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...
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-10-01
|
| Series: | Materials |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1996-1944/16/20/6698 |
| _version_ | 1797573191920517120 |
|---|---|
| author | Juyeong Pyo Junwon Jang Dongyeol Ju Subaek Lee Wonbo Shim Sungjun Kim |
| author_facet | Juyeong Pyo Junwon Jang Dongyeol Ju Subaek Lee Wonbo Shim Sungjun Kim |
| author_sort | Juyeong Pyo |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-03-10T21:06:24Z |
| format | Article |
| id | doaj.art-e854ca03871b41998c139efcf04780d7 |
| institution | Directory Open Access Journal |
| issn | 1996-1944 |
| language | English |
| last_indexed | 2024-03-10T21:06:24Z |
| publishDate | 2023-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Materials |
| spelling | doaj.art-e854ca03871b41998c139efcf04780d72023-11-19T17:11:02ZengMDPI AGMaterials1996-19442023-10-011620669810.3390/ma16206698Amorphous BN-Based Synaptic Device with High Performance in Neuromorphic ComputingJuyeong Pyo0Junwon Jang1Dongyeol Ju2Subaek Lee3Wonbo Shim4Sungjun Kim5Division of Electronics and Electrical Engineering, Dongguk University, Seoul 04620, Republic of KoreaDivision of Electronics and Electrical Engineering, Dongguk University, Seoul 04620, Republic of KoreaDivision of Electronics and Electrical Engineering, Dongguk University, Seoul 04620, Republic of KoreaDivision of Electronics and Electrical Engineering, Dongguk University, Seoul 04620, Republic of KoreaDepartment of Electrical and Information Engineering, Seoul National University of Science and Technology, Seoul 01811, Republic of KoreaDivision of Electronics and Electrical Engineering, Dongguk University, Seoul 04620, Republic of KoreaThe 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.https://www.mdpi.com/1996-1944/16/20/6698neuromorphic systemmemristorsynaptic deviceresistive switchingamorphous boron nitride |
| spellingShingle | Juyeong Pyo Junwon Jang Dongyeol Ju Subaek Lee Wonbo Shim Sungjun Kim Amorphous BN-Based Synaptic Device with High Performance in Neuromorphic Computing Materials neuromorphic system memristor synaptic device resistive switching amorphous boron nitride |
| title | Amorphous BN-Based Synaptic Device with High Performance in Neuromorphic Computing |
| title_full | Amorphous BN-Based Synaptic Device with High Performance in Neuromorphic Computing |
| title_fullStr | Amorphous BN-Based Synaptic Device with High Performance in Neuromorphic Computing |
| title_full_unstemmed | Amorphous BN-Based Synaptic Device with High Performance in Neuromorphic Computing |
| title_short | Amorphous BN-Based Synaptic Device with High Performance in Neuromorphic Computing |
| title_sort | amorphous bn based synaptic device with high performance in neuromorphic computing |
| topic | neuromorphic system memristor synaptic device resistive switching amorphous boron nitride |
| url | https://www.mdpi.com/1996-1944/16/20/6698 |
| work_keys_str_mv | AT juyeongpyo amorphousbnbasedsynapticdevicewithhighperformanceinneuromorphiccomputing AT junwonjang amorphousbnbasedsynapticdevicewithhighperformanceinneuromorphiccomputing AT dongyeolju amorphousbnbasedsynapticdevicewithhighperformanceinneuromorphiccomputing AT subaeklee amorphousbnbasedsynapticdevicewithhighperformanceinneuromorphiccomputing AT wonboshim amorphousbnbasedsynapticdevicewithhighperformanceinneuromorphiccomputing AT sungjunkim amorphousbnbasedsynapticdevicewithhighperformanceinneuromorphiccomputing |