Synaptic devices based on HfO2 memristors

Mireia Bargalló Gonzalez; Marcos Maestro-Izquierdo; Samuel Poblador; Miguel Zabala; Francesca Campabadal; Gerardo González-Cordero; Samuel Aldana; David Maldonado; Francisco Jiménez-Molinos; Juan Bautista Roldán Aranda

doi:10.1016/b978-0-12-821184-7.00028-1

Synaptic devices based on HfO2 memristors

Mireia Bargalló Gonzalez
, Marcos Maestro-Izquierdo
, Samuel Poblador
, Miguel Zabala
, Francesca Campabadal
, Gerardo González-Cordero
, Samuel Aldana
, David Maldonado
, Francisco Jiménez-Molinos
, Juan Bautista Roldán Aranda

Institut de Microelectrònica de Barcelona, IMB-CNM (CSIC)
University of Granada
University of Granada

Research output: Chapter in Book/Report/Conference proceedings › Chapter

Abstract

Nanodevices based on resistive switching structures are currently being explored as potential candidates to mimic biological synapses in neuromorphic systems due to the analog control of the device resistance, their scaling capability, and their low power operation. In this chapter, the electronic properties and the conductance modulation capability of HfO2-based memristors will be presented, and their potential to emulate the synaptic functionality in biological-inspired neuromorphic networks will be discussed. In addition, the stability and reliability issues that may limit the performance of resistive synaptic devices will be discussed. Finally, physical simulations at different complexity levels will be presented providing a deeper insight into the key transport mechanisms involved in the resistive switching phenomenon.

Original language	English
Title of host publication	Mem-elements for Neuromorphic Circuits with Artificial Intelligence Applications
Publisher	Academic Press
Chapter	19
Pages	383-426
ISBN (Print)	9780128211847
DOIs	https://doi.org/10.1016/b978-0-12-821184-7.00028-1
Publication status	Published - 1 Jan 2021
Externally published	Yes

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10.1016/b978-0-12-821184-7.00028-1

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Bargalló Gonzalez, M., Maestro-Izquierdo, M., Poblador, S., Zabala, M., Campabadal, F., González-Cordero, G., Aldana, S., Maldonado, D., Jiménez-Molinos, F., & Roldán Aranda, J. B. (2021). Synaptic devices based on HfO2 memristors. In Mem-elements for Neuromorphic Circuits with Artificial Intelligence Applications (pp. 383-426). Academic Press. https://doi.org/10.1016/b978-0-12-821184-7.00028-1

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title = "Synaptic devices based on HfO2 memristors",

abstract = "Nanodevices based on resistive switching structures are currently being explored as potential candidates to mimic biological synapses in neuromorphic systems due to the analog control of the device resistance, their scaling capability, and their low power operation. In this chapter, the electronic properties and the conductance modulation capability of HfO2-based memristors will be presented, and their potential to emulate the synaptic functionality in biological-inspired neuromorphic networks will be discussed. In addition, the stability and reliability issues that may limit the performance of resistive synaptic devices will be discussed. Finally, physical simulations at different complexity levels will be presented providing a deeper insight into the key transport mechanisms involved in the resistive switching phenomenon.",

author = "\{Bargall{\'o} Gonzalez\}, Mireia and Marcos Maestro-Izquierdo and Samuel Poblador and Miguel Zabala and Francesca Campabadal and Gerardo Gonz{\'a}lez-Cordero and Samuel Aldana and David Maldonado and Francisco Jim{\'e}nez-Molinos and \{Rold{\'a}n Aranda\}, \{Juan Bautista\}",

year = "2021",

month = jan,

day = "1",

doi = "10.1016/b978-0-12-821184-7.00028-1",

language = "English",

isbn = "9780128211847",

pages = "383--426",

booktitle = "Mem-elements for Neuromorphic Circuits with Artificial Intelligence Applications",

publisher = "Academic Press",

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}

Bargalló Gonzalez, M, Maestro-Izquierdo, M, Poblador, S, Zabala, M, Campabadal, F, González-Cordero, G, Aldana, S, Maldonado, D, Jiménez-Molinos, F & Roldán Aranda, JB 2021, Synaptic devices based on HfO2 memristors. in Mem-elements for Neuromorphic Circuits with Artificial Intelligence Applications. Academic Press, pp. 383-426. https://doi.org/10.1016/b978-0-12-821184-7.00028-1

TY - CHAP

T1 - Synaptic devices based on HfO2 memristors

AU - Bargalló Gonzalez, Mireia

AU - Maestro-Izquierdo, Marcos

AU - Poblador, Samuel

AU - Zabala, Miguel

AU - Campabadal, Francesca

AU - González-Cordero, Gerardo

AU - Aldana, Samuel

AU - Maldonado, David

AU - Jiménez-Molinos, Francisco

AU - Roldán Aranda, Juan Bautista

PY - 2021/1/1

Y1 - 2021/1/1

N2 - Nanodevices based on resistive switching structures are currently being explored as potential candidates to mimic biological synapses in neuromorphic systems due to the analog control of the device resistance, their scaling capability, and their low power operation. In this chapter, the electronic properties and the conductance modulation capability of HfO2-based memristors will be presented, and their potential to emulate the synaptic functionality in biological-inspired neuromorphic networks will be discussed. In addition, the stability and reliability issues that may limit the performance of resistive synaptic devices will be discussed. Finally, physical simulations at different complexity levels will be presented providing a deeper insight into the key transport mechanisms involved in the resistive switching phenomenon.

AB - Nanodevices based on resistive switching structures are currently being explored as potential candidates to mimic biological synapses in neuromorphic systems due to the analog control of the device resistance, their scaling capability, and their low power operation. In this chapter, the electronic properties and the conductance modulation capability of HfO2-based memristors will be presented, and their potential to emulate the synaptic functionality in biological-inspired neuromorphic networks will be discussed. In addition, the stability and reliability issues that may limit the performance of resistive synaptic devices will be discussed. Finally, physical simulations at different complexity levels will be presented providing a deeper insight into the key transport mechanisms involved in the resistive switching phenomenon.

UR - http://dx.doi.org/10.1016/b978-0-12-821184-7.00028-1

U2 - 10.1016/b978-0-12-821184-7.00028-1

DO - 10.1016/b978-0-12-821184-7.00028-1

M3 - Chapter

SN - 9780128211847

SP - 383

EP - 426

BT - Mem-elements for Neuromorphic Circuits with Artificial Intelligence Applications

PB - Academic Press

ER -

Synaptic devices based on HfO2 memristors

Abstract

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