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Improving linearity by introducing Al in HfO2 as a memristor synapse device

Improving linearity by introducing Al in HfO2 as a memristor synapse device
Improving linearity by introducing Al in HfO2 as a memristor synapse device
Artificial synapse having good linearity is crucial to achieve an efficient learning process in neuromorphic computing. It is found that the synaptic linearity can be enhanced by engineering the doping region across the switching layer. The nonlinearity of potentiation and depression of the pure device is 36% and 91%, respectively; meanwhile, the nonlinearity after doping can be suppressed to be 22% (potentiation) and 60% (depression). Henceforth, the learning accuracy of the doped device is 91% with only 13 iterations; meanwhile, the pure device is 78%. A detailed conduction mechanism to understand this phenomenon is proposed.
0957-4484
Chandrasekaran, Sridhar
f822e829-d5fb-4150-8f55-53634a1705da
Simanjuntak, Firman Mangasa
a5b8dd07-002c-4520-9f67-2dc20d2ff0d5
Saminathan, R.
667e5d1a-3e48-4b51-92b2-f146e7ac2a83
Panda, Debashis
0de17b04-a876-49e8-91d7-1eb50a34cf43
Tseng, Tseung-Yuen
c284f1b3-a030-4b56-bc22-5bfa2d9650df
Chandrasekaran, Sridhar
f822e829-d5fb-4150-8f55-53634a1705da
Simanjuntak, Firman Mangasa
a5b8dd07-002c-4520-9f67-2dc20d2ff0d5
Saminathan, R.
667e5d1a-3e48-4b51-92b2-f146e7ac2a83
Panda, Debashis
0de17b04-a876-49e8-91d7-1eb50a34cf43
Tseng, Tseung-Yuen
c284f1b3-a030-4b56-bc22-5bfa2d9650df

Chandrasekaran, Sridhar, Simanjuntak, Firman Mangasa, Saminathan, R., Panda, Debashis and Tseng, Tseung-Yuen (2019) Improving linearity by introducing Al in HfO2 as a memristor synapse device. Nanotechnology. (doi:10.1088/1361-6528/ab3480).

Record type: Article

Abstract

Artificial synapse having good linearity is crucial to achieve an efficient learning process in neuromorphic computing. It is found that the synaptic linearity can be enhanced by engineering the doping region across the switching layer. The nonlinearity of potentiation and depression of the pure device is 36% and 91%, respectively; meanwhile, the nonlinearity after doping can be suppressed to be 22% (potentiation) and 60% (depression). Henceforth, the learning accuracy of the doped device is 91% with only 13 iterations; meanwhile, the pure device is 78%. A detailed conduction mechanism to understand this phenomenon is proposed.

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Published date: 20 August 2019

Identifiers

Local EPrints ID: 448756
URI: http://eprints.soton.ac.uk/id/eprint/448756
ISSN: 0957-4484
PURE UUID: 4f839f3b-e46f-4cae-b026-02e1542b9cc7

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Date deposited: 05 May 2021 16:30
Last modified: 14 Sep 2021 20:43

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Contributors

Author: Sridhar Chandrasekaran
Author: Firman Mangasa Simanjuntak
Author: R. Saminathan
Author: Debashis Panda
Author: Tseung-Yuen Tseng

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