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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Format: | Article |
Language: | English |
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Wiley-VCH
2023-08-01
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Series: | Advanced Electronic Materials |
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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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format | Article |
id | doaj.art-eb06692cb86f4f28a85269198ef8bc0e |
institution | Directory Open Access Journal |
issn | 2199-160X |
language | English |
last_indexed | 2024-03-12T15:22:17Z |
publishDate | 2023-08-01 |
publisher | Wiley-VCH |
record_format | Article |
series | Advanced Electronic Materials |
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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