Network modelling of avalanche dynamics in Ag-hBN memristor
Certain single memristors and self-assembled neuromorphic networks exhibit correlated electrical noise similar to that found in biological systems. Hence, they can serve as promising platforms to test whether such correlated noise brings any advantage in performing low power learning tasks. These sy...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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IOP Publishing
2025-01-01
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| Series: | Nano Express |
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| Online Access: | https://doi.org/10.1088/2632-959X/adf7c4 |
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| _version_ | 1849238716281782272 |
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| author | Vivek Dey Steffen Kampmann Ankit Rao Rafael Gutierrez Gianaurelio Cuniberti Pavan Nukala |
| author_facet | Vivek Dey Steffen Kampmann Ankit Rao Rafael Gutierrez Gianaurelio Cuniberti Pavan Nukala |
| author_sort | Vivek Dey |
| collection | DOAJ |
| description | Certain single memristors and self-assembled neuromorphic networks exhibit correlated electrical noise similar to that found in biological systems. Hence, they can serve as promising platforms to test whether such correlated noise brings any advantage in performing low power learning tasks. These systems are characterized by spatiotemporal avalanches and their crackling behavior, and developing robust physical modeling of them is a crucial step in understanding their computing abilities. Here, we use a network theory-based approach to provide a physical model for percolative tunelling network, found in Ag-hBN memristive system, consisting of nodes (atomic clusters) of Ag intercalated in the hBN van der Waals layers. By modeling a single edge plasticity through constitutive electrochemical filament formation and annihilation through Joule heating, we identify independent parameters that determine the percolative network connectivity. We construct a parameter space map through the percolative network connectivity parameters and show that a small region of the parameter space contains signals which are long-range temporally correlated, but only a subset of them display self-organized criticality. |
| format | Article |
| id | doaj-art-b3b9ebc47b5c47d8bde65090225f971b |
| institution | Kabale University |
| issn | 2632-959X |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IOP Publishing |
| record_format | Article |
| series | Nano Express |
| spelling | doaj-art-b3b9ebc47b5c47d8bde65090225f971b2025-08-20T04:01:25ZengIOP PublishingNano Express2632-959X2025-01-016303501010.1088/2632-959X/adf7c4Network modelling of avalanche dynamics in Ag-hBN memristorVivek Dey0Steffen Kampmann1Ankit Rao2Rafael Gutierrez3https://orcid.org/0000-0001-8121-8041Gianaurelio Cuniberti4Pavan Nukala5https://orcid.org/0000-0002-3988-4224Centre for Nano Science and Engineering, Indian Institute of Science , Bengaluru, 560012, IndiaInstitute for Materials Science and Max Bergmann Centre of Biomaterials, Technische Universität Dresden , 01062, Dresden, GermanyCentre for Nano Science and Engineering, Indian Institute of Science , Bengaluru, 560012, IndiaInstitute for Materials Science and Max Bergmann Centre of Biomaterials, Technische Universität Dresden , 01062, Dresden, GermanyInstitute for Materials Science and Max Bergmann Centre of Biomaterials, Technische Universität Dresden , 01062, Dresden, Germany; Dresden Centre for Computational Materials Science (DCMS), Technische Universität Dresden , 01062, Dresden, GermanyCentre for Nano Science and Engineering, Indian Institute of Science , Bengaluru, 560012, IndiaCertain single memristors and self-assembled neuromorphic networks exhibit correlated electrical noise similar to that found in biological systems. Hence, they can serve as promising platforms to test whether such correlated noise brings any advantage in performing low power learning tasks. These systems are characterized by spatiotemporal avalanches and their crackling behavior, and developing robust physical modeling of them is a crucial step in understanding their computing abilities. Here, we use a network theory-based approach to provide a physical model for percolative tunelling network, found in Ag-hBN memristive system, consisting of nodes (atomic clusters) of Ag intercalated in the hBN van der Waals layers. By modeling a single edge plasticity through constitutive electrochemical filament formation and annihilation through Joule heating, we identify independent parameters that determine the percolative network connectivity. We construct a parameter space map through the percolative network connectivity parameters and show that a small region of the parameter space contains signals which are long-range temporally correlated, but only a subset of them display self-organized criticality.https://doi.org/10.1088/2632-959X/adf7c4hexagonal boron nitridememristoravalanchenetwork theoryself-organised criticality |
| spellingShingle | Vivek Dey Steffen Kampmann Ankit Rao Rafael Gutierrez Gianaurelio Cuniberti Pavan Nukala Network modelling of avalanche dynamics in Ag-hBN memristor Nano Express hexagonal boron nitride memristor avalanche network theory self-organised criticality |
| title | Network modelling of avalanche dynamics in Ag-hBN memristor |
| title_full | Network modelling of avalanche dynamics in Ag-hBN memristor |
| title_fullStr | Network modelling of avalanche dynamics in Ag-hBN memristor |
| title_full_unstemmed | Network modelling of avalanche dynamics in Ag-hBN memristor |
| title_short | Network modelling of avalanche dynamics in Ag-hBN memristor |
| title_sort | network modelling of avalanche dynamics in ag hbn memristor |
| topic | hexagonal boron nitride memristor avalanche network theory self-organised criticality |
| url | https://doi.org/10.1088/2632-959X/adf7c4 |
| work_keys_str_mv | AT vivekdey networkmodellingofavalanchedynamicsinaghbnmemristor AT steffenkampmann networkmodellingofavalanchedynamicsinaghbnmemristor AT ankitrao networkmodellingofavalanchedynamicsinaghbnmemristor AT rafaelgutierrez networkmodellingofavalanchedynamicsinaghbnmemristor AT gianaureliocuniberti networkmodellingofavalanchedynamicsinaghbnmemristor AT pavannukala networkmodellingofavalanchedynamicsinaghbnmemristor |