-9d25-4d22-b155-06c8437acfbf
Fanciulli, M.
b1503882-bc16-411e-86ad-8201a26f1587
Spiga, S.
3e1f1c09-707f-4508-a31f-3d6bfaef7755

Covi, E., Brivo, S., Serb, A., Prodromakis, T., Fanciulli, M. and Spiga, S. (2016) HfO2-based memristors for neuromorphic applications. In 2016 IEEE International Symposium on Circuits and Systems (ISCAS). IEEE. pp. 393-396 . (doi:10.1109/ISCAS.2016.7527253).

Record type: Conference or Workshop Item (Paper)

Abstract

In recent years, biologically inspired systems, which emulate the nervous system of living beings, are becoming more and more requested due to their ability to solve ill-posed problems such as pattern recognition or interaction with the external environment. By virtue of their nanoscaled size and their tunable conductance, memristors are key elements to emulate high-density networks of biological synapses that regulate the communication efficacy among neurons and implement learning capability. We propose a TiN/ HfO2/Ti/TiN memristor as artificial synapse for neuromorphic architectures. The device can gradually change its conductance upon application of proper electrical stimuli. More specifically, it features gradual potentiation and depression when stimulated by trains of identical potentiating or depressing spikes, which are easy to be implemented on-chip. Moreover, we demonstrate that the memristor conductance can be regulated according to the delay time between two spikes incoming to the device terminals. This regulation of memristor conductance implements the typical biological learning process named Spike-Time-Dependent-Plasticity (STDP). Finally, collected STDP data were used to simulate a simple fully connected Spiking Neural Network (SNN) for pattern recognition.

Full text not available from this repository.

More information

Accepted/In Press date: 2016
e-pub ahead of print date: 11 August 2016
Published date: 11 August 2016
Venue - Dates: IEEE International Symposium in Circuits and System, Montreal, Canada, 2016-05-22 - 2016-05-25
Organisations: Nanoelectronics and Nanotechnology

Identifiers

Local EPrints ID: 386365
URI: https://eprints.soton.ac.uk/id/eprint/386365
PURE UUID: 5a7c95d2-ce40-4af7-b9ed-9fef2f82ca79
ORCID for T. Prodromakis: ORCID iD orcid.org/0000-0002-6267-6909

Catalogue record

Date deposited: 05 Feb 2016 16:31
Last modified: 10 Dec 2019 01:35

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