Evidence of soft bound behaviour in analogue memristive devices for neuromorphic computing

Abstract The development of devices that can modulate their conductance under the application of electrical stimuli constitutes a fundamental step towards the realization of synaptic connectivity in neural networks. Optimization of synaptic functionality requires the understanding of the analogue co...

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Autores principales:	Jacopo Frascaroli, Stefano Brivio, Erika Covi, Sabina Spiga
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2018
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/1d5d6f4413e04217b7a43cd6966c0cba
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spelling	oai:doaj.org-article:1d5d6f4413e04217b7a43cd6966c0cba2021-12-02T11:40:35ZEvidence of soft bound behaviour in analogue memristive devices for neuromorphic computing10.1038/s41598-018-25376-x2045-2322https://doaj.org/article/1d5d6f4413e04217b7a43cd6966c0cba2018-05-01T00:00:00Zhttps://doi.org/10.1038/s41598-018-25376-xhttps://doaj.org/toc/2045-2322Abstract The development of devices that can modulate their conductance under the application of electrical stimuli constitutes a fundamental step towards the realization of synaptic connectivity in neural networks. Optimization of synaptic functionality requires the understanding of the analogue conductance update under different programming conditions. Moreover, properties of physical devices such as bounded conductance values and state-dependent modulation should be considered as they affect storage capacity and performance of the network. This work provides a study of the conductance dynamics produced by identical pulses as a function of the programming parameters in an HfO2 memristive device. The application of a phenomenological model that considers a soft approach to the conductance boundaries allows the identification of different operation regimes and to quantify conductance modulation in the analogue region. Device non-linear switching kinetics is recognized as the physical origin of the transition between different dynamics and motivates the crucial trade-off between degree of analog modulation and memory window. Different kinetics for the processes of conductance increase and decrease account for device programming asymmetry. The identification of programming trade-off together with an evaluation of device variations provide a guideline for the optimization of the analogue programming in view of hardware implementation of neural networks.Jacopo FrascaroliStefano BrivioErika CoviSabina SpigaNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 8, Iss 1, Pp 1-12 (2018)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Jacopo Frascaroli Stefano Brivio Erika Covi Sabina Spiga Evidence of soft bound behaviour in analogue memristive devices for neuromorphic computing
description	Abstract The development of devices that can modulate their conductance under the application of electrical stimuli constitutes a fundamental step towards the realization of synaptic connectivity in neural networks. Optimization of synaptic functionality requires the understanding of the analogue conductance update under different programming conditions. Moreover, properties of physical devices such as bounded conductance values and state-dependent modulation should be considered as they affect storage capacity and performance of the network. This work provides a study of the conductance dynamics produced by identical pulses as a function of the programming parameters in an HfO2 memristive device. The application of a phenomenological model that considers a soft approach to the conductance boundaries allows the identification of different operation regimes and to quantify conductance modulation in the analogue region. Device non-linear switching kinetics is recognized as the physical origin of the transition between different dynamics and motivates the crucial trade-off between degree of analog modulation and memory window. Different kinetics for the processes of conductance increase and decrease account for device programming asymmetry. The identification of programming trade-off together with an evaluation of device variations provide a guideline for the optimization of the analogue programming in view of hardware implementation of neural networks.
format	article
author	Jacopo Frascaroli Stefano Brivio Erika Covi Sabina Spiga
author_facet	Jacopo Frascaroli Stefano Brivio Erika Covi Sabina Spiga
author_sort	Jacopo Frascaroli
title	Evidence of soft bound behaviour in analogue memristive devices for neuromorphic computing
title_short	Evidence of soft bound behaviour in analogue memristive devices for neuromorphic computing
title_full	Evidence of soft bound behaviour in analogue memristive devices for neuromorphic computing
title_fullStr	Evidence of soft bound behaviour in analogue memristive devices for neuromorphic computing
title_full_unstemmed	Evidence of soft bound behaviour in analogue memristive devices for neuromorphic computing
title_sort	evidence of soft bound behaviour in analogue memristive devices for neuromorphic computing
publisher	Nature Portfolio
publishDate	2018
url	https://doaj.org/article/1d5d6f4413e04217b7a43cd6966c0cba
work_keys_str_mv	AT jacopofrascaroli evidenceofsoftboundbehaviourinanaloguememristivedevicesforneuromorphiccomputing AT stefanobrivio evidenceofsoftboundbehaviourinanaloguememristivedevicesforneuromorphiccomputing AT erikacovi evidenceofsoftboundbehaviourinanaloguememristivedevicesforneuromorphiccomputing AT sabinaspiga evidenceofsoftboundbehaviourinanaloguememristivedevicesforneuromorphiccomputing
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Evidence of soft bound behaviour in analogue memristive devices for neuromorphic computing

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