Memristive and Synaptic Characteristics of Nitride-Based Heterostructures on Si Substrate

Brain-inspired artificial synaptic devices and neurons have the potential for application in future neuromorphic computing as they consume low energy. In this study, the memristive switching characteristics of a nitride-based device with two amorphous layers (SiN/BN) is investigated. We demonstrate...

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Main Authors: Mehr Khalid Rahmani, Min-Hwi Kim, Fayyaz Hussain, Yawar Abbas, Muhammad Ismail, Kyungho Hong, Chandreswar Mahata, Changhwan Choi, Byung-Gook Park, Sungjun Kim
Format: Article
Language:English
Published: MDPI AG 2020-05-01
Series:Nanomaterials
Subjects:
Online Access:https://www.mdpi.com/2079-4991/10/5/994
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spelling doaj-4c08e1cfb1fb4809bc0c7dfd2b9871972020-11-25T03:51:00ZengMDPI AGNanomaterials2079-49912020-05-011099499410.3390/nano10050994Memristive and Synaptic Characteristics of Nitride-Based Heterostructures on Si SubstrateMehr Khalid Rahmani0Min-Hwi Kim1Fayyaz Hussain2Yawar Abbas3Muhammad Ismail4Kyungho Hong5Chandreswar Mahata6Changhwan Choi7Byung-Gook Park8Sungjun Kim9School of Electronics Engineering, Chungbuk National University, Cheongju 28644, KoreaInter-University Semiconductor Research Center (ISRC) and the Department of Electrical and Computer Engineering, Seoul National University, Seoul 08826, KoreaMaterials Simulation Research Laboratory (MSRL), Department of Physics, Bahauddin Zakariya University Multan Pakistan, Multan 60800, PakistanDepartment of Physics, Khalifa University, Abu Dhabi 127788, UAESchool of Electronics Engineering, Chungbuk National University, Cheongju 28644, KoreaInter-University Semiconductor Research Center (ISRC) and the Department of Electrical and Computer Engineering, Seoul National University, Seoul 08826, KoreaSchool of Electronics Engineering, Chungbuk National University, Cheongju 28644, KoreaDivision of Materials Science and Engineering, Hanyang University, Seoul 04763, KoreaInter-University Semiconductor Research Center (ISRC) and the Department of Electrical and Computer Engineering, Seoul National University, Seoul 08826, KoreaDivision of Electronics and Electrical Engineering, Dongguk University, Seoul 04620, KoreaBrain-inspired artificial synaptic devices and neurons have the potential for application in future neuromorphic computing as they consume low energy. In this study, the memristive switching characteristics of a nitride-based device with two amorphous layers (SiN/BN) is investigated. We demonstrate the coexistence of filamentary (abrupt) and interface (homogeneous) switching of Ni/SiN/BN/n<sup>++</sup>-Si devices. A better gradual conductance modulation is achieved for interface-type switching as compared with filamentary switching for an artificial synaptic device using appropriate voltage pulse stimulations. The improved classification accuracy for the interface switching (85.6%) is confirmed and compared to the accuracy of the filamentary switching mode (75.1%) by a three-layer neural network (784 × 128 × 10). Furthermore, the spike-timing-dependent plasticity characteristics of the synaptic device are also demonstrated. The results indicate the possibility of achieving an artificial synapse with a bilayer SiN/BN structure.https://www.mdpi.com/2079-4991/10/5/994memristorsilicon nitrideboron nitrideneuromorphic computingresistive switching
collection DOAJ
language English
format Article
sources DOAJ
author Mehr Khalid Rahmani
Min-Hwi Kim
Fayyaz Hussain
Yawar Abbas
Muhammad Ismail
Kyungho Hong
Chandreswar Mahata
Changhwan Choi
Byung-Gook Park
Sungjun Kim
spellingShingle Mehr Khalid Rahmani
Min-Hwi Kim
Fayyaz Hussain
Yawar Abbas
Muhammad Ismail
Kyungho Hong
Chandreswar Mahata
Changhwan Choi
Byung-Gook Park
Sungjun Kim
Memristive and Synaptic Characteristics of Nitride-Based Heterostructures on Si Substrate
Nanomaterials
memristor
silicon nitride
boron nitride
neuromorphic computing
resistive switching
author_facet Mehr Khalid Rahmani
Min-Hwi Kim
Fayyaz Hussain
Yawar Abbas
Muhammad Ismail
Kyungho Hong
Chandreswar Mahata
Changhwan Choi
Byung-Gook Park
Sungjun Kim
author_sort Mehr Khalid Rahmani
title Memristive and Synaptic Characteristics of Nitride-Based Heterostructures on Si Substrate
title_short Memristive and Synaptic Characteristics of Nitride-Based Heterostructures on Si Substrate
title_full Memristive and Synaptic Characteristics of Nitride-Based Heterostructures on Si Substrate
title_fullStr Memristive and Synaptic Characteristics of Nitride-Based Heterostructures on Si Substrate
title_full_unstemmed Memristive and Synaptic Characteristics of Nitride-Based Heterostructures on Si Substrate
title_sort memristive and synaptic characteristics of nitride-based heterostructures on si substrate
publisher MDPI AG
series Nanomaterials
issn 2079-4991
publishDate 2020-05-01
description Brain-inspired artificial synaptic devices and neurons have the potential for application in future neuromorphic computing as they consume low energy. In this study, the memristive switching characteristics of a nitride-based device with two amorphous layers (SiN/BN) is investigated. We demonstrate the coexistence of filamentary (abrupt) and interface (homogeneous) switching of Ni/SiN/BN/n<sup>++</sup>-Si devices. A better gradual conductance modulation is achieved for interface-type switching as compared with filamentary switching for an artificial synaptic device using appropriate voltage pulse stimulations. The improved classification accuracy for the interface switching (85.6%) is confirmed and compared to the accuracy of the filamentary switching mode (75.1%) by a three-layer neural network (784 × 128 × 10). Furthermore, the spike-timing-dependent plasticity characteristics of the synaptic device are also demonstrated. The results indicate the possibility of achieving an artificial synapse with a bilayer SiN/BN structure.
topic memristor
silicon nitride
boron nitride
neuromorphic computing
resistive switching
url https://www.mdpi.com/2079-4991/10/5/994
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