Novel Solution‐Processed Fe2O3/WS2 Hybrid Nanocomposite Dynamic Memristor for Advanced Power Efficiency in Neuromorphic Computing
Abstract Non‐volatile memory (NVM) based neuromorphic computing, which is inspired by the human brain, is a compelling paradigm in regard to building energy‐efficient computing hardware that is tailored for artificial intelligence. However, the current state of the art NVMs are facing challenges wit...
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| Format: | Article |
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Wiley
2025-05-01
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| Series: | Advanced Science |
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| Online Access: | https://doi.org/10.1002/advs.202408133 |
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| author | Faisal Ghafoor Honggyun Kim Bilal Ghafoor Zaheer Ahmed Muhammad Farooq Khan Muhammad Rabeel Muhammad Faheem Maqsood Sobia Nasir Wajid Zulfiqar Ghulam Dastageer Myoung‐Jae Lee Deok‐kee Kim |
| author_facet | Faisal Ghafoor Honggyun Kim Bilal Ghafoor Zaheer Ahmed Muhammad Farooq Khan Muhammad Rabeel Muhammad Faheem Maqsood Sobia Nasir Wajid Zulfiqar Ghulam Dastageer Myoung‐Jae Lee Deok‐kee Kim |
| author_sort | Faisal Ghafoor |
| collection | DOAJ |
| description | Abstract Non‐volatile memory (NVM) based neuromorphic computing, which is inspired by the human brain, is a compelling paradigm in regard to building energy‐efficient computing hardware that is tailored for artificial intelligence. However, the current state of the art NVMs are facing challenges with low operating voltages, energy efficiencies, and high densities in order to meet the new computing system beyond Moore's law. It is therefore necessary to develop novel hybrid materials with controlled compositional dynamics is crucial for initiating memristor devices capable of low‐power operations. This study validates the effectiveness of Ag/Fe90W10/Pt hybrid nanocomposite memristor devices, demonstrating superior performance including ultra‐low voltage operation, high stability, reproducibility, exceptional endurance (105 cycles), environmental resilience, and low energy consumption of 0.072 pJ. Moreover, the memristor exhibits the ability to emulate essential biological synaptic mechanisms. The resistive switching phenomenon is primarily attributed to the controlled filament formation along unique heterophase grain boundaries. Furthermore, the hybrid nanocomposite synaptic device achieved an image recognition accuracy of 94.3% in Artificial Neural Network (ANN) simulations by using the Modified National Institute of Standards and Technology (MNIST) dataset. These results imply that the device's performance has promising implications for facilitating efficient neuromorphic architectures in the future. |
| format | Article |
| id | doaj-art-b8aef24d835d46e3a57d27c93c297324 |
| institution | DOAJ |
| issn | 2198-3844 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advanced Science |
| spelling | doaj-art-b8aef24d835d46e3a57d27c93c2973242025-08-20T02:56:12ZengWileyAdvanced Science2198-38442025-05-011217n/an/a10.1002/advs.202408133Novel Solution‐Processed Fe2O3/WS2 Hybrid Nanocomposite Dynamic Memristor for Advanced Power Efficiency in Neuromorphic ComputingFaisal Ghafoor0Honggyun Kim1Bilal Ghafoor2Zaheer Ahmed3Muhammad Farooq Khan4Muhammad Rabeel5Muhammad Faheem Maqsood6Sobia Nasir7Wajid Zulfiqar8Ghulam Dastageer9Myoung‐Jae Lee10Deok‐kee Kim11Department of Electrical Engineering and Convergence Engineering for Intelligent Drone Sejong University Seoul 05006 Republic of KoreaDepartment of Semiconductor Systems Engineering Sejong University Seoul 05006 Republic of KoreaSchool of Materials Science and Engineering Shanghai University Shanghai 200444 ChinaDepartment of Electrical Engineering and Convergence Engineering for Intelligent Drone Sejong University Seoul 05006 Republic of KoreaDepartment of Electrical Engineering and Convergence Engineering for Intelligent Drone Sejong University Seoul 05006 Republic of KoreaDepartment of Electrical Engineering and Convergence Engineering for Intelligent Drone Sejong University Seoul 05006 Republic of KoreaMaterial Science and Engineering Program College of Arts and Science American University of Sharjah Sharjah 26666 UAEDepartment of Electrical Engineering and Convergence Engineering for Intelligent Drone Sejong University Seoul 05006 Republic of KoreaDepartment of Electrical Engineering and Convergence Engineering for Intelligent Drone Sejong University Seoul 05006 Republic of KoreaDepartment of Physics and Astronomy Sejong University Seoul 05006 South KoreaInstitute of Conversion Daegu Gyeongbuk Institute of Science and Technology (DGIST) Daegu 42988 South KoreaDepartment of Electrical Engineering and Convergence Engineering for Intelligent Drone Sejong University Seoul 05006 Republic of KoreaAbstract Non‐volatile memory (NVM) based neuromorphic computing, which is inspired by the human brain, is a compelling paradigm in regard to building energy‐efficient computing hardware that is tailored for artificial intelligence. However, the current state of the art NVMs are facing challenges with low operating voltages, energy efficiencies, and high densities in order to meet the new computing system beyond Moore's law. It is therefore necessary to develop novel hybrid materials with controlled compositional dynamics is crucial for initiating memristor devices capable of low‐power operations. This study validates the effectiveness of Ag/Fe90W10/Pt hybrid nanocomposite memristor devices, demonstrating superior performance including ultra‐low voltage operation, high stability, reproducibility, exceptional endurance (105 cycles), environmental resilience, and low energy consumption of 0.072 pJ. Moreover, the memristor exhibits the ability to emulate essential biological synaptic mechanisms. The resistive switching phenomenon is primarily attributed to the controlled filament formation along unique heterophase grain boundaries. Furthermore, the hybrid nanocomposite synaptic device achieved an image recognition accuracy of 94.3% in Artificial Neural Network (ANN) simulations by using the Modified National Institute of Standards and Technology (MNIST) dataset. These results imply that the device's performance has promising implications for facilitating efficient neuromorphic architectures in the future.https://doi.org/10.1002/advs.202408133hybrid nanocomposite (HN)neuromorphic computing (NC)non‐volatile memory (NVM)transition‐metal dichalcogenides (TMDCs) |
| spellingShingle | Faisal Ghafoor Honggyun Kim Bilal Ghafoor Zaheer Ahmed Muhammad Farooq Khan Muhammad Rabeel Muhammad Faheem Maqsood Sobia Nasir Wajid Zulfiqar Ghulam Dastageer Myoung‐Jae Lee Deok‐kee Kim Novel Solution‐Processed Fe2O3/WS2 Hybrid Nanocomposite Dynamic Memristor for Advanced Power Efficiency in Neuromorphic Computing Advanced Science hybrid nanocomposite (HN) neuromorphic computing (NC) non‐volatile memory (NVM) transition‐metal dichalcogenides (TMDCs) |
| title | Novel Solution‐Processed Fe2O3/WS2 Hybrid Nanocomposite Dynamic Memristor for Advanced Power Efficiency in Neuromorphic Computing |
| title_full | Novel Solution‐Processed Fe2O3/WS2 Hybrid Nanocomposite Dynamic Memristor for Advanced Power Efficiency in Neuromorphic Computing |
| title_fullStr | Novel Solution‐Processed Fe2O3/WS2 Hybrid Nanocomposite Dynamic Memristor for Advanced Power Efficiency in Neuromorphic Computing |
| title_full_unstemmed | Novel Solution‐Processed Fe2O3/WS2 Hybrid Nanocomposite Dynamic Memristor for Advanced Power Efficiency in Neuromorphic Computing |
| title_short | Novel Solution‐Processed Fe2O3/WS2 Hybrid Nanocomposite Dynamic Memristor for Advanced Power Efficiency in Neuromorphic Computing |
| title_sort | novel solution processed fe2o3 ws2 hybrid nanocomposite dynamic memristor for advanced power efficiency in neuromorphic computing |
| topic | hybrid nanocomposite (HN) neuromorphic computing (NC) non‐volatile memory (NVM) transition‐metal dichalcogenides (TMDCs) |
| url | https://doi.org/10.1002/advs.202408133 |
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