Gate-controlled neuromorphic functional transition in an electrochemical graphene transistor
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2023
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866910653276487680 |
|---|---|
| author | Yu, Chenglin Li, Shaorui Pan, Zhoujie Liu, Yanming Wang, Yongchao Zhou, Siyi Gao, Zhiting Tian, He Jiang, Kaili Wang, Yayu Zhang, Jinsong |
| author_facet | Yu, Chenglin Li, Shaorui Pan, Zhoujie Liu, Yanming Wang, Yongchao Zhou, Siyi Gao, Zhiting Tian, He Jiang, Kaili Wang, Yayu Zhang, Jinsong |
| contents | Neuromorphic devices have gained significant attention as potential building blocks for the next generation of computing technologies owing to their ability to emulate the functionalities of biological nervous systems. The essential components in artificial neural network such as synapses and neurons are predominantly implemented by dedicated devices with specific functionalities. In this work, we present a gate-controlled transition of neuromorphic functions between artificial neurons and synapses in monolayer graphene transistors that can be employed as memtransistors or synaptic transistors as required. By harnessing the reliability of reversible electrochemical reactions between C atoms and hydrogen ions, the electric conductivity of graphene transistors can be effectively manipulated, resulting in high on/off resistance ratio, well-defined set/reset voltage, and prolonged retention time. Overall, the on-demand switching of neuromorphic functions in a single graphene transistor provides a promising opportunity to develop adaptive neural networks for the upcoming era of artificial intelligence and machine learning. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2312_04934 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Gate-controlled neuromorphic functional transition in an electrochemical graphene transistor Yu, Chenglin Li, Shaorui Pan, Zhoujie Liu, Yanming Wang, Yongchao Zhou, Siyi Gao, Zhiting Tian, He Jiang, Kaili Wang, Yayu Zhang, Jinsong Applied Physics Materials Science Neuromorphic devices have gained significant attention as potential building blocks for the next generation of computing technologies owing to their ability to emulate the functionalities of biological nervous systems. The essential components in artificial neural network such as synapses and neurons are predominantly implemented by dedicated devices with specific functionalities. In this work, we present a gate-controlled transition of neuromorphic functions between artificial neurons and synapses in monolayer graphene transistors that can be employed as memtransistors or synaptic transistors as required. By harnessing the reliability of reversible electrochemical reactions between C atoms and hydrogen ions, the electric conductivity of graphene transistors can be effectively manipulated, resulting in high on/off resistance ratio, well-defined set/reset voltage, and prolonged retention time. Overall, the on-demand switching of neuromorphic functions in a single graphene transistor provides a promising opportunity to develop adaptive neural networks for the upcoming era of artificial intelligence and machine learning. |
| title | Gate-controlled neuromorphic functional transition in an electrochemical graphene transistor |
| topic | Applied Physics Materials Science |
| url | https://arxiv.org/abs/2312.04934 |