Graphene-based RRAM devices for neural computing
Resistive random access memory is very well known for its potential application in in-memory and neural computing. However, they often have different types of device-to-device and cycle-to-cycle variability. This makes it harder to build highly accurate crossbar arrays. Traditional RRAM designs make...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2023-10-01
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| Series: | Frontiers in Neuroscience |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fnins.2023.1253075/full |
| _version_ | 1797662472461615104 |
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| author | Rajalekshmi T. R Rinku Rani Das Chithra Reghuvaran Alex James |
| author_facet | Rajalekshmi T. R Rinku Rani Das Chithra Reghuvaran Alex James |
| author_sort | Rajalekshmi T. R |
| collection | DOAJ |
| description | Resistive random access memory is very well known for its potential application in in-memory and neural computing. However, they often have different types of device-to-device and cycle-to-cycle variability. This makes it harder to build highly accurate crossbar arrays. Traditional RRAM designs make use of various filament-based oxide materials for creating a channel that is sandwiched between two electrodes to form a two-terminal structure. They are often subjected to mechanical and electrical stress over repeated read-and-write cycles. The behavior of these devices often varies in practice across wafer arrays over these stresses when fabricated. The use of emerging 2D materials is explored to improve electrical endurance, long retention time, high switching speed, and fewer power losses. This study provides an in-depth exploration of neuro-memristive computing and its potential applications, focusing specifically on the utilization of graphene and 2D materials in RRAM for neural computing. The study presents a comprehensive analysis of the structural and design aspects of graphene-based RRAM, along with a thorough examination of commercially available RRAM models and their fabrication techniques. Furthermore, the study investigates the diverse range of applications that can benefit from graphene-based RRAM devices. |
| first_indexed | 2024-03-11T19:00:40Z |
| format | Article |
| id | doaj.art-a4c17701382f492f8c70a4ec25384dd5 |
| institution | Directory Open Access Journal |
| issn | 1662-453X |
| language | English |
| last_indexed | 2024-03-11T19:00:40Z |
| publishDate | 2023-10-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Neuroscience |
| spelling | doaj.art-a4c17701382f492f8c70a4ec25384dd52023-10-10T13:19:32ZengFrontiers Media S.A.Frontiers in Neuroscience1662-453X2023-10-011710.3389/fnins.2023.12530751253075Graphene-based RRAM devices for neural computingRajalekshmi T. RRinku Rani DasChithra ReghuvaranAlex JamesResistive random access memory is very well known for its potential application in in-memory and neural computing. However, they often have different types of device-to-device and cycle-to-cycle variability. This makes it harder to build highly accurate crossbar arrays. Traditional RRAM designs make use of various filament-based oxide materials for creating a channel that is sandwiched between two electrodes to form a two-terminal structure. They are often subjected to mechanical and electrical stress over repeated read-and-write cycles. The behavior of these devices often varies in practice across wafer arrays over these stresses when fabricated. The use of emerging 2D materials is explored to improve electrical endurance, long retention time, high switching speed, and fewer power losses. This study provides an in-depth exploration of neuro-memristive computing and its potential applications, focusing specifically on the utilization of graphene and 2D materials in RRAM for neural computing. The study presents a comprehensive analysis of the structural and design aspects of graphene-based RRAM, along with a thorough examination of commercially available RRAM models and their fabrication techniques. Furthermore, the study investigates the diverse range of applications that can benefit from graphene-based RRAM devices.https://www.frontiersin.org/articles/10.3389/fnins.2023.1253075/fullchemical vapor deposition (CVD)cryptographygrapheneneuromorphic computingresistive random access memory (RRAM) |
| spellingShingle | Rajalekshmi T. R Rinku Rani Das Chithra Reghuvaran Alex James Graphene-based RRAM devices for neural computing Frontiers in Neuroscience chemical vapor deposition (CVD) cryptography graphene neuromorphic computing resistive random access memory (RRAM) |
| title | Graphene-based RRAM devices for neural computing |
| title_full | Graphene-based RRAM devices for neural computing |
| title_fullStr | Graphene-based RRAM devices for neural computing |
| title_full_unstemmed | Graphene-based RRAM devices for neural computing |
| title_short | Graphene-based RRAM devices for neural computing |
| title_sort | graphene based rram devices for neural computing |
| topic | chemical vapor deposition (CVD) cryptography graphene neuromorphic computing resistive random access memory (RRAM) |
| url | https://www.frontiersin.org/articles/10.3389/fnins.2023.1253075/full |
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