Artificial synapse based on low-voltage Ni-doped CuI thin-film transistors for neuromorphic application
Inspired by the human brain's capacity as a powerful biological computer capable of simultaneously processing a vast array of cognitive tasks, many emerging artificial synapse devices have been developed in recent years. Electric-double-layer (EDL) transistors based on interfacial ion-modulation have attracted widespread attention for simulating synaptic plasticity and neural functions. Here, low-voltage EDL p-type thin-film transistors (TFTs) are fabricated on glass substrates, with Ni-doped cuprous iodide (Ni0.06Cu0.94I) as the channel and chitosan as the dielectric. The electrical performance of the Ni0.06Cu0.94I TFTs is investigated: current on/off ratio of 6.4 × 104, subthreshold swing of 33 mV/dec, threshold voltage of 1.38 V, operating voltage of 2 V, and saturation field-effect mobility of 15.75 cm2 V−1 s−1. A dual in-plane gate OR logic operation is demonstrated. Importantly, by applying single voltage pulses, dual voltage pulses, and multiple voltage pulses to the gate, the Ni0.06Cu0.94I transistors exhibited typical synaptic characteristics, including short-term potentiation, short-term depression, long-term potentiation, long-term depression, paired-pulse facilitation, and spiking-rate-dependent plasticity. Furthermore, the synaptic transistor can also simulate the learning–forgetting–relearning process of the human brain. These remarkable behaviors of voltage-stimulated synaptic transistors have potential for neuromorphic applications in future artificial systems.
Authors
- Pengfei Chen (ORCID: https://orcid.org/0009-0008-4487-4021)
- Yuehua Peng (ORCID: https://orcid.org/0000-0002-5484-9830)
- Dongsheng Tang (ORCID: https://orcid.org/0000-0003-0804-983X)
- N. Zhang
- Wei Dou (ORCID: https://orcid.org/0000-0003-4936-9151)
- G. C. Jiang (ORCID: https://orcid.org/0009-0007-5002-320X)
- Yanling Yin (ORCID: https://orcid.org/0000-0002-8095-6541)
- Yuling Peng
- XU Xiao-dong (ORCID: https://orcid.org/0009-0001-9473-4683)
- Pufan Deng
Institutions
- Hunan Normal University (CN)
Publication Details
- Journal
- Applied Physics Letters
- Published
- 2024-08-26
- DOI
- https://doi.org/10.1063/5.0219857
- Citations
- 5
- Primary Topic
- Advanced Memory and Neural Computing
- Type
- article
- Field-Weighted Citation Impact
- 1.04
Funders
- National Natural Science Foundation of China