Ion-gating synaptic transistors with long-term synaptic weight modulation
| Autor: | Youngjun Park, Min-Kyu Kim, Jang-Sik Lee |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
Materials science
Quantitative Biology::Neurons and Cognition Artificial neural network Transistor Hardware_PERFORMANCEANDRELIABILITY 02 engineering and technology General Chemistry Gating 010402 general chemistry 021001 nanoscience & nanotechnology 01 natural sciences 0104 chemical sciences law.invention Synapse Synaptic weight Computer Science::Emerging Technologies Neuromorphic engineering law Modulation Materials Chemistry Electronic engineering 0210 nano-technology Communication channel |
| Zdroj: | Journal of Materials Chemistry C. 9:5396-5402 |
| ISSN: | 2050-7534 2050-7526 |
| DOI: | 10.1039/d1tc00048a |
| Popis: | Neuromorphic devices that emulate the human brain are required for efficient computing systems. To develop efficient neuromorphic devices, artificial synapses that are capable of linear and symmetric synaptic weight updates are necessary. Artificial synapses that exploit ion dynamics are suitable for achieving these properties, but stable and long-term synaptic weight modulation is difficult to be achieved because ions can be easily dissipated at the interfaces or in electrolytes. To prevent spontaneous ion dissipation, we design synaptic transistors that operate by ion injection into the channel layer; this process allows long-term synaptic weight updates. We also use a threshold switch as an access device for synaptic transistors. The threshold switch shows low resistance during weight updates of a synapse, and high resistance otherwise to prevent self-discharge of injected ions into the channel layer, which can improve the data retention of synaptic transistors. Linear and symmetric synaptic weight updates are achieved with a large dynamic range (>20), which enables high recognition accuracy (91.4%) of handwritten digits by artificial neural networks. These results provide insights into applications of synaptic transistors for future neuromorphic systems. |
| Databáze: | OpenAIRE |
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