Temperature-dependent behavior of VO2-based artificial neurons
Temperature serves as a pivotal factor influencing information transmission and computational capacity in neurons, significantly affecting the function and efficiency of neural networks. However, the temperature dependence of VO2-based artificial neuron, which is one of the highly promising artificial neurons, has been hardly reported to date. Here, high-performance VO2 devices with NDR features are prepared by rapid annealing and electroforming processes. We constructed VO2-based artificial neurons with output properties similar to those of biological neurons on the basis of the Pearson–Anson oscillation circuit. The temperature-dependent behavior of VO2 neurons was fully investigated. Increasing temperature leads to a decrease in the peak-to-peak value of the output spikes of VO2 neurons. The spike period of VO2 neurons remains relatively stable near room temperature, but it decreases as the temperature reaches above 26 °C. These temperature-dependent features of VO2 neurons are similar to the ones of biological neurons, suggesting a natural advantage of VO2-based artificial neurons in mimicking biological neural activity. These findings contribute toward comprehending and regulating the temperature-dependent behavior of artificial neurons based on Mott memristor.
Authors
- Deen Gu (ORCID: https://orcid.org/0000-0003-4034-5620)
- Xiaoyi Bao (ORCID: https://orcid.org/0000-0002-5525-0694)
- Yuan Xu (ORCID: https://orcid.org/0000-0001-9589-6046)
- Jia‐Cheng Liu (ORCID: https://orcid.org/0000-0002-7779-3016)
- Yuan Liu (ORCID: https://orcid.org/0000-0003-4887-907X)
- Tiancheng Zhao (ORCID: https://orcid.org/0009-0009-5034-4163)
Institutions
- University of Electronic Science and Technology of China (CN)
- National Engineering Research Center of Electromagnetic Radiation Control Materials (CN)
Publication Details
- Journal
- Applied Physics Letters
- Published
- 2024-11-18
- DOI
- https://doi.org/10.1063/5.0231840
- Citations
- 5
- Primary Topic
- Advanced Memory and Neural Computing
- Type
- article
- Field-Weighted Citation Impact
- 1.04
Funders
- National Natural Science Foundation of China