Amorphous BN-Based Synaptic Device with High Performance in Neuromorphic Computing
The von Neumann architecture has faced challenges requiring high-fulfillment levels due to the performance gap between its processor and memory. Among the numerous resistive-switching random-access memories, the properties of hexagonal boron nitride (BN) have been extensively reported, but those of...
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| Veröffentlicht in: | Materials 2023-10, Vol.16 (20), p.6698 |
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| creator | Pyo, Juyeong Jang, Junwon Ju, Dongyeol Lee, Subaek Shim, Wonbo Kim, Sungjun |
| description | The von Neumann architecture has faced challenges requiring high-fulfillment levels due to the performance gap between its processor and memory. Among the numerous resistive-switching random-access memories, the properties of hexagonal boron nitride (BN) have been extensively reported, but those of amorphous BN have been insufficiently explored for memory applications. Herein, we fabricated a Pt/BN/TiN device utilizing the resistive switching mechanism to achieve synaptic characteristics in a neuromorphic system. The switching mechanism is investigated based on the I–V curves. Utilizing these characteristics, we optimize the potentiation and depression to mimic the biological synapse. In artificial neural networks, high-recognition rates are achieved using linear conductance updates in a memristor device. The short-term memory characteristics are investigated in depression by controlling the conductance level and time interval. |
| doi_str_mv | 10.3390/ma16206698 |
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Among the numerous resistive-switching random-access memories, the properties of hexagonal boron nitride (BN) have been extensively reported, but those of amorphous BN have been insufficiently explored for memory applications. Herein, we fabricated a Pt/BN/TiN device utilizing the resistive switching mechanism to achieve synaptic characteristics in a neuromorphic system. The switching mechanism is investigated based on the I–V curves. Utilizing these characteristics, we optimize the potentiation and depression to mimic the biological synapse. In artificial neural networks, high-recognition rates are achieved using linear conductance updates in a memristor device. The short-term memory characteristics are investigated in depression by controlling the conductance level and time interval.</description><identifier>ISSN: 1996-1944</identifier><identifier>EISSN: 1996-1944</identifier><identifier>DOI: 10.3390/ma16206698</identifier><identifier>PMID: 37895680</identifier><language>eng</language><publisher>Basel: MDPI AG</publisher><subject>Artificial neural networks ; Boron nitride ; Compliance ; Electrodes ; Internet of Things ; Microprocessors ; Neural networks ; Neuromorphic computing ; Nitrogen ; Random access memory ; Spectrum analysis ; Switching ; Technology application</subject><ispartof>Materials, 2023-10, Vol.16 (20), p.6698</ispartof><rights>COPYRIGHT 2023 MDPI AG</rights><rights>2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). 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| subjects | Artificial neural networks Boron nitride Compliance Electrodes Internet of Things Microprocessors Neural networks Neuromorphic computing Nitrogen Random access memory Spectrum analysis Switching Technology application |
| title | Amorphous BN-Based Synaptic Device with High Performance in Neuromorphic Computing |
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