Low Switching Power Neuromorphic Perovskite Devices with Quick Relearning Functionality

Abstract In the quest to reduce energy consumption, there is a growing demand for technology beyond silicon as electronic materials for neuromorphic artificial intelligence devices. Equipped with the criteria of energy efficiency and excellent adaptability, organohalide perovskites can emulate the c...

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Main Authors: Dani S. Assi, Muhammed P.U. Haris, Vaithinathan Karthikeyan, Samrana Kazim, Shahzada Ahmad, Vellaisamy A. L. Roy
Format: Article
Language:English
Published: Wiley-VCH 2023-08-01
Series:Advanced Electronic Materials
Subjects:
Online Access:https://doi.org/10.1002/aelm.202300285
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author Dani S. Assi
Muhammed P.U. Haris
Vaithinathan Karthikeyan
Samrana Kazim
Shahzada Ahmad
Vellaisamy A. L. Roy
author_facet Dani S. Assi
Muhammed P.U. Haris
Vaithinathan Karthikeyan
Samrana Kazim
Shahzada Ahmad
Vellaisamy A. L. Roy
author_sort Dani S. Assi
collection DOAJ
description Abstract In the quest to reduce energy consumption, there is a growing demand for technology beyond silicon as electronic materials for neuromorphic artificial intelligence devices. Equipped with the criteria of energy efficiency and excellent adaptability, organohalide perovskites can emulate the characteristics of synaptic functions in the human brain. In this aspect, this study designs and develops CsFAPbI3‐based memristive neuromorphic devices that can switch at low power and show larger endurance by adopting the powder engineering methodology. The neuromorphic characteristics of the CsFAPbI3‐based devices exhibit an ultra‐high paired‐pulse facilitation index for an applied electric stimuli pulse. Moreover, the transition from short‐term to long‐term memory requires ultra‐low energy with long relaxation times. The learning and training cycles illustrate that the CsFAPbI3‐based devices exhibit faster learning and memorization process owing to their larger carrier lifetime compared to other perovskites. The results provide a pathway to attain low‐power neuromorphic devices that are synchronic to the human brain's performance.
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spelling doaj.art-eb06692cb86f4f28a85269198ef8bc0e2023-08-11T02:16:17ZengWiley-VCHAdvanced Electronic Materials2199-160X2023-08-0198n/an/a10.1002/aelm.202300285Low Switching Power Neuromorphic Perovskite Devices with Quick Relearning FunctionalityDani S. Assi0Muhammed P.U. Haris1Vaithinathan Karthikeyan2Samrana Kazim3Shahzada Ahmad4Vellaisamy A. L. Roy5Electronics and Nanoscale Engineering James Watt School of Engineering University of Glasgow Glasgow G12 8QQ UKBCMaterials Basque Center for Materials Applications and Nanostructures UPV/EHU Science Park Leioa 48940 SpainElectronics and Nanoscale Engineering James Watt School of Engineering University of Glasgow Glasgow G12 8QQ UKBCMaterials Basque Center for Materials Applications and Nanostructures UPV/EHU Science Park Leioa 48940 SpainBCMaterials Basque Center for Materials Applications and Nanostructures UPV/EHU Science Park Leioa 48940 SpainElectronics and Nanoscale Engineering James Watt School of Engineering University of Glasgow Glasgow G12 8QQ UKAbstract In the quest to reduce energy consumption, there is a growing demand for technology beyond silicon as electronic materials for neuromorphic artificial intelligence devices. Equipped with the criteria of energy efficiency and excellent adaptability, organohalide perovskites can emulate the characteristics of synaptic functions in the human brain. In this aspect, this study designs and develops CsFAPbI3‐based memristive neuromorphic devices that can switch at low power and show larger endurance by adopting the powder engineering methodology. The neuromorphic characteristics of the CsFAPbI3‐based devices exhibit an ultra‐high paired‐pulse facilitation index for an applied electric stimuli pulse. Moreover, the transition from short‐term to long‐term memory requires ultra‐low energy with long relaxation times. The learning and training cycles illustrate that the CsFAPbI3‐based devices exhibit faster learning and memorization process owing to their larger carrier lifetime compared to other perovskites. The results provide a pathway to attain low‐power neuromorphic devices that are synchronic to the human brain's performance.https://doi.org/10.1002/aelm.202300285artificial neural networksartificial synaptic devicesperovskitessynapses
spellingShingle Dani S. Assi
Muhammed P.U. Haris
Vaithinathan Karthikeyan
Samrana Kazim
Shahzada Ahmad
Vellaisamy A. L. Roy
Low Switching Power Neuromorphic Perovskite Devices with Quick Relearning Functionality
Advanced Electronic Materials
artificial neural networks
artificial synaptic devices
perovskites
synapses
title Low Switching Power Neuromorphic Perovskite Devices with Quick Relearning Functionality
title_full Low Switching Power Neuromorphic Perovskite Devices with Quick Relearning Functionality
title_fullStr Low Switching Power Neuromorphic Perovskite Devices with Quick Relearning Functionality
title_full_unstemmed Low Switching Power Neuromorphic Perovskite Devices with Quick Relearning Functionality
title_short Low Switching Power Neuromorphic Perovskite Devices with Quick Relearning Functionality
title_sort low switching power neuromorphic perovskite devices with quick relearning functionality
topic artificial neural networks
artificial synaptic devices
perovskites
synapses
url https://doi.org/10.1002/aelm.202300285
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AT muhammedpuharis lowswitchingpowerneuromorphicperovskitedeviceswithquickrelearningfunctionality
AT vaithinathankarthikeyan lowswitchingpowerneuromorphicperovskitedeviceswithquickrelearningfunctionality
AT samranakazim lowswitchingpowerneuromorphicperovskitedeviceswithquickrelearningfunctionality
AT shahzadaahmad lowswitchingpowerneuromorphicperovskitedeviceswithquickrelearningfunctionality
AT vellaisamyalroy lowswitchingpowerneuromorphicperovskitedeviceswithquickrelearningfunctionality