| Autor: |
Han Xu, Renrui Fang, Shuyu Wu, Junjie An, Woyu Zhang, Chao Li, Jikai Lu, Yue Li, Xiaoxin Xu, Yan Wang, Qi Liu, Dashan Shang |
| Jazyk: |
angličtina |
| Rok vydání: |
2023 |
| Předmět: |
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| Zdroj: |
Advanced Electronic Materials, Vol 9, Iss 2, Pp n/a-n/a (2023) |
| Druh dokumentu: |
article |
| ISSN: |
2199-160X |
| DOI: |
10.1002/aelm.202200915 |
| Popis: |
Abstract The hardware implementation of artificial neural networks requires synaptic devices with linear and high‐speed weight modulation. Memristors as a candidate suffer from excessive write variation and asymmetric resistance modulation that inherently rooted in their stochastic mechanisms. Thanks to a controllable ion intercalation/deintercalation mechanism, electrolyte‐gated transistors (EGTs) hold prominent switching linearity and low write variation, and thus have been the promising alternative for synaptic devices. However, the operation frequency of EGTs is seriously limited by the time that is required for the state stabilization, that is, the write‐read delay after each set/reset operation. Here, a Li‐ion‐based EGT (Li‐EGT) with write‐read delay of 3 ms along with multistates, low energy consumption, and quasi‐linear weight update is introduced. The origin of the short write‐read delay of the device is attributed to the permeable interface between electrolyte and gate electrode. Leveraging the Li‐EGT characteristic, near‐ideal accuracy (≈94%) on handwritten digital image data set has been achieved by the corresponding neural network simulation. These results provide an insight into the development of Li‐EGTs for high‐speed neuromorphic computing. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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